TY - JOUR AU - Zhou, Xu-Hua AU - Chen, Hui AU - Yang, Weiwei AU - Wang, Li AU - Chen, Lin AU - Zhu, Ying AU - Zhang, Yingjun AU - Shi, Mei AU - Zhang, Qin PY - 2025/3/26 TI - Efficacy of eHealth Interventions for Hemodialysis Patients: Systematic Review and Meta-Analysis JO - J Med Internet Res SP - e67246 VL - 27 KW - hemodialysis KW - eHealth interventions KW - quality of life KW - treatment adherence KW - anxiety KW - depression KW - meta-analysis KW - kidney KW - systematic review KW - kidney diseases KW - kidney function KW - chronic diseases N2 - Background: Within hemodialysis patient populations, eHealth interventions have been considered as an alternative and complementary option to routine care services. However, the efficacy of eHealth interventions for hemodialysis patients remains poorly understood owing to a lack of rigorous quantitative evidence synthesis. Objective: This meta-analysis aimed to evaluate the efficacy of eHealth interventions in improving quality of life, treatment adherence, and psychological outcomes (anxiety and depression) among hemodialysis patients. In addition, the study sought to identify specific intervention components and methodological quality associated with enhanced quality of life and health outcomes in this population. Methods: A comprehensive search was performed across PubMed, Web of Science, Embase, CINAHL, Cochrane Library, PsycINFO, China National Knowledge Infrastructure, WanFang, China Science and Technology Journal Database, and China BioMedical Literature Database databases from their inception to September 7, 2024. Randomized controlled trials on eHealth interventions for hemodialysis patients published in English or Chinese were included. Critical appraisal was carried out independently by 2 reviewers to assess the bias risk of the studies included. Quantitative synthesis of the outcomes of interest was conducted using a random-effects model. The quality of evidence for the outcomes was evaluated following the Grading of Recommendations, Assessment, Development, and Evaluation approach. Results: A total of 17 randomized controlled trials involving 1728 participants were included in this meta-analysis out of 5741 articles identified in the initial database search and additional search references. In the 17 studies, 8 kinds of eHealth intervention delivery formats were used, including text messages, telephone sessions, video, network platforms, social media, computers, websites, and mobile apps. The majority of research studies used a single form of eHealth intervention, and 7 studies adopted a combined approach of 2 or more eHealth technologies. The duration of eHealth interventions demonstrated substantial variability across studies, spanning from 4 weeks to 12 months, of which 3 months was the most common. A total of 14 (82%) studies were considered to have ?some concern? about selection bias. In addition, 15 (88%) trials were classified as having a ?high risk? of performance and detection bias, and all trials were judged to be at ?low risk? of attrition and reporting bias. The pooled results revealed a significant difference between the eHealth interventions and control groups on quality of life (standardized mean difference [SMD]=0.87, 95?% CI 0.38 to 1.37, low certainty evidence), treatment adherence (SMD=1.11, 95?% CI 0.30 to 1.91, moderate certainty evidence), anxiety (SMD=?2.11, 95?% CI ?3.25 to ?0.97, moderate certainty evidence), and depression (SMD=?2.46, 95?% CI ?3.68 to ?1.25, moderate certainty evidence). Conclusions: eHealth interventions could be a beneficial approach for improving quality of life and treatment adherence and reducing anxiety and depression among hemodialysis patients. However, future high-quality randomized controlled trials are essential to draw more reliable conclusions. Trial Registration: PROSPERO CRD42024589799; https://www.crd.york.ac.uk/PROSPERO/view/CRD42024589799 UR - https://www.jmir.org/2025/1/e67246 UR - http://dx.doi.org/10.2196/67246 UR - http://www.ncbi.nlm.nih.gov/pubmed/ ID - info:doi/10.2196/67246 ER - TY - JOUR AU - Cho, Nam-Jun AU - Jeong, Inyong AU - Ahn, Se-Jin AU - Gil, Hyo-Wook AU - Kim, Yeongmin AU - Park, Jin-Hyun AU - Kang, Sanghee AU - Lee, Hwamin PY - 2025/3/18 TI - Machine Learning to Assist in Managing Acute Kidney Injury in General Wards: Multicenter Retrospective Study JO - J Med Internet Res SP - e66568 VL - 27 KW - acute kidney injury KW - machine learning KW - recovery of function KW - creatinine KW - kidney KW - patient rooms N2 - Background: Most artificial intelligence?based research on acute kidney injury (AKI) prediction has focused on intensive care unit settings, limiting their generalizability to general wards. The lack of standardized AKI definitions and reliance on intensive care units further hinder the clinical applicability of these models. Objective: This study aims to develop and validate a machine learning?based framework to assist in managing AKI and acute kidney disease (AKD) in general ward patients, using a refined operational definition of AKI to improve predictive performance and clinical relevance. Methods: This retrospective multicenter cohort study analyzed electronic health record data from 3 hospitals in South Korea. AKI and AKD were defined using a refined version of the Kidney Disease: Improving Global Outcomes criteria, which included adjustments to baseline serum creatinine estimation and a stricter minimum increase threshold to reduce misclassification due to transient fluctuations. The primary outcome was the development of machine learning models for early prediction of AKI (within 3 days before onset) and AKD (nonrecovery within 7 days after AKI). Results: The final analysis included 135,068 patients. A total of 7658 (8%) patients in the internal cohort and 2898 (7.3%) patients in the external cohort developed AKI. Among the 5429 patients in the internal cohort and 1998 patients in the external cohort for whom AKD progression could be assessed, 896 (16.5%) patients and 287 (14.4%) patients, respectively, progressed to AKD. Using the refined criteria, 2898 cases of AKI were identified, whereas applying the standard Kidney Disease: Improving Global Outcomes criteria resulted in the identification of 5407 cases. Among the 2509 patients who were not classified as having AKI under the refined criteria, 2242 had a baseline serum creatinine level below 0.6 mg/dL, while the remaining 267 experienced a decrease in serum creatinine before the onset of AKI. The final selected early prediction model for AKI achieved an area under the receiver operating characteristic curve of 0.9053 in the internal cohort and 0.8860 in the external cohort. The early prediction model for AKD achieved an area under the receiver operating characteristic curve of 0.8202 in the internal cohort and 0.7833 in the external cohort. Conclusions: The proposed machine learning framework successfully predicted AKI and AKD in general ward patients with high accuracy. The refined AKI definition significantly reduced the classification of patients with transient serum creatinine fluctuations as AKI cases compared to the previous criteria. These findings suggest that integrating this machine learning framework into hospital workflows could enable earlier interventions, optimize resource allocation, and improve patient outcomes. UR - https://www.jmir.org/2025/1/e66568 UR - http://dx.doi.org/10.2196/66568 UR - http://www.ncbi.nlm.nih.gov/pubmed/ ID - info:doi/10.2196/66568 ER - TY - JOUR AU - Valkonen, Paula AU - Hölsä, Sini AU - Viitanen, Johanna AU - Leinonen, Sini AU - Karisalmi, Nina AU - Rauta, Virpi PY - 2025/3/18 TI - Illustrating User Needs for eHealth With Experience Map: Interview Study With Chronic Kidney Disease Patients JO - JMIR Hum Factors SP - e48221 VL - 12 KW - user need KW - chronic illness KW - kidney disease KW - older adult KW - eHealth KW - experience map KW - human-centered design KW - home dialysis N2 - Background: Chronic kidney disease (CKD) is a common condition worldwide and home dialysis (HD) provides economic, quality of life, and clinical advantages compared to other dialysis modalities. Human-centered design aims to support the development of eHealth solutions with high usability and user experience. However, research on the eHealth needs of patients using HD is scarce. Objective: This study aimed to support the design of eHealth for patients with CKD, particularly for patients using HD, by developing a kidney disease experience map that illustrates user needs, concerns, and barriers. The research questions were (1) what experiences do patients, particularly older adults, have in their everyday lives with CKD? (2) what user needs do patients with CKD have for HD eHealth? (3) how can these needs be illustrated using the experience map technique? The study focused on patients aged >60 years, as they are at a higher risk of chronic conditions. The study was conducted as part of the eHealth in HD project, coordinated by Hospital District of Helsinki and Uusimaa, Finland. Methods: In total, 18 patients in different care modalities participated in retrospective interviews conducted between October 2020 and April 2021. The interviews included a preliminary task with patient journey illustrations and questions about their experiences and everyday lives with CKD. The data analysis was conducted using a thematic analysis approach and the process included several phases. Results: On the basis of the thematic analysis, 5 categories were identified: healthy habits, concerns about and barriers to eHealth use, digital communication, patients? emotions, and everyday life with CKD. These were illustrated in the first version of the kidney disease experience map. The patients had different healthy habits regarding social life, sports, and other activities. They had challenges with poorly functioning eHealth software and experienced other factors, such as a lack of interest and lack of skills for eHealth use. Technical devices do not always meet the emotional or physical needs of their users. This caused feelings of frustration, worry, and fear in patients, yet also fostered situational awareness and hope. Conclusions: The experience map is a promising method for illustrating user needs and communicating the patient?s voice for eHealth development. eHealth offers possibilities to support patient?s everyday life with chronic disease. The patient?s situation and capacity to use eHealth solutions vary with their everyday challenges, opportunities, and their current stage of treatment. The kidney disease experience map will be used and further developed in the ongoing research project ?Better Health at Home?Optimized Human-Centered Care of Predialysis and Home Dialysis Patients? (2022 to 2026). UR - https://humanfactors.jmir.org/2025/1/e48221 UR - http://dx.doi.org/10.2196/48221 UR - http://www.ncbi.nlm.nih.gov/pubmed/ ID - info:doi/10.2196/48221 ER - TY - JOUR AU - Wu, Jia AU - Zeng, Youjia AU - Yang, Jun AU - Yao, Yutong AU - Xu, Xiuling AU - Song, Gaofeng AU - Yi, Wuyong AU - Wang, Taifen AU - Zheng, Yihou AU - Jia, Zhongwei AU - Yan, Xiangyu PY - 2025/3/3 TI - Daily Treatment Monitoring for Patients Receiving Home-Based Peritoneal Dialysis and Prediction of Heart Failure Risk: mHealth Tool Development and Modeling Study JO - JMIR Form Res SP - e56254 VL - 9 KW - peritoneal dialysis KW - mHealth KW - patient management KW - heart failure KW - prediction model N2 - Background: Peritoneal dialysis is one of the major renal replacement modalities for patients with end-stage renal disease. Heart failure is a common adverse event among patients who undergo peritoneal dialysis treatment, especially for those who undergo continuous ambulatory peritoneal dialysis at home, because of the lack of professional input-output volume monitoring and management during treatment. Objective: This study aims to develop novel mobile health (mHealth) tools to improve the quality of home-based continuous ambulatory peritoneal dialysis treatment and to build a prediction model of heart failure based on the system?s daily treatment monitoring data. Methods: The mHealth tools with a 4-layer system were designed and developed using Spring Boot, MyBatis Plus, MySQL, and Redis as backend technology stack, and Vue, Element User Interface, and WeChat Mini Program as front-end technology stack. Patients were recruited to use the tool during daily peritoneal dialysis treatment from January 1, 2017, to April 20, 2023. Logistic regression models based on real-time treatment monitoring data were used for heart failure prediction. The sensitivity, specificity, accuracy, and Youden index were calculated to evaluate the performance of the prediction model. In the sensitivity analysis, the ratio of patients with and without heart failure was set to 1:4 and 1:10, respectively, to better evaluate the stability of the prediction model. Results: A WeChat Mini Program named Futou Bao for patients and a patient data management platform for doctors was developed. Futou Bao included an intelligent data upload function module and an auxiliary function module. The doctor?s data management platform consisted of 4 function modules, that is, patient management, data visualization and marking, data statistics, and system management. During the study period, the records of 6635 patients who received peritoneal dialysis treatment were uploaded in Futou Bao, with 0.71% (47/6635) of them experiencing heart failure. The prediction model that included sex, age, and diastolic blood pressure was considered as the optimal model, wherein the sensitivity, specificity, accuracy, and Youden index were 0.75, 0.91, 0.89, and 0.66, respectively, with an area under the curve value of 0.879 (95% CI 0.772-0.986) using the validation dataset. The sensitivity analysis showed stable results. Conclusions: This study provides a new home-based peritoneal dialysis management paradigm that enables the daily monitoring and early warning of heart failure risk. This novel paradigm is of great value for improving the efficiency, security, and personalization of peritoneal dialysis. UR - https://formative.jmir.org/2025/1/e56254 UR - http://dx.doi.org/10.2196/56254 UR - http://www.ncbi.nlm.nih.gov/pubmed/40053710 ID - info:doi/10.2196/56254 ER - TY - JOUR AU - Bhak, Youngmin AU - Lee, Ho Yu AU - Kim, Joonhyung AU - Lee, Kiwon AU - Lee, Daehwan AU - Jang, Chan Eun AU - Jang, Eunjeong AU - Lee, Seungkyu Christopher AU - Kang, Seok Eun AU - Park, Sehee AU - Han, Wook Hyun AU - Nam, Min Sang PY - 2025/2/7 TI - Diagnosis of Chronic Kidney Disease Using Retinal Imaging and Urine Dipstick Data: Multimodal Deep Learning Approach JO - JMIR Med Inform SP - e55825 VL - 13 KW - multimodal deep learning KW - chronic kidney disease KW - fundus image KW - saliency map KW - urine dipstick N2 - Background: Chronic kidney disease (CKD) is a prevalent condition with significant global health implications. Early detection and management are critical to prevent disease progression and complications. Deep learning (DL) models using retinal images have emerged as potential noninvasive screening tools for CKD, though their performance may be limited, especially in identifying individuals with proteinuria and in specific subgroups. Objective: We aim to evaluate the efficacy of integrating retinal images and urine dipstick data into DL models for enhanced CKD diagnosis. Methods: The 3 models were developed and validated: eGFR-RIDL (estimated glomerular filtration rate?retinal image deep learning), eGFR-UDLR (logistic regression using urine dipstick data), and eGFR-MMDL (multimodal deep learning combining retinal images and urine dipstick data). All models were trained to predict an eGFR<60 mL/min/1.73 m², a key indicator of CKD, calculated using the 2009 CKD-EPI (Chronic Kidney Disease Epidemiology Collaboration) equation. This study used a multicenter dataset of participants aged 20?79 years, including a development set (65,082 people) and an external validation set (58,284 people). Wide Residual Networks were used for DL, and saliency maps were used to visualize model attention. Sensitivity analyses assessed the impact of numerical variables. Results: eGFR-MMDL outperformed eGFR-RIDL in both the test and external validation sets, with area under the curves of 0.94 versus 0.90 and 0.88 versus 0.77 (P<.001 for both, DeLong test). eGFR-UDLR outperformed eGFR-RIDL and was comparable to eGFR-MMDL, particularly in the external validation. However, in the subgroup analysis, eGFR-MMDL showed improvement across all subgroups, while eGFR-UDLR demonstrated no such gains. This suggested that the enhanced performance of eGFR-MMDL was not due to urine data alone, but rather from the synergistic integration of both retinal images and urine data. The eGFR-MMDL model demonstrated the best performance in individuals younger than 65 years or those with proteinuria. Age and proteinuria were identified as critical factors influencing model performance. Saliency maps indicated that urine data and retinal images provide complementary information, with urine offering insights into retinal abnormalities and retinal images, particularly the arcade vessels, being key for predicting kidney function. Conclusions: The MMDL model integrating retinal images and urine dipstick data show significant promise for noninvasive CKD screening, outperforming the retinal image?only model. However, routine blood tests are still recommended for individuals aged 65 years and older due to the model?s limited performance in this age group. UR - https://medinform.jmir.org/2025/1/e55825 UR - http://dx.doi.org/10.2196/55825 ID - info:doi/10.2196/55825 ER - TY - JOUR AU - Delvallée, Marion AU - Guerraoui, Abdallah AU - Tchetgnia, Lucas AU - Grangier, Jean-Pierre AU - Amamra, Nassira AU - Camarroque, Anne-Laure AU - Haesebaert, Julie AU - Caillette-Beaudoin, Agnès PY - 2025/1/22 TI - Barriers and Facilitators in Implementing a Telemonitoring Application for Patients With Chronic Kidney Disease and Health Professionals: Ancillary Implementation Study of the NeLLY (New Health e-Link in the Lyon Region) Stepped-Wedge Randomized Controlled Trial JO - JMIR Mhealth Uhealth SP - e50014 VL - 13 KW - telehealth KW - telemonitoring KW - chronic kidney disease KW - implementation KW - Consolidated Framework for Implementation Research KW - Technology Acceptance Model N2 - Background: The use of telemonitoring to manage renal function in patients with chronic kidney disease (CKD) is recommended by health authorities. However, despite these recommendations, the adoption of telemonitoring by both health care professionals and patients faces numerous challenges. Objective: This study aims to identify barriers and facilitators in the implementation of a telemonitoring program for patients with CKD, as perceived by health care professionals and patients, and to explore factors associated with the adoption of the program. This study serves as a process evaluation conducted alongside the cost-effectiveness NeLLY (New Health e-Link in the Lyon Region) trial. Methods: A mixed methods approach combining a quantitative questionnaire and semistructured interviews was conducted among nurses, nephrologists, and patients with stages 3 and 4 CKD across 10 renal care centers in France that have implemented telemonitoring. The Technology Acceptance Model (TAM) and the Consolidated Framework for Implementation Research (CFIR) were used to design the questionnaires and interview guides. The dimensions investigated included ease of use, perceived usefulness, and intention to use (TAM), as well as characteristics of the intervention, local and general context, individual factors, and processes (CFIR). The adoption of telemonitoring was assessed based on the frequency with which patients connected to the telemonitoring device. Determinants of telemonitoring use were analyzed using nonparametric tests, specifically the Wilcoxon-Mann-Whitney and Kruskal-Wallis tests. Thematic analysis was conducted on the transcriptions of semistructured interviews. Both quantitative and qualitative results, including data from patients and professionals, were integrated to provide a comprehensive understanding of the factors associated with the use of remote monitoring in CKD. Results: A total of 42 professionals and 128 patients with CKD responded to our questionnaire. Among these, 11 professionals and 13 patients participated in interviews. Nurses, who were responsible for patient follow-up, regularly used telemonitoring (8/13, 62%, at least once a month), while nephrologists, who were responsible for prescribing it, were primarily occasional users (5/8, 63%, using it less than once a month). Among professionals, the main obstacles identified were the heavy workload generated by telemonitoring, lack of training, and insufficient support for nurses. Among the 128 patients, 46 (35.9%) reported using the application at least once a week. The main barriers for patients were issues related to computer use, as well as the lack of feedback and communication with health care professionals. The main facilitators identified by both professionals and patients for using telemonitoring were the empowerment of patients in managing their health and the reduction of the burden associated with CKD. Conclusions: Improving adherence to telemonitoring in the context of CKD requires collaborative efforts from both professionals and patients. Our results provide insights that can inform the design of effective, theory-driven interventions aimed at improving telemonitoring adoption and usage. UR - https://mhealth.jmir.org/2025/1/e50014 UR - http://dx.doi.org/10.2196/50014 UR - http://www.ncbi.nlm.nih.gov/pubmed/ ID - info:doi/10.2196/50014 ER - TY - JOUR AU - Chen, Nai-Jung AU - Chang, Ching-Hao AU - Huang, Chiu-Mieh AU - Lin, Fen-He AU - Lu, Li-Ting AU - Liu, Kuan-Yi AU - Lai, Chih-Lin AU - Lin, Chin-Yao AU - Hou, Yi-Chou AU - Guo, Jong-Long PY - 2025/1/9 TI - Assessing the Effectiveness of Interactive Robot-Assisted Virtual Health Coaching for Health Literacy and Disease Knowledge of Patients with Chronic Kidney Disease: Quasiexperimental Study JO - J Med Internet Res SP - e68072 VL - 27 KW - chronic kidney disease KW - disease knowledge KW - eHealth KW - health coaching KW - health education KW - health literacy KW - interactive robot N2 - Background: Chronic kidney disease (CKD) imposes a significant global health and economic burden, impacting millions globally. Despite its high prevalence, public awareness and understanding of CKD remain limited, leading to delayed diagnosis and suboptimal management. Traditional patient education methods, such as 1-on-1 verbal instruction or printed brochures, are often insufficient, especially considering the shortage of nursing staff. Technology-assisted education presents a promising and standardized solution, emphasizing the need for innovative and scalable approaches to improve CKD-specific knowledge and health literacy. Objective: This study aimed to develop and evaluate the effectiveness of an innovative 12-unit virtual health coaching program delivered through interactive robots that is intended to enhance disease knowledge and health literacy among patients with CKD. Methods: A quasiexperimental design was used, and 60 participants were evenly assigned to experimental and comparison groups. However, due to attrition, 14 participants in the experimental group and 16 participants in the comparison group completed the study. The intervention involved a 12-unit program, with each unit lasting approximately 20 minutes to 30 minutes and delivered across 3 to 4 learning sessions, and participants completed 3 to 4 units per session. The program addressed key aspects of CKD-specific health literacy including functional, communicative, and critical literacy and CKD-specific knowledge including basic knowledge, prevention, lifestyle, dietary intake, and medication. Data were collected through validated pre and postintervention questionnaires. All 30 participants completed the program and subsequent evaluations, with outcome measures assessing changes in CKD-specific knowledge and health literacy. Results: Postintervention analysis using generalized estimating equations, adjusted for age, revealed that the experimental group (n=14) had significantly greater improvements in health literacy (coefficient=2.51, Wald ?²1=5.89; P=.02) and disease knowledge (coefficient=1.66, Wald ?²1=11.75; P=.001) than the comparison group (n=16). Postintervention t tests revealed significant improvements in CKD-specific health literacy and disease knowledge (P<.001) between the experimental and comparison groups. Additional analyses identified significant group × time interactions, indicating improvements in communicative literacy (P=.01) and critical literacy (P=.02), while no significant changes were observed in functional literacy. Regarding disease knowledge, the experimental group demonstrated a significant improvement in medication (P<.001), whereas changes in basic knowledge, prevention, lifestyle, and dietary intake were not significant. Conclusions: This study demonstrated that interactive robot-assisted eHealth coaching effectively enhanced CKD-specific disease knowledge and health literacy. Despite the challenges posed by the COVID-19 pandemic, which constrained sample sizes, the findings indicate that this program is a promising patient education tool in clinical nephrology. Future research should involve larger sample sizes to enhance generalizability and examine additional factors influencing effectiveness. UR - https://www.jmir.org/2025/1/e68072 UR - http://dx.doi.org/10.2196/68072 UR - http://www.ncbi.nlm.nih.gov/pubmed/ ID - info:doi/10.2196/68072 ER - TY - JOUR AU - Luo, Xiao-Qin AU - Zhang, Ning-Ya AU - Deng, Ying-Hao AU - Wang, Hong-Shen AU - Kang, Yi-Xin AU - Duan, Shao-Bin PY - 2025/1/3 TI - Major Adverse Kidney Events in Hospitalized Older Patients With Acute Kidney Injury: Machine Learning?Based Model Development and Validation Study JO - J Med Internet Res SP - e52786 VL - 27 KW - major adverse kidney events within 30 days KW - older KW - acute kidney injury KW - machine learning KW - prediction model N2 - Background: Acute kidney injury (AKI) is a common complication in hospitalized older patients, associated with increased morbidity, mortality, and health care costs. Major adverse kidney events within 30 days (MAKE30), a composite of death, new renal replacement therapy, or persistent renal dysfunction, has been recommended as a patient-centered endpoint for clinical trials involving AKI. Objective: This study aimed to develop and validate a machine learning?based model to predict MAKE30 in hospitalized older patients with AKI. Methods: A total of 4266 older patients (aged ? 65 years) with AKI admitted to the Second Xiangya Hospital of Central South University from January 1, 2015, to December 31, 2020, were included and randomly divided into a training set and an internal test set in a ratio of 7:3. An additional cohort of 11,864 eligible patients from the Medical Information Mart for Intensive Care ? database served as an external test set. The Boruta algorithm was used to select the most important predictor variables from 53 candidate variables. The eXtreme Gradient Boosting algorithm was applied to establish a prediction model for MAKE30. Model discrimination was evaluated by the area under the receiver operating characteristic curve (AUROC). The SHapley Additive exPlanations method was used to interpret model predictions. Results: The overall incidence of MAKE30 in the 2 study cohorts was 28.3% (95% CI 26.9%-29.7%) and 26.7% (95% CI 25.9%-27.5%), respectively. The prediction model for MAKE30 exhibited adequate predictive performance, with an AUROC of 0.868 (95% CI 0.852-0.881) in the training set and 0.823 (95% CI 0.798-0.846) in the internal test set. Its simplified version achieved an AUROC of 0.744 (95% CI 0.735-0.754) in the external test set. The SHapley Additive exPlanations method showed that the use of vasopressors, mechanical ventilation, blood urea nitrogen level, red blood cell distribution width-coefficient of variation, and serum albumin level were closely associated with MAKE30. Conclusions: An interpretable eXtreme Gradient Boosting model was developed and validated to predict MAKE30, which provides opportunities for risk stratification, clinical decision-making, and the conduct of clinical trials involving AKI. Trial Registration: Chinese Clinical Trial Registry ChiCTR2200061610; https://tinyurl.com/3smf9nuw UR - https://www.jmir.org/2025/1/e52786 UR - http://dx.doi.org/10.2196/52786 UR - http://www.ncbi.nlm.nih.gov/pubmed/ ID - info:doi/10.2196/52786 ER - TY - JOUR AU - Ma, Mengqing AU - Chen, Caimei AU - Chen, Dawei AU - Zhang, Hao AU - Du, Xia AU - Sun, Qing AU - Fan, Li AU - Kong, Huiping AU - Chen, Xueting AU - Cao, Changchun AU - Wan, Xin PY - 2024/12/19 TI - A Machine Learning?Based Prediction Model for Acute Kidney Injury in Patients With Community-Acquired Pneumonia: Multicenter Validation Study JO - J Med Internet Res SP - e51255 VL - 26 KW - acute kidney injury KW - community-acquired KW - pneumonia KW - machine learning KW - prediction model N2 - Background: Acute kidney injury (AKI) is common in patients with community-acquired pneumonia (CAP) and is associated with increased morbidity and mortality. Objective: This study aimed to establish and validate predictive models for AKI in hospitalized patients with CAP based on machine learning algorithms. Methods: We trained and externally validated 5 machine learning algorithms, including logistic regression, support vector machine, random forest, extreme gradient boosting, and deep forest (DF). Feature selection was conducted using the sliding window forward feature selection technique. Shapley additive explanations and local interpretable model-agnostic explanation techniques were applied to the optimal model for visual interpretation. Results: A total of 6371 patients with CAP met the inclusion criteria. The development of CAP-associated AKI (CAP-AKI) was recognized in 1006 (15.8%) patients. The 11 selected indicators were sex, temperature, breathing rate, diastolic blood pressure, C-reactive protein, albumin, white blood cell, hemoglobin, platelet, blood urea nitrogen, and neutrophil count. The DF model achieved the best area under the receiver operating characteristic curve (AUC) and accuracy in the internal (AUC=0.89, accuracy=0.90) and external validation sets (AUC=0.87, accuracy=0.83). Furthermore, the DF model had the best calibration among all models. In addition, a web-based prediction platform was developed to predict CAP-AKI. Conclusions: The model described in this study is the first multicenter-validated AKI prediction model that accurately predicts CAP-AKI during hospitalization. The web-based prediction platform embedded with the DF model serves as a user-friendly tool for early identification of high-risk patients. UR - https://www.jmir.org/2024/1/e51255 UR - http://dx.doi.org/10.2196/51255 UR - http://www.ncbi.nlm.nih.gov/pubmed/39699941 ID - info:doi/10.2196/51255 ER - TY - JOUR AU - Ma, Jianwei AU - Wang, Jiangyuan AU - Ying, Jiapei AU - Xie, Shasha AU - Su, Qin AU - Zhou, Tianmeng AU - Han, Fuman AU - Xu, Jiayan AU - Zhu, Siyi AU - Yuan, Chenyi AU - Huang, Ziyuan AU - Xu, Jingfang AU - Chen, Xuyong AU - Bian, Xueyan PY - 2024/11/25 TI - Long-Term Efficacy of an AI-Based Health Coaching Mobile App in Slowing the Progression of Nondialysis-Dependent Chronic Kidney Disease: Retrospective Cohort Study JO - J Med Internet Res SP - e54206 VL - 26 KW - artificial intelligence KW - chronic kidney disease KW - eHealth care KW - mobile app KW - self-management KW - kidney function KW - telemedicine KW - app KW - health coaching KW - CKD KW - mobile phone N2 - Background: Chronic kidney disease (CKD) is a significant public health concern. Therefore, practical strategies for slowing CKD progression and improving patient outcomes are imperative. There is limited evidence to substantiate the efficacy of mobile app?based nursing systems for decelerating CKD progression. Objective: This study aimed to evaluate the long-term efficacy of the KidneyOnline intelligent care system in slowing the progression of nondialysis-dependent CKD. Methods: In this retrospective study, the KidneyOnline app was used for patients with CKD in China who were registered between January 2017 and April 2023. Patients were divided into 2 groups: an intervention group using the app?s nurse-led, patient-oriented management system and a conventional care group that did not use the app. Patients? uploaded health data were processed via deep learning optical character recognition, and the artificial intelligence (AI) system provided personalized health care plans and interventions. Conversely, the conventional care group received suggestions from nephrologists during regular visits without AI. Monitoring extended for an average duration of 2.1 (SD 1.4) years. The study?s objective is to assess the app?s effectiveness in preserving kidney function. The primary outcome was the estimated glomerular filtration rate slope over the follow-up period, and secondary outcomes included changes in albumin-to-creatinine ratio (ACR) and mean arterial pressure. Results: A total of 12,297 eligible patients were enrolled for the analysis. Among them, 808 patients were successfully matched using 1:1 propensity score matching, resulting in 404 (50%) patients in the KidneyOnline care system group and another 404 (50%) patients in the conventional care group. The estimated glomerular filtration rate slope in the KidneyOnline care group was significantly lower than that in the conventional care group (odds ratio ?1.3, 95% CI ?2.4 to ?0.1 mL/min/1.73 m2 per year vs odds ratio ?2.8, 95% CI ?3.8 to ?1.9 mL/min/1.73 m2 per year; P=.009). Subgroup analysis revealed that the effect of the KidneyOnline care group was more significant in male patients, patients older than 45 years, and patients with worse baseline kidney function, higher blood pressure, and heavier proteinuria. After 3 and 6 months, the mean arterial pressure in the KidneyOnline care group decreased to 85.6 (SD 9.2) and 83.6 (SD 10.5) mm Hg, respectively, compared to 94.9 (SD 10.6) and 95.2 (SD 11.6) mm Hg in the conventional care group (P<.001). The ACR in the KidneyOnline care group showed a more significant reduction after 3 and 6 months (736 vs 980 mg/g and 572 vs 840 mg/g; P=.07 and P=.03); however, there was no significant difference in ACR between the two groups at the end of the follow-up period (618 vs 639 mg/g; P=.90). Conclusions: The utilization of KidneyOnline, an AI-based, nurse-led, patient-centered care system, may be beneficial in slowing the progression of nondialysis-dependent CKD. UR - https://www.jmir.org/2024/1/e54206 UR - http://dx.doi.org/10.2196/54206 UR - http://www.ncbi.nlm.nih.gov/pubmed/39402012 ID - info:doi/10.2196/54206 ER - TY - JOUR AU - Li, Xingyuan AU - Liu, Ke AU - Lang, Yanlin AU - Chai, Zhonglin AU - Liu, Fang PY - 2024/11/15 TI - Exploring the Potential of Claude 3 Opus in Renal Pathological Diagnosis: Performance Evaluation JO - JMIR Med Inform SP - e65033 VL - 12 KW - artificial intelligence KW - Claude 3 Opus KW - renal pathology KW - diagnostic performance KW - large language model KW - LLM KW - performance evaluation KW - medical diagnosis KW - AI language model KW - diagnosis KW - pathology images KW - pathologist KW - clinical relevance KW - accuracy KW - language fluency KW - pathological diagnosis N2 - Background: Artificial intelligence (AI) has shown great promise in assisting medical diagnosis, but its application in renal pathology remains limited. Objective: We evaluated the performance of an advanced AI language model, Claude 3 Opus (Anthropic), in generating diagnostic descriptions for renal pathological images. Methods: We carefully curated a dataset of 100 representative renal pathological images from the Diagnostic Atlas of Renal Pathology (3rd edition). The image selection aimed to cover a wide spectrum of common renal diseases, ensuring a balanced and comprehensive dataset. Claude 3 Opus generated diagnostic descriptions for each image, which were scored by 2 pathologists on clinical relevance, accuracy, fluency, completeness, and overall value. Results: Claude 3 Opus achieved a high mean score in language fluency (3.86) but lower scores in clinical relevance (1.75), accuracy (1.55), completeness (2.01), and overall value (1.75). Performance varied across disease types. Interrater agreement was substantial for relevance (?=0.627) and overall value (?=0.589) and moderate for accuracy (?=0.485) and completeness (?=0.458). Conclusions: Claude 3 Opus shows potential in generating fluent renal pathology descriptions but needs improvement in accuracy and clinical value. The AI?s performance varied across disease types. Addressing the limitations of single-source data and incorporating comparative analyses with other AI approaches are essential steps for future research. Further optimization and validation are needed for clinical applications. UR - https://medinform.jmir.org/2024/1/e65033 UR - http://dx.doi.org/10.2196/65033 UR - http://www.ncbi.nlm.nih.gov/pubmed/ ID - info:doi/10.2196/65033 ER - TY - JOUR AU - McBride, Caroline AU - Hunter, Barbara AU - Lumsden, Natalie AU - Somasundaram, Kaleswari AU - McMorrow, Rita AU - Boyle, Douglas AU - Emery, Jon AU - Nelson, Craig AU - Manski-Nankervis, Jo-Anne PY - 2024/11/13 TI - Clinical Acceptability of a Quality Improvement Program for Reducing Cardiovascular Disease Risk in People With Chronic Kidney Disease in Australian General Practice: Qualitative Study JO - JMIR Hum Factors SP - e55667 VL - 11 KW - clinical decision support KW - general practice KW - GP KW - primary care KW - family medicine KW - general medicine KW - family physician KW - implementation science KW - chronic kidney disease KW - CKD KW - nephrology KW - nephrologist KW - chronic disease KW - cardiovascular risk KW - cardiology KW - quality improvement KW - EHR KW - electronic health record KW - clinical software N2 - Background: Future Health Today (FHT) is a technology program that integrates with general practice clinical software to provide point of care (PoC) clinical decision support and a quality improvement dashboard. This qualitative study looks at the use of FHT in the context of cardiovascular disease risk in chronic kidney disease (CKD). Objective: This study aims to explore factors influencing clinical implementation of the FHT module focusing on cardiovascular risk in CKD, from the perspectives of participating general practitioner staff. Methods: Practices in Victoria were recruited to participate in a pragmatic cluster randomized controlled trial using FHT, of which 19 practices were randomly assigned to use FHT?s cardiovascular risk in CKD program. A total of 13 semistructured interviews were undertaken with a nominated general practitioner (n=7) or practice nurse (n=6) from 10 participating practices. Interview questions focused on the clinical usefulness of the tool and its place in clinical workflows. Qualitative data were coded by 2 researchers and analyzed using framework analysis and Clinical Performance Feedback Intervention Theory. Results: All 13 interviewees had used the FHT PoC tool, and feedback was largely positive. Overall, clinicians described engaging with the tool as a ?prompt? or ?reminder? system. Themes reflected that the tool?s goals and clinical content were aligned with clinician?s existing priorities and knowledge, and the tool?s design facilitated easy integration into existing workflows. The main barrier to implementation identified by 2 clinicians was notification fatigue. A total of 7 interviewees had used the FHT dashboard tool. The main barriers to use were its limited integration into clinical workflows, such that some participants did not know of its existence; clinicians? competing clinical priorities; and limited time to learn and use the tool. Conclusions: This study identified many facilitators for the successful use of the FHT PoC program, in the context of cardiovascular risk in CKD, and barriers to the use of the dashboard program. This work will be used to inform the wider implementation of FHT, as well as the development of future modules of FHT for other risk or disease states. Trial Registration: Australian New Zealand Clinical Trial Registry ACTRN12620000993998; https://www.anzctr.org.au/Trial/Registration/TrialReview.aspx?id=380119&is UR - https://humanfactors.jmir.org/2024/1/e55667 UR - http://dx.doi.org/10.2196/55667 ID - info:doi/10.2196/55667 ER - TY - JOUR AU - Deinboll, Anne AU - Moe, Fredriksen Cathrine AU - Ludvigsen, Spliid Mette PY - 2024/11/11 TI - Participation in eHealth Communication Interventions Among Patients Undergoing Hemodialysis: Scoping Review JO - J Med Internet Res SP - e51900 VL - 26 KW - eHealth KW - electronic health records KW - hemodialysis KW - patient participation KW - renal dialysis KW - renal insufficiency KW - chronic KW - mobile phone N2 - Background: eHealth communication interventions have been shown to offer individuals with chronic kidney disease the opportunity to embrace dialysis therapies with greater confidence, the potential to obtain better clinical outcomes, and an increased quality of life. eHealth is an emerging field that offers diverse, flexible designs and delivery options. However, existing evidence on eHealth communication among patients undergoing hemodialysis is sparse and scattered and lacks systematization. Objective: This scoping review aims to identify and map the current evidence on patient participation in eHealth communication interventions. We aimed to map the associations between interventions and electronic health records, the participative role of individuals living with chronic kidney disease and undergoing hemodialysis, and the barriers to and facilitators of patient involvement in eHealth communication with health care professionals. Methods: This study used the Joanna Briggs Institute methodology for conducting a scoping review. Studies eligible for inclusion were those that included adult patients (aged >18 y) undergoing all types of hemodialysis, including prescheduled in-center hemodialysis and conventional home-based hemodialysis. Systematic searches were completed in Ovid MEDLINE, Ovid Embase, EBSCOhost CINAHL with Full Text, Scopus, and ProQuest Dissertations and Theses. Extracted data from the included studies were presented in figures and tables along with descriptions that responded to the research questions. This review was reported according to the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) guidelines. Results: In total, 9 peer-reviewed studies were included. The main result was a low participative patient role and a vaguely described link to electronic health records. The key participative facilitators were availability of and access to the intervention; security, trust, and confidence; patient knowledge of their health situation and use of self-care; and patient preparedness for an uncertain future health situation and the ability to relate to family and friends about it. The key participative barriers were lack of availability of and access to information, mistrust and lack of safety, lack of knowledge of health situation and self-care, and relational issues. All barriers and facilitators were related to health literacy. Conclusions: This scoping review summarizes 4 specific and 3 nonspecific eHealth communication interventions developed and evaluated in various studies involving patients receiving hemodialysis. A knowledge gap exists between low levels of patient participation in eHealth communication and patients? limited access to electronic health records. eHealth communication interventions should implement patient participation and focus on the fact that different modalities of eHealth communication can complement face-to-face communication. International Registered Report Identifier (IRRID): RR2-10.2196/38615 UR - https://www.jmir.org/2024/1/e51900 UR - http://dx.doi.org/10.2196/51900 UR - http://www.ncbi.nlm.nih.gov/pubmed/ ID - info:doi/10.2196/51900 ER - TY - JOUR AU - Miao, Jing AU - Thongprayoon, Charat AU - Garcia Valencia, Oscar AU - Craici, M. Iasmina AU - Cheungpasitporn, Wisit PY - 2024/10/10 TI - Navigating Nephrology's Decline Through a GPT-4 Analysis of Internal Medicine Specialties in the United States: Qualitative Study JO - JMIR Med Educ SP - e57157 VL - 10 KW - artificial intelligence KW - ChatGPT KW - nephrology fellowship training KW - fellowship matching KW - medical education KW - AI KW - nephrology KW - fellowship KW - United States KW - factor KW - chatbots KW - intellectual KW - complexity KW - work-life balance KW - procedural involvement KW - opportunity KW - career demand KW - financial compensation N2 - Background: The 2024 Nephrology fellowship match data show the declining interest in nephrology in the United States, with an 11% drop in candidates and a mere 66% (321/488) of positions filled. Objective: The study aims to discern the factors influencing this trend using ChatGPT, a leading chatbot model, for insights into the comparative appeal of nephrology versus other internal medicine specialties. Methods: Using the GPT-4 model, the study compared nephrology with 13 other internal medicine specialties, evaluating each on 7 criteria including intellectual complexity, work-life balance, procedural involvement, research opportunities, patient relationships, career demand, and financial compensation. Each criterion was assigned scores from 1 to 10, with the cumulative score determining the ranking. The approach included counteracting potential bias by instructing GPT-4 to favor other specialties over nephrology in reverse scenarios. Results: GPT-4 ranked nephrology only above sleep medicine. While nephrology scored higher than hospice and palliative medicine, it fell short in key criteria such as work-life balance, patient relationships, and career demand. When examining the percentage of filled positions in the 2024 appointment year match, nephrology?s filled rate was 66%, only higher than the 45% (155/348) filled rate of geriatric medicine. Nephrology?s score decreased by 4%?14% in 5 criteria including intellectual challenge and complexity, procedural involvement, career opportunity and demand, research and academic opportunities, and financial compensation. Conclusions: ChatGPT does not favor nephrology over most internal medicine specialties, highlighting its diminishing appeal as a career choice. This trend raises significant concerns, especially considering the overall physician shortage, and prompts a reevaluation of factors affecting specialty choice among medical residents. UR - https://mededu.jmir.org/2024/1/e57157 UR - http://dx.doi.org/10.2196/57157 ID - info:doi/10.2196/57157 ER - TY - JOUR AU - Overstreet, Morgan AU - Culpepper, Hannah AU - DeHoff, Deanna AU - Gebregziabher, Mulugeta AU - Posadas Salas, Aurora Maria AU - Su, Zemin AU - Chandler, Jessica AU - Bartlett, Felicia AU - Dunton, Paige AU - Carcella, Taylor AU - Taber, David PY - 2024/10/10 TI - Multifaceted Intervention to Improve Graft Outcome Disparities in African American Kidney Transplants (MITIGAAT Study): Protocol for a Randomized Controlled Trial JO - JMIR Res Protoc SP - e57784 VL - 13 KW - kidney transplant KW - mobile health KW - medication adherence KW - mHealth KW - nephrology KW - transplant surgery KW - postoperative monitoring KW - telemedicine KW - eHealth N2 - Background: The outcome disparities for African American recipients of kidney transplant is a public health issue that has plagued the field of transplant since its inception. Based on national data, African American recipients have nearly twice the risk of graft loss at 5 years after transplant, when compared with White recipients. Evidence demonstrates that medication nonadherence and high tacrolimus variability substantially impact graft outcomes and racial disparities, most notably late (>2 years) after the transplant. Nonadherence is a leading cause of graft loss. Prospective multicenter data demonstrate that one-third of all graft loss are directly attributed to nonadherence. We have spent 10 years of focused research to develop a comprehensive model explaining the predominant risk factors leading to disparities in African American kidney recipients. However, there are still gaps in patient-level data that hinder the deeper understanding of the disparities. Lack of data from the patient often lead to provider biases, which will be addressed with this intervention. Culturally competent, pharmacist-led interventions in medication therapy management will also address therapeutic inertia. Pharmacist interventions will mitigate medication access barriers as well (cost and insurance denials). Thus, this multidimensional intervention addresses patient, provider, and structural factors that drive racial disparities in African American kidney recipients. Objective: This prospective, randomized controlled trial aimed to determine the impact of multimodal health services intervention on health outcomes disparities in African American recipients of kidney transplant. The aims of this study are to improve adherence and control of late clinical issues, which are predominant factors for racial disparities in kidney recipients, through a technology-enabled, telehealth-delivered, 4-level intervention. Methods: The Multifaceted Intervention to Improve Graft Outcome Disparities in African American Kidney Transplants (MITIGAAT) study is a 24-month, 2-arm, single-center (Medical University of South Carolina), 1:1 randomized controlled trial involving 190 participants (95 in each arm), measuring the impact on adherence and control of late clinical issues for racial disparities in kidney recipients, through a technology-enabled, telehealth-delivered, 4-level intervention. The key clinical issues for this study include tacrolimus variability, blood pressure, and glucose control (in those with diabetes mellitus). We will also assess the impact of the intervention on health care use (hospitalizations and emergency department visits) and conduct a cost-benefit analysis. Finally, we will assess the impact of the intervention on acute rejection and graft survival rates as compared with a large contemporary national cohort. Results: This study was funded in July 2023. Enrolled began in April 2024 and is expected to be complete in 2026. All patients will complete the study by the end of 2028. Conclusions: In this protocol, we describe the study design, methods, aims, and outcome measures that will be used in the ongoing MITIGAAT clinical trials. Trial Registration: ClinicalTrials.gov NCT06023615; https://www.clinicaltrials.gov/study/NCT06023615 International Registered Report Identifier (IRRID): PRR1-10.2196/57784 UR - https://www.researchprotocols.org/2024/1/e57784 UR - http://dx.doi.org/10.2196/57784 UR - http://www.ncbi.nlm.nih.gov/pubmed/ ID - info:doi/10.2196/57784 ER - TY - JOUR AU - Xu, Lingyu AU - Li, Chenyu AU - Gao, Shuang AU - Zhao, Long AU - Guan, Chen AU - Shen, Xuefei AU - Zhu, Zhihui AU - Guo, Cheng AU - Zhang, Liwei AU - Yang, Chengyu AU - Bu, Quandong AU - Zhou, Bin AU - Xu, Yan PY - 2024/9/20 TI - Personalized Prediction of Long-Term Renal Function Prognosis Following Nephrectomy Using Interpretable Machine Learning Algorithms: Case-Control Study JO - JMIR Med Inform SP - e52837 VL - 12 KW - nephrectomy KW - acute kidney injury KW - acute kidney disease KW - chronic kidney disease KW - machine learning N2 - Background: Acute kidney injury (AKI) is a common adverse outcome following nephrectomy. The progression from AKI to acute kidney disease (AKD) and subsequently to chronic kidney disease (CKD) remains a concern; yet, the predictive mechanisms for these transitions are not fully understood. Interpretable machine learning (ML) models offer insights into how clinical features influence long-term renal function outcomes after nephrectomy, providing a more precise framework for identifying patients at risk and supporting improved clinical decision-making processes. Objective: This study aimed to (1) evaluate postnephrectomy rates of AKI, AKD, and CKD, analyzing long-term renal outcomes along different trajectories; (2) interpret AKD and CKD models using Shapley Additive Explanations values and Local Interpretable Model-Agnostic Explanations algorithm; and (3) develop a web-based tool for estimating AKD or CKD risk after nephrectomy. Methods: We conducted a retrospective cohort study involving patients who underwent nephrectomy between July 2012 and June 2019. Patient data were randomly split into training, validation, and test sets, maintaining a ratio of 76.5:8.5:15. Eight ML algorithms were used to construct predictive models for postoperative AKD and CKD. The performance of the best-performing models was assessed using various metrics. We used various Shapley Additive Explanations plots and Local Interpretable Model-Agnostic Explanations bar plots to interpret the model and generated directed acyclic graphs to explore the potential causal relationships between features. Additionally, we developed a web-based prediction tool using the top 10 features for AKD prediction and the top 5 features for CKD prediction. Results: The study cohort comprised 1559 patients. Incidence rates for AKI, AKD, and CKD were 21.7% (n=330), 15.3% (n=238), and 10.6% (n=165), respectively. Among the evaluated ML models, the Light Gradient-Boosting Machine (LightGBM) model demonstrated superior performance, with an area under the receiver operating characteristic curve of 0.97 for AKD prediction and 0.96 for CKD prediction. Performance metrics and plots highlighted the model?s competence in discrimination, calibration, and clinical applicability. Operative duration, hemoglobin, blood loss, urine protein, and hematocrit were identified as the top 5 features associated with predicted AKD. Baseline estimated glomerular filtration rate, pathology, trajectories of renal function, age, and total bilirubin were the top 5 features associated with predicted CKD. Additionally, we developed a web application using the LightGBM model to estimate AKD and CKD risks. Conclusions: An interpretable ML model effectively elucidated its decision-making process in identifying patients at risk of AKD and CKD following nephrectomy by enumerating critical features. The web-based calculator, found on the LightGBM model, can assist in formulating more personalized and evidence-based clinical strategies. UR - https://medinform.jmir.org/2024/1/e52837 UR - http://dx.doi.org/10.2196/52837 UR - http://www.ncbi.nlm.nih.gov/pubmed/ ID - info:doi/10.2196/52837 ER - TY - JOUR AU - Lonati, Caterina AU - Wellhausen, Marie AU - Pennig, Stefan AU - Röhrßen, Thomas AU - Kircelli, Fatih AU - Arendt, Svenja AU - Tschulena, Ulrich PY - 2024/8/6 TI - The Use of a Novel Virtual Reality Training Tool for Peritoneal Dialysis: Qualitative Assessment Among Health Care Professionals JO - JMIR Med Educ SP - e46220 VL - 10 KW - peritoneal dialysis KW - virtual reality KW - patient education KW - patient training KW - chronic kidney disease KW - nursing KW - qualitative assessment N2 - Background: Effective peritoneal dialysis (PD) training is essential for performing dialysis at home and reducing the risk of peritonitis and other PD-related infections. Virtual reality (VR) is an innovative learning tool that is able to combine theoretical information, interactivity, and behavioral instructions while offering a playful learning environment. To improve patient training for PD, Fresenius Medical Care launched the stay?safe MyTraining VR, a novel educational program based on the use of a VR headset and a handheld controller. Objective: This qualitative assessment aims to investigate opinions toward the new tool among the health care professionals (HCPs) who were responsible for implementing the VR application. Methods: We recruited nursing staff and nephrologists who have gained practical experience with the stay?safe MyTraining VR within pilot dialysis centers. Predetermined open-ended questions were administered during individual and group video interviews. Results: We interviewed 7 HCPs who have 2 to 20 years of experience in PD training. The number of patients trained with the stay?safe MyTraining VR ranged from 2 to 5 for each professional. The stay?safe MyTraining VR was well accepted and perceived as a valuable supplementary tool for PD training. From the respondents? perspective, the technology improved patients? learning experience by facilitating the internalization of both medical information and procedural skills. HCPs highlighted that the opportunity offered by VR to reiterate training activities in a positive and safe learning environment, according to each patient?s needs, can facilitate error correction and implement a standardized training curriculum. However, VR had limited use in the final phase of the patient PD training program, where learners need to get familiar with the handling of the materials. Moreover, the traditional PD training was still considered essential to manage the emotional and motivational aspects and address any patient-specific application-oriented questions. In addition to its use within PD training, VR was perceived as a useful tool to support the decision-making process of patients and train other HCPs. Moreover, VR introduction was associated with increased efficiency and productivity of HCPs because it enabled them to perform other activities while the patient was practicing with the device. As for patients? acceptance of the new tool, interviewees reported positive feedback, including that of older adults. Limited use with patients experiencing dementia or severe visual impairment or lacking sensomotoric competence was mentioned. Conclusions: The stay?safe MyTraining VR is suggested to improve training efficiency and efficacy and thus could have a positive impact in the PD training scenario. Our study offers a process proposal that can serve as a guide to the implementation of a VR-based PD training program within other dialysis centers. Dedicated research is needed to assess the operational benefits and the consequences on patient management. UR - https://mededu.jmir.org/2024/1/e46220 UR - http://dx.doi.org/10.2196/46220 UR - http://www.ncbi.nlm.nih.gov/pubmed/39106093 ID - info:doi/10.2196/46220 ER - TY - JOUR AU - Valle, Jhaqueline AU - Lebensburger, D. Jeffrey AU - Garimella, S. Pranav AU - Gopal, Srila PY - 2024/7/15 TI - Prevalence, Mortality, and Access to Care for Chronic Kidney Disease in Medicaid-Enrolled Adults With Sickle Cell Disease in California: Retrospective Cohort Study JO - JMIR Public Health Surveill SP - e57290 VL - 10 KW - sickle cell disease KW - chronic kidney disease KW - prevalence KW - mortality KW - access to care KW - Medicaid KW - California KW - United States KW - retrospective KW - cohort study KW - investigate KW - emergency department KW - hospitalization KW - specialized care KW - adult KW - adults KW - hematologist KW - hematologists KW - nephrologist KW - nephrologists KW - t-test N2 - Background: Chronic kidney disease (CKD) is a significant complication in patients with sickle cell disease (SCD), leading to increased mortality. Objective: This study aims to investigate the burden of CKD in Medicaid-enrolled adults with SCD in California, examine differences in disease burden between male and female individuals, and assess mortality rates and access to specialized care. Methods: This retrospective cohort study used the California Sickle Cell Data Collection program to identify and monitor individuals with SCD. Medicaid claims, vital records, emergency department, and hospitalization data from 2011 to 2020 were analyzed. CKD prevalence was assessed based on ICD (International Classification of Diseases) codes, and mortality rates were calculated. Access to specialized care was examined through outpatient encounter rates with hematologists and nephrologists. Results: Among the 2345 adults with SCD, 24.4% (n=572) met the case definition for CKD. The SCD-CKD group was older at the beginning of this study (average age 44, SD 14 vs 34, SD 12.6 years) than the group without CKD. CKD prevalence increased with age, revealing significant disparities by sex. While the youngest (18-29 years) and oldest (>65 years) groups showed similar CKD prevalences between sexes (female: 12/111, 10.8% and male: 12/101, 11.9%; female: 74/147, 50.3% and male: 34/66, 51.5%, respectively), male individuals in the aged 30-59 years bracket exhibited significantly higher rates than female individuals (30-39 years: 49/294, 16.7%, P=.01; 40-49 years: 52/182, 28.6%, P=.02; and 50-59 years: 76/157,48.4%, P<.001). During this study, of the 2345 adults, 435 (18.5%) deaths occurred, predominantly within the SCD-CKD cohort (226/435, 39.5%). The median age at death was 53 (IQR 61-44) years for the SCD-CKD group compared to 43 (IQR 33-56) years for the SCD group, with male individuals in the SCD-CKD group showing significantly higher mortality rates (111/242, 45.9%; P=.009) than female individuals (115/330, 34.9%). Access to specialist care was notably limited: approximately half (281/572, 49.1%) of the SCD-CKD cohort had no hematologist visits, and 61.9% (354/572) did not see a nephrologist during this study?s period. Conclusions: This study provides robust estimates of CKD prevalence and mortality among Medicaid-enrolled adults with SCD in California. The findings highlight the need for improved access to specialized care for this population and increased awareness of the high mortality risk and progression associated with CKD. UR - https://publichealth.jmir.org/2024/1/e57290 UR - http://dx.doi.org/10.2196/57290 UR - http://www.ncbi.nlm.nih.gov/pubmed/39008353 ID - info:doi/10.2196/57290 ER - TY - JOUR AU - Ortiz, Fernanda AU - Grasberger, Juulia AU - Ekstrand, Agneta AU - Helanterä, Ilkka AU - Giunti, Guido PY - 2024/7/9 TI - Interactive Health Technology Tool for Kidney Living Donor Assessment to Standardize the Informed Consent Process: Usability and Qualitative Content Analysis JO - JMIR Form Res SP - e47785 VL - 8 KW - eHealth KW - kidney living donor KW - informed consent KW - telemedicine KW - process standardization KW - kidney KW - donor KW - tool KW - usability KW - psychological impact KW - utility KW - smartphone KW - coping KW - surgery N2 - Background: Kidney living donation carries risks, yet standardized information provision regarding nephrectomy risks and psychological impacts for candidates remains lacking. Objective: This study assesses the benefit of interactive health technology in improving the informed consent process for kidney living donation. Methods: The Kidney Hub institutional open portal offers comprehensive information on kidney disease and donation. Individuals willing to start the kidney living donation process at Helsinki University Hospital (January 2019-January 2022) were invited to use the patient-tailored digital care path (Living Donor Digital Care Path) included in the Kidney Hub. This platform provides detailed donation process information and facilitates communication between health care professionals and patients. eHealth literacy was evaluated via the eHealth Literacy Scale (eHEALS), usability with the System Usability Scale (SUS), and system utility through Likert-scale surveys with scores of 1-5. Qualitative content analysis addressed an open-ended question. Results: The Kidney Hub portal received over 8000 monthly visits, including to its sections on donation benefits (n=1629 views) and impact on donors? lives (n=4850 views). Of 127 living kidney donation candidates, 7 did not use Living Donor Digital Care Path. Users? ages ranged from 20 to 79 years, and they exchanged over 3500 messages. A total of 74 living donor candidates participated in the survey. Female candidates more commonly searched the internet about kidney donation (n=79 female candidates vs n=48 male candidates; P=.04). The mean eHEALS score correlated with internet use for health decisions (r=0.45; P<.001) and its importance (r=0.40; P=.01). Participants found that the Living Donor Digital Care Path was technically satisfactory (mean SUS score 4.4, SD 0.54) and useful but not pivotal in donation decision-making. Concerns focused on postsurgery coping for donors and recipients. Conclusions: Telemedicine effectively educates living kidney donor candidates on the donation process. The Living Donor Digital Care Path serves as a valuable eHealth tool, aiding clinicians in standardizing steps toward informed consent. Trial Registration: ClinicalTrials.gov NCT04791670; https://clinicaltrials.gov/study/NCT04791670 International Registered Report Identifier (IRRID): RR2-10.1136/bmjopen-2021-051166 UR - https://formative.jmir.org/2024/1/e47785 UR - http://dx.doi.org/10.2196/47785 UR - http://www.ncbi.nlm.nih.gov/pubmed/ ID - info:doi/10.2196/47785 ER - TY - JOUR AU - Heo, Suncheol AU - Kang, Eun-Ae AU - Yu, Yong Jae AU - Kim, Reong Hae AU - Lee, Suehyun AU - Kim, Kwangsoo AU - Hwangbo, Yul AU - Park, Woong Rae AU - Shin, Hyunah AU - Ryu, Kyeongmin AU - Kim, Chungsoo AU - Jung, Hyojung AU - Chegal, Yebin AU - Lee, Jae-Hyun AU - Park, Rang Yu PY - 2024/7/5 TI - Time Series AI Model for Acute Kidney Injury Detection Based on a Multicenter Distributed Research Network: Development and Verification Study JO - JMIR Med Inform SP - e47693 VL - 12 KW - adverse drug reaction KW - real world data KW - multicenter study KW - distributed research network KW - common data model KW - time series AI KW - time series KW - artificial intelligence KW - machine learning KW - adverse reaction KW - adverse reactions KW - detect KW - detection KW - toxic KW - toxicity KW - renal KW - kidney KW - nephrology KW - pharmaceutical KW - pharmacology KW - pharmacy KW - pharmaceutics N2 - Background: Acute kidney injury (AKI) is a marker of clinical deterioration and renal toxicity. While there are many studies offering prediction models for the early detection of AKI, those predicting AKI occurrence using distributed research network (DRN)?based time series data are rare. Objective: In this study, we aimed to detect the early occurrence of AKI by applying an interpretable long short-term memory (LSTM)?based model to hospital electronic health record (EHR)?based time series data in patients who took nephrotoxic drugs using a DRN. Methods: We conducted a multi-institutional retrospective cohort study of data from 6 hospitals using a DRN. For each institution, a patient-based data set was constructed using 5 drugs for AKI, and an interpretable multivariable LSTM (IMV-LSTM) model was used for training. This study used propensity score matching to mitigate differences in demographics and clinical characteristics. Additionally, the temporal attention values of the AKI prediction model?s contribution variables were demonstrated for each institution and drug, with differences in highly important feature distributions between the case and control data confirmed using 1-way ANOVA. Results: This study analyzed 8643 and 31,012 patients with and without AKI, respectively, across 6 hospitals. When analyzing the distribution of AKI onset, vancomycin showed an earlier onset (median 12, IQR 5-25 days), and acyclovir was the slowest compared to the other drugs (median 23, IQR 10-41 days). Our temporal deep learning model for AKI prediction performed well for most drugs. Acyclovir had the highest average area under the receiver operating characteristic curve score per drug (0.94), followed by acetaminophen (0.93), vancomycin (0.92), naproxen (0.90), and celecoxib (0.89). Based on the temporal attention values of the variables in the AKI prediction model, verified lymphocytes and calcvancomycin ium had the highest attention, whereas lymphocytes, albumin, and hemoglobin tended to decrease over time, and urine pH and prothrombin time tended to increase. Conclusions: Early surveillance of AKI outbreaks can be achieved by applying an IMV-LSTM based on time series data through an EHR-based DRN. This approach can help identify risk factors and enable early detection of adverse drug reactions when prescribing drugs that cause renal toxicity before AKI occurs. UR - https://medinform.jmir.org/2024/1/e47693 UR - http://dx.doi.org/10.2196/47693 ID - info:doi/10.2196/47693 ER - TY - JOUR AU - Shen, Hongxia AU - van der Kleij, Rianne AU - van der Boog, M. Paul J. AU - Chavannes, H. Niels PY - 2024/6/13 TI - Developing a Tailored eHealth Self-Management Intervention for Patients With Chronic Kidney Disease in China: Intervention Mapping Approach JO - JMIR Form Res SP - e48605 VL - 8 KW - eHealth KW - self-management KW - intervention mapping KW - chronic kidney disease KW - intervention development KW - mobile phone N2 - Background: Chronic kidney disease (CKD) is a major public health concern. Adequate self-management skills are vital to reduce CKD burden, optimize patient health outcomes, and control health care expenditures. Using eHealth to support CKD self-management has the potential to promote healthy behaviors and improve health outcomes of patients with CKD. However, knowledge of the implementation of such interventions in general, and in China specifically, is still limited. Objective: This study aims to develop a tailored eHealth self-management intervention for patients with CKD in China based on the Dutch Medical Dashboard (MD) eHealth self-management intervention. Methods: We used an intervention mapping approach. In phase 1, a systematic review and 2 qualitative studies were conducted to examine the needs, beliefs, and perceptions of patients with CKD and health care professionals regarding CKD self-management and eHealth interventions. Afterward, key factors gathered from the aforementioned studies were categorized following the 5 domains of the Consolidated Framework for Implementation Research (CFIR). In phase 2, we specified program outcomes, performance objectives, determinants, theory-based methods, and practical strategies. Knowledge obtained from previous results was combined to complement core components of the MD self-management intervention and adapt them for Chinese patients with CKD. Additionally, the CFIR?Expert Recommendations for Implementing Change Matching Tool was pragmatically used to generate a list of potential implementation strategies to address the key factors influencing the implementation of eHealth CKD self-management interventions, and implementation strategies were discussed and finalized with the intervention monitoring group. Results: An overview of the CFIR domains showed the essential factors influencing the implementation of eHealth CKD self-management interventions in Chinese settings, including ?knowledge and beliefs? in the domain ?individual characteristics,? ?quality and advantage of eHealth intervention? in the domain ?intervention characteristics,? ?compatibility? in the domain ?inner setting,? and ?cultural context? in the domain ?outer setting.? To ensure the effectiveness of the Dutch MD?based self-management intervention, we did not change the core self-management intervention components of MD that underlie its effectiveness, such as self-monitoring. We identified surface-level cultural adaptations involving customizing intervention content, messages, and approaches to the observable cultural characteristics of the local population to enhance the intervention?s appeal, receptivity, and feasibility, such as providing video or voice call options to support interactions with health care professionals. Furthermore, the adapted modules such as Knowledge Center and My Self-Monitoring were developed in a mobile health app. Conclusions: Our study resulted in the delivery of a culturally tailored, standardized eHealth self-management intervention for patients with CKD in China that has the potential to optimize patients? self-management skills and improve health status and quality of life. Moreover, our study?s research approach and results can inform future research on the tailoring and translation of evidence-based, eHealth self-management interventions to various contexts. Trial Registration: ClinicalTrials.gov NCT04212923; https://classic.clinicaltrials.gov/ct2/show/NCT04212923 UR - https://formative.jmir.org/2024/1/e48605 UR - http://dx.doi.org/10.2196/48605 UR - http://www.ncbi.nlm.nih.gov/pubmed/38869943 ID - info:doi/10.2196/48605 ER - TY - JOUR AU - Liu, Pei AU - Liu, Yijun AU - Liu, Hao AU - Xiong, Linping AU - Mei, Changlin AU - Yuan, Lei PY - 2024/6/3 TI - A Random Forest Algorithm for Assessing Risk Factors Associated With Chronic Kidney Disease: Observational Study JO - Asian Pac Isl Nurs J SP - e48378 VL - 8 KW - chronic kidney disease KW - random forest model KW - risk factors KW - assessment N2 - Background: The prevalence and mortality rate of chronic kidney disease (CKD) are increasing year by year, and it has become a global public health issue. The economic burden caused by CKD is increasing at a rate of 1% per year. CKD is highly prevalent and its treatment cost is high but unfortunately remains unknown. Therefore, early detection and intervention are vital means to mitigate the treatment burden on patients and decrease disease progression. Objective: In this study, we investigated the advantages of using the random forest (RF) algorithm for assessing risk factors associated with CKD. Methods: We included 40,686 people with complete screening records who underwent screening between January 1, 2015, and December 22, 2020, in Jing?an District, Shanghai, China. We grouped the participants into those with and those without CKD by staging based on the glomerular filtration rate staging and grouping based on albuminuria. Using a logistic regression model, we determined the relationship between CKD and risk factors. The RF machine learning algorithm was used to score the predictive variables and rank them based on their importance to construct a prediction model. Results: The logistic regression model revealed that gender, older age, obesity, abnormal index estimated glomerular filtration rate, retirement status, and participation in urban employee medical insurance were significantly associated with the risk of CKD. On RF algorithm?based screening, the top 4 factors influencing CKD were age, albuminuria, working status, and urinary albumin-creatinine ratio. The RF model predicted an area under the receiver operating characteristic curve of 93.15%. Conclusions: Our findings reveal that the RF algorithm has significant predictive value for assessing risk factors associated with CKD and allows the screening of individuals with risk factors. This has crucial implications for early intervention and prevention of CKD. UR - https://apinj.jmir.org/2024/1/e48378 UR - http://dx.doi.org/10.2196/48378 UR - http://www.ncbi.nlm.nih.gov/pubmed/38830204 ID - info:doi/10.2196/48378 ER - TY - JOUR AU - Nielsen, Flindt Steffen AU - Duus, Lundgreen Camilla AU - Buus, Henrik Niels AU - Bech, Nørgaard Jesper AU - Mose, Holden Frank PY - 2024/5/29 TI - Effects of Empagliflozin in Type 2 Diabetes With and Without Chronic Kidney Disease and Nondiabetic Chronic Kidney Disease: Protocol for 3 Crossover Randomized Controlled Trials (SiRENA Project) JO - JMIR Res Protoc SP - e56067 VL - 13 KW - SGLT2i KW - empagliflozin KW - renal function KW - blood flow KW - DM2 KW - diabetes mellitus type 2 KW - CKD KW - chronic kidney disease KW - vascular function KW - sodium-glucose cotransporter 2 inhibitors KW - T2DM KW - type 2 diabetes mellitus KW - randomized controlled trial KW - RCT KW - CVD KW - placebo KW - renal KW - recruitment KW - Denmark KW - cardiovascular disease N2 - Background: Sodium-glucose-cotransporter 2 inhibitors (SGLT2is) have revolutionized the treatment of type 2 diabetes mellitus (DM2) and chronic kidney disease (CKD), reducing the risk of cardiovascular and renal end points by up to 40%. The underlying mechanisms are not fully understood. Objective: The study aims to examine the effects of empagliflozin versus placebo on renal hemodynamics, sodium balance, vascular function, and markers of the innate immune system in patients with DM2, DM2 and CKD, and nondiabetic CKD. Methods: We conducted 3 double-blind, crossover, randomized controlled trials, each with identical study protocols but different study populations. We included patients with DM2 and preserved kidney function (estimated glomerular filtration rate >60 mL/min/1.73 m2), DM2 and CKD, and nondiabetic CKD (both with estimated glomerular filtration rate 20-60 mL/min/1.73 m2). Each participant was randomly assigned to 4 weeks of treatment with either 10 mg of empagliflozin once daily or a matching placebo. After a wash-out period of at least 2 weeks, participants were crossed over to the opposite treatment. End points were measured at the end of each treatment period. The primary end point was renal blood flow measured with 82Rubidium positron emission tomography?computed tomography (82Rb-PET/CT). Secondary end points include glomerular filtration rate measured with 99mTechnetium-diethylene-triamine-pentaacetate (99mTc-DTPA) clearance, vascular function assessed by forearm venous occlusion strain gauge plethysmography, measurements of the nitric oxide (NO) system, water and sodium excretion, body composition measurements, and markers of the complement immune system. Results: Recruitment began in April 2021 and was completed in September 2022. Examinations were completed by December 2022. In total, 49 participants completed the project: 16 participants in the DM2 and preserved kidney function study, 17 participants in the DM2 and CKD study, and 16 participants in the nondiabetic CKD study. Data analysis is ongoing. Results are yet to be published. Conclusions: This paper describes the rationale, design, and methods used in a project consisting of 3 double-blind, crossover, randomized controlled trials examining the effects of empagliflozin versus placebo in patients with DM2 with and without CKD and patients with nondiabetic CKD, respectively. Trial Registration: EU Clinical Trials Register 2019-004303-12; https://www.clinicaltrialsregister.eu/ctr-search/search?query=2019-004303-12, EU Clinical Trials Register 2019-004447-80; https://www.clinicaltrialsregister.eu/ctr-search/search?query=2019-004447-80, EU Clinical Trials Register 2019-004467-50; https://www.clinicaltrialsregister.eu/ctr-search/search?query=and+2019-004467-50 International Registered Report Identifier (IRRID): DERR1-10.2196/56067 UR - https://www.researchprotocols.org/2024/1/e56067 UR - http://dx.doi.org/10.2196/56067 UR - http://www.ncbi.nlm.nih.gov/pubmed/38680116 ID - info:doi/10.2196/56067 ER - TY - JOUR AU - Aspelund, Anna AU - Valkonen, Paula AU - Viitanen, Johanna AU - Rauta, Virpi PY - 2024/5/14 TI - Designing for Improved Patient Experiences in Home Dialysis: Usability and User Experience Findings From User-Based Evaluation Study With Patients With Chronic Conditions JO - JMIR Hum Factors SP - e53691 VL - 11 KW - usability KW - UX KW - user experience KW - PX KW - patient experience KW - user-based evaluation KW - patients KW - eHealth KW - digital health solution KW - kidney disease KW - home dialysis N2 - Background: Chronic kidney disease affects 10% of the population worldwide, and the number of patients receiving treatment for end-stage kidney disease is forecasted to increase. Therefore, there is a pressing need for innovative digital solutions that increase the efficiency of care and improve patients? quality of life. The aim of the eHealth in Home Dialysis project is to create a novel eHealth solution, called eC4Me, to facilitate predialysis and home dialysis care for patients with chronic kidney disease. Objective: Our study aimed to evaluate the usability, user experience (UX), and patient experience (PX) of the first version of the eC4Me solution. Methods: We used a user-based evaluation approach involving usability testing, questionnaire, and interview methods. The test sessions were conducted remotely with 10 patients with chronic kidney disease, 5 of whom had used the solution in their home environment before the tests, while the rest were using it for the first time. Thematic analysis was used to analyze user test and questionnaire data, and descriptive statistics were calculated for the UMUX (Usability Metric for User Experience) scores. Results: Most usability problems were related to navigation, the use of terminology, and the presentation of health-related data. Despite usability challenges, UMUX ratings of the solution were positive overall. The results showed noteworthy variation in the expected benefits and perceived effort of using the solution. From a PX perspective, it is important that the solution supports patients? own health-related goals and fits with the needs of their everyday lives with the disease. Conclusions: A user-based evaluation is a useful and necessary part of the eHealth solution development process. Our study findings can be used to improve the usability and UX of the evaluated eC4Me solution. Patients should be actively involved in the solution development process when specifying what information is relevant for them. Traditional usability tests complemented with questionnaire and interview methods can serve as a meaningful methodological approach for gaining insight not only into usability but also into UX- and PX-related aspects of digital health solutions. UR - https://humanfactors.jmir.org/2024/1/e53691 UR - http://dx.doi.org/10.2196/53691 UR - http://www.ncbi.nlm.nih.gov/pubmed/38743476 ID - info:doi/10.2196/53691 ER - TY - JOUR AU - Chedid, Maroun AU - Chebib, T. Fouad AU - Dahlen, Erin AU - Mueller, Theodore AU - Schnell, Theresa AU - Gay, Melissa AU - Hommos, Musab AU - Swaminathan, Sundararaman AU - Garg, Arvind AU - Mao, Michael AU - Amberg, Brigid AU - Balderes, Kirk AU - Johnson, F. Karen AU - Bishop, Alyssa AU - Vaughn, Kay Jackqueline AU - Hogan, Marie AU - Torres, Vicente AU - Chaudhry, Rajeev AU - Zoghby, Ziad PY - 2024/5/1 TI - An Electronic Health Record?Integrated Application for Standardizing Care and Monitoring Patients With Autosomal Dominant Polycystic Kidney Disease Enrolled in a Tolvaptan Clinic: Design and Implementation Study JO - JMIR Med Inform SP - e50164 VL - 12 KW - ADPKD KW - autosomal dominant polycystic kidney disease KW - polycystic kidney disease KW - tolvaptan KW - EHR KW - electronic health record KW - digital health solutions KW - monitoring KW - kidney disease KW - drug-related toxicity KW - digital application KW - management KW - chronic disease N2 - Background: Tolvaptan is the only US Food and Drug Administration?approved drug to slow the progression of autosomal dominant polycystic kidney disease (ADPKD), but it requires strict clinical monitoring due to potential serious adverse events. Objective: We aimed to share our experience in developing and implementing an electronic health record (EHR)?based application to monitor patients with ADPKD who were initiated on tolvaptan. Methods: The application was developed in collaboration with clinical informatics professionals based on our clinical protocol with frequent laboratory test monitoring to detect early drug-related toxicity. The application streamlined the clinical workflow and enabled our nursing team to take appropriate actions in real time to prevent drug-related serious adverse events. We retrospectively analyzed the characteristics of the enrolled patients. Results: As of September 2022, a total of 214 patients were enrolled in the tolvaptan program across all Mayo Clinic sites. Of these, 126 were enrolled in the Tolvaptan Monitoring Registry application and 88 in the Past Tolvaptan Patients application. The mean age at enrollment was 43.1 (SD 9.9) years. A total of 20 (9.3%) patients developed liver toxicity, but only 5 (2.3%) had to discontinue the drug. The 2 EHR-based applications allowed consolidation of all necessary patient information and real-time data management at the individual or population level. This approach facilitated efficient staff workflow, monitoring of drug-related adverse events, and timely prescription renewal. Conclusions: Our study highlights the feasibility of integrating digital applications into the EHR workflow to facilitate efficient and safe care delivery for patients enrolled in a tolvaptan program. This workflow needs further validation but could be extended to other health care systems managing chronic diseases requiring drug monitoring. UR - https://medinform.jmir.org/2024/1/e50164 UR - http://dx.doi.org/10.2196/50164 ID - info:doi/10.2196/50164 ER - TY - JOUR AU - Osmanodja, Bilgin AU - Sassi, Zeineb AU - Eickmann, Sascha AU - Hansen, Maria Carla AU - Roller, Roland AU - Burchardt, Aljoscha AU - Samhammer, David AU - Dabrock, Peter AU - Möller, Sebastian AU - Budde, Klemens AU - Herrmann, Anne PY - 2024/4/1 TI - Investigating the Impact of AI on Shared Decision-Making in Post-Kidney Transplant Care (PRIMA-AI): Protocol for a Randomized Controlled Trial JO - JMIR Res Protoc SP - e54857 VL - 13 KW - shared decision-making KW - SDM KW - kidney transplantation KW - artificial intelligence KW - AI KW - decision-support system KW - DSS KW - qualitative research N2 - Background: Patients after kidney transplantation eventually face the risk of graft loss with the concomitant need for dialysis or retransplantation. Choosing the right kidney replacement therapy after graft loss is an important preference-sensitive decision for kidney transplant recipients. However, the rate of conversations about treatment options after kidney graft loss has been shown to be as low as 13% in previous studies. It is unknown whether the implementation of artificial intelligence (AI)?based risk prediction models can increase the number of conversations about treatment options after graft loss and how this might influence the associated shared decision-making (SDM). Objective: This study aims to explore the impact of AI-based risk prediction for the risk of graft loss on the frequency of conversations about the treatment options after graft loss, as well as the associated SDM process. Methods: This is a 2-year, prospective, randomized, 2-armed, parallel-group, single-center trial in a German kidney transplant center. All patients will receive the same routine post?kidney transplant care that usually includes follow-up visits every 3 months at the kidney transplant center. For patients in the intervention arm, physicians will be assisted by a validated and previously published AI-based risk prediction system that estimates the risk for graft loss in the next year, starting from 3 months after randomization until 24 months after randomization. The study population will consist of 122 kidney transplant recipients >12 months after transplantation, who are at least 18 years of age, are able to communicate in German, and have an estimated glomerular filtration rate <30 mL/min/1.73 m2. Patients with multi-organ transplantation, or who are not able to communicate in German, as well as underage patients, cannot participate. For the primary end point, the proportion of patients who have had a conversation about their treatment options after graft loss is compared at 12 months after randomization. Additionally, 2 different assessment tools for SDM, the CollaboRATE mean score and the Control Preference Scale, are compared between the 2 groups at 12 months and 24 months after randomization. Furthermore, recordings of patient-physician conversations, as well as semistructured interviews with patients, support persons, and physicians, are performed to support the quantitative results. Results: The enrollment for the study is ongoing. The first results are expected to be submitted for publication in 2025. Conclusions: This is the first study to examine the influence of AI-based risk prediction on physician-patient interaction in the context of kidney transplantation. We use a mixed methods approach by combining a randomized design with a simple quantitative end point (frequency of conversations), different quantitative measurements for SDM, and several qualitative research methods (eg, records of physician-patient conversations and semistructured interviews) to examine the implementation of AI-based risk prediction in the clinic. Trial Registration: ClinicalTrials.gov NCT06056518; https://clinicaltrials.gov/study/NCT06056518 International Registered Report Identifier (IRRID): PRR1-10.2196/54857 UR - https://www.researchprotocols.org/2024/1/e54857 UR - http://dx.doi.org/10.2196/54857 UR - http://www.ncbi.nlm.nih.gov/pubmed/38557315 ID - info:doi/10.2196/54857 ER - TY - JOUR AU - Schapranow, Matthieu-P AU - Bayat, Mozhgan AU - Rasheed, Aadil AU - Naik, Marcel AU - Graf, Verena AU - Schmidt, Danilo AU - Budde, Klemens AU - Cardinal, Héloïse AU - Sapir-Pichhadze, Ruth AU - Fenninger, Franz AU - Sherwood, Karen AU - Keown, Paul AU - Günther, P. Oliver AU - Pandl, D. Konstantin AU - Leiser, Florian AU - Thiebes, Scott AU - Sunyaev, Ali AU - Niemann, Matthias AU - Schimanski, Andreas AU - Klein, Thomas PY - 2023/12/22 TI - NephroCAGE?German-Canadian Consortium on AI for Improved Kidney Transplantation Outcome: Protocol for an Algorithm Development and Validation Study JO - JMIR Res Protoc SP - e48892 VL - 12 KW - posttransplant risks KW - kidney transplantation KW - federated learning infrastructure KW - clinical prediction model KW - donor-recipient matching KW - multinational transplant data set N2 - Background: Recent advances in hardware and software enabled the use of artificial intelligence (AI) algorithms for analysis of complex data in a wide range of daily-life use cases. We aim to explore the benefits of applying AI to a specific use case in transplant nephrology: risk prediction for severe posttransplant events. For the first time, we combine multinational real-world transplant data, which require specific legal and technical protection measures. Objective: The German-Canadian NephroCAGE consortium aims to develop and evaluate specific processes, software tools, and methods to (1) combine transplant data of more than 8000 cases over the past decades from leading transplant centers in Germany and Canada, (2) implement specific measures to protect sensitive transplant data, and (3) use multinational data as a foundation for developing high-quality prognostic AI models. Methods: To protect sensitive transplant data addressing the first and second objectives, we aim to implement a decentralized NephroCAGE federated learning infrastructure upon a private blockchain. Our NephroCAGE federated learning infrastructure enables a switch of paradigms: instead of pooling sensitive data into a central database for analysis, it enables the transfer of clinical prediction models (CPMs) to clinical sites for local data analyses. Thus, sensitive transplant data reside protected in their original sites while the comparable small algorithms are exchanged instead. For our third objective, we will compare the performance of selected AI algorithms, for example, random forest and extreme gradient boosting, as foundation for CPMs to predict severe short- and long-term posttransplant risks, for example, graft failure or mortality. The CPMs will be trained on donor and recipient data from retrospective cohorts of kidney transplant patients. Results: We have received initial funding for NephroCAGE in February 2021. All clinical partners have applied for and received ethics approval as of 2022. The process of exploration of clinical transplant database for variable extraction has started at all the centers in 2022. In total, 8120 patient records have been retrieved as of August 2023. The development and validation of CPMs is ongoing as of 2023. Conclusions: For the first time, we will (1) combine kidney transplant data from nephrology centers in Germany and Canada, (2) implement federated learning as a foundation to use such real-world transplant data as a basis for the training of CPMs in a privacy-preserving way, and (3) develop a learning software system to investigate population specifics, for example, to understand population heterogeneity, treatment specificities, and individual impact on selected posttransplant outcomes. International Registered Report Identifier (IRRID): DERR1-10.2196/48892 UR - https://www.researchprotocols.org/2023/1/e48892 UR - http://dx.doi.org/10.2196/48892 UR - http://www.ncbi.nlm.nih.gov/pubmed/38133915 ID - info:doi/10.2196/48892 ER - TY - JOUR AU - Persson, Inger AU - Grünwald, Adam AU - Morvan, Ludivine AU - Becedas, David AU - Arlbrandt, Martin PY - 2023/12/14 TI - A Machine Learning Algorithm Predicting Acute Kidney Injury in Intensive Care Unit Patients (NAVOY Acute Kidney Injury): Proof-of-Concept Study JO - JMIR Form Res SP - e45979 VL - 7 KW - acute kidney injury KW - AKI KW - algorithm KW - early detection KW - electronic health records KW - ICU KW - intensive care unit KW - machine learning KW - nephrology KW - prediction KW - software as a medical device N2 - Background: Acute kidney injury (AKI) represents a significant global health challenge, leading to increased patient distress and financial health care burdens. The development of AKI in intensive care unit (ICU) settings is linked to prolonged ICU stays, a heightened risk of long-term renal dysfunction, and elevated short- and long-term mortality rates. The current diagnostic approach for AKI is based on late indicators, such as elevated serum creatinine and decreased urine output, which can only detect AKI after renal injury has transpired. There are no treatments to reverse or restore renal function once AKI has developed, other than supportive care. Early prediction of AKI enables proactive management and may improve patient outcomes. Objective: The primary aim was to develop a machine learning algorithm, NAVOY Acute Kidney Injury, capable of predicting the onset of AKI in ICU patients using data routinely collected in ICU electronic health records. The ultimate goal was to create a clinical decision support tool that empowers ICU clinicians to proactively manage AKI and, consequently, enhance patient outcomes. Methods: We developed the NAVOY Acute Kidney Injury algorithm using a hybrid ensemble model, which combines the strengths of both a Random Forest (Leo Breiman and Adele Cutler) and an XGBoost model (Tianqi Chen). To ensure the accuracy of predictions, the algorithm used 22 clinical variables for hourly predictions of AKI as defined by the Kidney Disease: Improving Global Outcomes guidelines. Data for algorithm development were sourced from the Massachusetts Institute of Technology Lab for Computational Physiology Medical Information Mart for Intensive Care IV clinical database, focusing on ICU patients aged 18 years or older. Results: The developed algorithm, NAVOY Acute Kidney Injury, uses 4 hours of input and can, with high accuracy, predict patients with a high risk of developing AKI 12 hours before onset. The prediction performance compares well with previously published prediction algorithms designed to predict AKI onset in accordance with Kidney Disease: Improving Global Outcomes diagnosis criteria, with an impressive area under the receiver operating characteristics curve (AUROC) of 0.91 and an area under the precision-recall curve (AUPRC) of 0.75. The algorithm?s predictive performance was externally validated on an independent hold-out test data set, confirming its ability to predict AKI with exceptional accuracy. Conclusions: NAVOY Acute Kidney Injury is an important development in the field of critical care medicine. It offers the ability to predict the onset of AKI with high accuracy using only 4 hours of data routinely collected in ICU electronic health records. This early detection capability has the potential to strengthen patient monitoring and management, ultimately leading to improved patient outcomes. Furthermore, NAVOY Acute Kidney Injury has been granted Conformite Europeenne (CE)?marking, marking a significant milestone as the first CE-marked AKI prediction algorithm for commercial use in European ICUs. UR - https://formative.jmir.org/2023/1/e45979 UR - http://dx.doi.org/10.2196/45979 UR - http://www.ncbi.nlm.nih.gov/pubmed/38096015 ID - info:doi/10.2196/45979 ER - TY - JOUR AU - Reston, Elizabeth Riley AU - Caskey, J. Fergus AU - Hole, Barnaby AU - Udayaraj, Udaya AU - Weinman, John PY - 2023/11/23 TI - CareKnowDo?A Multichannel Digital and Telephone Support Program for People With Chronic Kidney Disease: Feasibility Randomized Controlled Trial JO - JMIR Form Res SP - e33147 VL - 7 KW - kidney disease KW - chronic KW - blood pressure KW - randomized controlled trial KW - telemedicine KW - mobile health KW - mHealth KW - self-management KW - guideline adherence KW - medication adherence KW - illness beliefs KW - medication beliefs KW - health psychology KW - preventative medicine KW - qualitative research N2 - Background: Chronic kidney disease (CKD) is a common, progressive condition. Lifestyle changes and antihypertensive medication can slow the progression to end-stage kidney disease, which requires renal replacement therapy. However, adherence to these recommendations is often low. Objective: The aim of CareKnowDo was to assess the feasibility of rolling out a digital self-management support and adherence program integrated with a patient-facing electronic health record, Patient View (PV). Methods: A 2-arm, parallel, individual-level pragmatic feasibility pilot randomized controlled trial was conducted at 2 National Health Service (NHS) sites in the United Kingdom. A total of 61 patients with CKD were randomized 1:1 into 2 groups and provided with either a new, tailored digital and telephone support program (CareKnowDo: 31/61, 51%) integrated with PV or standard care (PV alone: 30/61, 49%). Quantitative measures included clinical and psychosocial measures. The primary outcomes were feasibility based: recruitment rate, dropout, and the exploration of associations. Results: Of the 1392 patients screened in local kidney clinics, 269 (19.32%) met the basic inclusion criteria; the first 22.7% (61/269) who met the eligibility criteria were recruited to participate in the study. Of the 69 patients, 23 (38%) patients completed the final 6-month follow-up web-based survey. Reasons for the attrition were explored. A higher belief in the ability of the treatment to control CKD was associated with lower blood pressure at baseline (r=0.52; P=.005), and a higher perceived understanding of CKD at baseline was associated with lower blood pressure at follow-up (r=0.66; P<.001). Beliefs about medicines at baseline were associated with blood pressure at baseline but not at follow-up. This was true for both concerns about medicines (r=0.58; P=.001) and perceived necessity of medicines (r=0.42; P=.03). Conclusions: A tailored digital and nurse call?based program to enhance support for patients with CKD was piloted in 2 NHS sites and found to be feasible and acceptable. However, to maximize the effectiveness of the intervention (and of future trials), consideration should be given to the target audience most likely to benefit, as well as how to help them access the program as quickly and easily as possible. Trial Registration: NHS Health Research Authority, IRAS ID 184206; https://www.hra.nhs.uk/planning-and-improving -research/application-summaries/research-summaries/careknowdo-pilot-version-1/ UR - https://formative.jmir.org/2023/1/e33147 UR - http://dx.doi.org/10.2196/33147 UR - http://www.ncbi.nlm.nih.gov/pubmed/37995117 ID - info:doi/10.2196/33147 ER - TY - JOUR AU - Faraldo-Cabana, Araceli AU - Sánchez-Fructuoso, Ana AU - Pérez-Flores, Isabel AU - Beneit-Montesinos, Vicente Juan AU - Muñoz-Jiménez, Daniel AU - Peix Jiménez, Belén AU - Asensio Arredondo, Sara AU - Nuño Santana, Isabel Enriqueta AU - Santana Valeros, José María AU - Hidalgo González, Virginia AU - González García, Fernando AU - Ortuño-Soriano, Ismael PY - 2023/11/6 TI - Development of an Information Guideline for Kidney Transplant Recipients in a Clinical Trial: Protocol for a Modified Delphi Method JO - JMIR Res Protoc SP - e46961 VL - 12 KW - compliance KW - Delphi method KW - guideline KW - kidney transplantation KW - patient adherence KW - patients N2 - Background: Renal transplantation is the treatment of choice for most cases of end-stage renal disease. Recipients need to lead a healthy lifestyle to minimize the potential side effects of immunosuppressive drugs and improve transplant outcomes. There is not much evidence about the best way to increase adherence to healthy lifestyles in kidney transplant recipients, so one of the objectives set by the nursing team is to train people to acquire the necessary skills and tools to be able to take care of themselves. In this sense, the consensual development of appropriate materials may be useful and of interest. Objective: The aim of this study was to develop an information guide for adults with kidney transplants to be assessed in a subsequent clinical trial as an intervention to increase adherence to healthy habits. Methods: We used a 3-step, methodological, sequential approach: (1) training from a group of experts and item consensus; (2) review of the medical literature available; and (3) use of the Delphi technique with on-site meetings. A total of 5 nurses from the Community of Madrid Kidney Transplantation Unit in Spain were asked to participate. The patients? lifestyle factors that, according to the medical literature available and experts? opinions, have the greatest impact on the survival of the transplanted organ and the recipients themselves were all described. Results: After using the modified Delphi method to reach a consensus on the items to be included and the information needed in each, an information guide for adult kidney transplant patients was developed. This guide facilitates the structuring of health care, information, and recommendations necessary for effective self-care for each person. The result is considered to be an easy-to-understand tool, useful for transplant doctors and nurses, in simple language, with information based on the latest scientific-medical evidence published to date, aspects of which will be evaluated in a clinical trial designed for this purpose. Conclusions: Currently, this guide is the main intervention variable of a clinical trial (registered on ClinicalTrials.gov; NCT05715580) aimed at improving compliance with healthy habits in kidney transplant recipients in the Community of Madrid, Spain. The method used in its development has been useful and agile, and the result is a guide that can be easily updated periodically following the same procedure. International Registered Report Identifier (IRRID): DERR1-10.2196/46961 UR - https://www.researchprotocols.org/2023/1/e46961 UR - http://dx.doi.org/10.2196/46961 UR - http://www.ncbi.nlm.nih.gov/pubmed/37930773 ID - info:doi/10.2196/46961 ER - TY - JOUR AU - Chen, Nai-Jung AU - Huang, Chiu-Mieh AU - Fan, Ching-Chih AU - Lu, Li-Ting AU - Lin, Fen-He AU - Liao, Jung-Yu AU - Guo, Jong-Long PY - 2023/9/19 TI - User Evaluation of a Chat-Based Instant Messaging Support Health Education Program for Patients With Chronic Kidney Disease: Preliminary Findings of a Formative Study JO - JMIR Form Res SP - e45484 VL - 7 KW - chronic kidney disease KW - chatbot KW - health education KW - push notification KW - users? evaluation N2 - Background: Artificial intelligence?driven chatbots are increasingly being used in health care, but few chat-based instant messaging support health education programs are designed for patients with chronic kidney disease (CKD) to evaluate their effectiveness. In addition, limited research exists on the usage of chat-based programs among patients with CKD, particularly those that integrate a chatbot aimed at enhancing the communication ability and disease-specific knowledge of patients. Objective: The objective of this formative study is to gather the data necessary to develop an intervention program of chat-based instant messaging support health education for patients with CKD. Participants? user experiences will form the basis for program design improvements. Methods: Data were collected from April to November 2020 using a structured questionnaire. A pre-post design was used, and a total of 60 patients consented to join the 3-month program. Among them, 55 successfully completed the study measurements. The System Usability Scale was used for participant evaluations of the usability of the chat-based program. Results: Paired t tests revealed significant differences before and after intervention for communicative literacy (t54=3.99; P<.001) and CKD-specific disease knowledge (t54=7.54; P<.001). Within disease knowledge, significant differences were observed in the aspects of CKD basic knowledge (t54=3.46; P=.001), lifestyle (t54=3.83; P=.001), dietary intake (t54=5.51; P<.001), and medication (t54=4.17; P=.001). However, no significant difference was found in the aspect of disease prevention. Subgroup analysis revealed that while the findings among male participants were similar to those of the main sample, this was not the case among female participants. Conclusions: The findings reveal that a chat-based instant messaging support health education program may be effective for middle-aged and older patients with CKD. The use of a chat-based program with multiple promoting approaches is promising, and users? evaluation is satisfactory. Trial Registration: ClinicalTrials.gov NCT05665517; https://clinicaltrials.gov/study/NCT05665517 UR - https://formative.jmir.org/2023/1/e45484 UR - http://dx.doi.org/10.2196/45484 UR - http://www.ncbi.nlm.nih.gov/pubmed/37725429 ID - info:doi/10.2196/45484 ER - TY - JOUR AU - Chamarthi, Gajapathiraju AU - Orozco, Tatiana AU - Shell, Popy AU - Fu, Devin AU - Hale-Gallardo, Jennifer AU - Jia, Huanguang AU - Shukla, M. Ashutosh PY - 2023/7/24 TI - Electronic Phenotype for Advanced Chronic Kidney Disease in a Veteran Health Care System Clinical Database: Systems-Based Strategy for Model Development and Evaluation JO - Interact J Med Res SP - e43384 VL - 12 KW - advanced chronic kidney disease KW - EHR phenotype KW - Veteran Health System KW - CKD cohort KW - kidney disease KW - chronic KW - clinical KW - database KW - data KW - diagnosis KW - risk KW - disease N2 - Background: Identifying advanced (stages 4 and 5) chronic kidney disease (CKD) cohorts in clinical databases is complicated and often unreliable. Accurately identifying these patients can allow targeting this population for their specialized clinical and research needs. Objective: This study was conducted as a system-based strategy to identify all prevalent Veterans with advanced CKD for subsequent enrollment in a clinical trial. We aimed to examine the prevalence and accuracy of conventionally used diagnosis codes and estimated glomerular filtration rate (eGFR)-based phenotypes for advanced CKD in an electronic health record (EHR) database. We sought to develop a pragmatic EHR phenotype capable of improving the real-time identification of advanced CKD cohorts in a regional Veterans health care system. Methods: Using the Veterans Affairs Informatics and Computing Infrastructure services, we extracted the source cohort of Veterans with advanced CKD based on a combination of the latest eGFR value ?30 ml·min?1·1.73 m?2 or existing International Classification of Diseases (ICD)-10 diagnosis codes for advanced CKD (N18.4 and N18.5) in the last 12 months. We estimated the prevalence of advanced CKD using various prior published EHR phenotypes (ie, advanced CKD diagnosis codes, using the latest single eGFR <30 ml·min?1·1.73 m?2, utilizing two eGFR values) and our operational EHR phenotypes of a high-, intermediate-, and low-risk advanced CKD cohort. We evaluated the accuracy of these phenotypes by examining the likelihood of a sustained reduction of eGFR <30 ml·min?1·1.73 m?2 over a 6-month follow-up period. Results: Of the 133,756 active Veteran enrollees at North Florida/South Georgia Veterans Health System (NF/SG VHS), we identified a source cohort of 1759 Veterans with advanced nondialysis CKD. Among these, 1102 (62.9%) Veterans had diagnosis codes for advanced CKD; 1391(79.1%) had the index eGFR <30 ml·min?1·1.73 m?2; and 928 (52.7%), 480 (27.2%), and 315 (17.9%) Veterans had high-, intermediate-, and low-risk advanced CKD, respectively. The prevalence of advanced CKD among Veterans at NF/SG VHS varied between 1% and 1.5% depending on the EHR phenotype. At the 6-month follow-up, the probability of Veterans remaining in the advanced CKD stage was 65.3% in the group defined by the ICD-10 codes and 90% in the groups defined by eGFR values. Based on our phenotype, 94.2% of high-risk, 71% of intermediate-risk, and 16.1% of low-risk groups remained in the advanced CKD category. Conclusions: While the prevalence of advanced CKD has limited variation between different EHR phenotypes, the accuracy can be improved by utilizing two eGFR values in a stratified manner. We report the development of a pragmatic EHR-based model to identify advanced CKD within a regional Veterans health care system in real time with a tiered approach that allows targeting the needs of the groups at risk of progression to end-stage kidney disease. UR - https://www.i-jmr.org/2023/1/e43384 UR - http://dx.doi.org/10.2196/43384 UR - http://www.ncbi.nlm.nih.gov/pubmed/37486757 ID - info:doi/10.2196/43384 ER - TY - JOUR AU - Starr, C. Michelle AU - Wallace, Samantha AU - Moore, Courtney AU - Cockrum, Brandon AU - Hawryluk, Bridget AU - Carroll, Aaron AU - Bennett, William PY - 2023/7/10 TI - Development of a Family-Centered Communication Tool for Kidney Health in Premature Infants: Qualitative Focus Group Study Using Human-Centered Design Methodology JO - J Particip Med SP - e45316 VL - 15 KW - qualitative research KW - patient-reported outcomes KW - neonates KW - chronic kidney disease KW - human-centered design KW - acute kidney injury KW - kidney health N2 - Background: Premature infants are at increased risk of kidney-related complications, including acute kidney injury (AKI) and chronic kidney disease (CKD). The risk of CKD in prematurely born infants is underrecognized by health care teams and caregivers. Understanding how to communicate the risk of CKD to caregivers is essential for longitudinal clinical follow-up and adherence. Objective: This study aimed to determine family caregiver attitudes toward kidney health and risk communication during a neonatal intensive care admission. We also sought to understand caregiver preferences for the communication of information surrounding the risk of CKD in premature infants. Methods: We augmented standard qualitative group sessions with human-centered design methods to assess parent preferences and clinician perspectives. Caregivers recruited had a prematurely born child who spent time in the neonatal intensive care unit at Riley Hospital for Children in Indianapolis, Indiana, and experienced AKI or another kidney complication, which put them at risk for future CKD. We used a variety of specific design methods in these sessions, including card sorting, projective methods, experience mapping, and constructive methods. Results: A total of 7 clinicians and 8 caregivers participated in 3 group sessions. Caregivers and clinicians readily acknowledged barriers to and drivers of long-term kidney monitoring as well as opportunities for communication of the risk of long-term kidney disease. Caregivers? primary concerns were for both the type and depth of information conveyed as well as the time at which it was communicated. Participants emphasized the importance of collaboration between the hospital care team and the primary care provider. Participant input was synthesized into several prototype concepts and, ultimately, into a rough prototype of a website and an informational flyer. Conclusions: Caregivers of premature infants are open to communication about kidney health during their neonatal admission. The next phase of this work will translate caregivers? preferences into family-centered communication tools and test their efficacy in the neonatal intensive care unit. UR - https://jopm.jmir.org/2023/1/e45316 UR - http://dx.doi.org/10.2196/45316 UR - http://www.ncbi.nlm.nih.gov/pubmed/37428553 ID - info:doi/10.2196/45316 ER - TY - JOUR AU - Liu, Wei AU - Yu, Xiaojuan AU - Wang, Jiangyuan AU - Zhou, Tianmeng AU - Yu, Ting AU - Chen, Xuyong AU - Xie, Shasha AU - Han, Fuman AU - Wang, Zi PY - 2023/6/1 TI - Improving Kidney Outcomes in Patients With Nondiabetic Chronic Kidney Disease Through an Artificial Intelligence?Based Health Coaching Mobile App: Retrospective Cohort Study JO - JMIR Mhealth Uhealth SP - e45531 VL - 11 KW - chronic kidney disease KW - self-management KW - mobile apps KW - end-stage kidney disease KW - eHealth intervention KW - kidney KW - efficacy KW - eHealth care KW - dialysis KW - deep-learning KW - artificial intelligence KW - patient care N2 - Background: Chronic kidney disease (CKD) is a global health burden. However, the efficacy of different modes of eHealth care in facilitating self-management for patients with CKD is unclear. Objective: The aim of this study was to evaluate the effectiveness of a mobile app?based intelligent care system in improving the kidney outcomes of patients with CKD. Methods: Our study was a retrospective analysis based on the KidneyOnline intelligent system developed in China. Patients with CKD but not dependent on dialysis who registered on the KidneyOnline app between January 2017 and January 2021 were screened. Patients in the the KidneyOnline intelligent system group and those in the conventional care group were 1:1 matched according to their baseline characteristics. The intervention group received center-based follow-up combined with the KidneyOnline intelligent patient care system, which was a nurse-led, patient-oriented collaborative management system. Health-related data uploaded by the patients were integrated using deep learning optical character recognition (OCR). Artificial intelligence (AI)?generated personalized recipes, lifestyle intervention suggestions, early warnings, real-time questions and answers, and personalized follow-up plans were also provided. Patients in the conventional group could get professional suggestions from the nephrologists through regular clinical visits, but they did not have access to the service provided by AI and the health coach team. Patients were followed for at least 3 months after recruitment or until death or start of renal replacement therapy. Results: A total of 2060 eligible patients who registered on the KidneyOnline app from 2017 to 2021 were enrolled for the analysis. Of those, 902 (43.8%) patients were assessed for survival analysis after propensity score matching, with 451(50%) patients in the KidneyOnline intelligent patient care system group and 451(50%) patients in the conventional care group. After a mean follow-up period of 15.8 (SD 9.5) months, the primary composite kidney outcome occurred in 28 (6%) participants in the KidneyOnline intelligent patient care system group and 32 (7%) in the conventional care group, with a hazard ratio of 0.391 (95% CI 0.231-0.660; P<.001). Subgroup survival analysis demonstrated that the KidneyOnline care system significantly reduced the risk of composite kidney outcome, irrespective of age, sex, baseline estimated glomerular filtration rate (eGFR), and proteinuria. In addition, the mean arterial pressure (MAP) significantly decreased from 88.9 (SD 10.5) mmHg at baseline to 85.6 (SD 7.9) mmHg at 6 months (P<.001) in the KidneyOnline intelligent patient care system group and from 89.3 (SD 11.1) mmHg to 87.5 (SD 8.2) mmHg (P=.002) in the conventional CKD care group. Conclusions: The utilization of the KidneyOnline intelligent care system was associated with reduced risk of unfavorable kidney outcomes in nondiabetic patients with CKD. UR - https://mhealth.jmir.org/2023/1/e45531 UR - http://dx.doi.org/10.2196/45531 UR - http://www.ncbi.nlm.nih.gov/pubmed/37261895 ID - info:doi/10.2196/45531 ER - TY - JOUR AU - Geerts, Jody AU - Pieterse, Marcel AU - Laverman, Goos AU - Waanders, Femke AU - Oosterom, Nicole AU - Slegten, Jacqueline AU - Salemink, Elske AU - Bode, Christina PY - 2023/5/29 TI - Cognitive Bias Modification Training Targeting Fatigue in Patients With Kidney Disease: Usability Study JO - JMIR Form Res SP - e43636 VL - 7 KW - cognitive bias KW - patient perspective KW - qualitative study KW - nephrology KW - fatigue KW - vitality KW - acceptability KW - applicability KW - usability KW - design N2 - Background: Fatigue is an important symptom for many patients, including patients with kidney disease. Cognitive biases, such as attentional bias and self-identity bias, are thought to influence fatigue. Cognitive bias modification (CBM) training is a promising technique to counter fatigue. Objective: We aimed to evaluate a CBM training among patients with kidney disease and health care professionals (HCPs) and assess acceptability and applicability in the clinical setting using an iterative design process to evaluate expectations and experiences with the training. Methods: This was a longitudinal, qualitative, and multiple stakeholder?perspective usability study in which we interviewed end users and HCPs during the prototyping phase and after the end of training. We conducted semistructured interviews with 29 patients and 16 HCPs. The interviews were transcribed and analyzed thematically. Next to a general evaluation of the training, the acceptability of the training was evaluated using the Theoretical Framework of Acceptability, and applicability was assessed by evaluating obstacles and solutions for implementation in the kidney care setting. Results: Generally, participants were positive about the training and its applicability. The biggest negatives were doubts about effectiveness and annoyance about the repetitive character of CBM. Acceptability was judged with a mixed evaluation, with a negative evaluation of perceived effectiveness; mixed results for burden, intervention coherence, and self-efficacy; and positive results for affective attitude, ethicality, and opportunity costs. Barriers for applicability were patients? varying computer skills, subjectivity of fatigue, and integration with regular treatment (eg, the role of HCPs). Possible solutions included assigning representatives among nurses, offering training on an app, and providing assistance via a help desk. The iterative design process, including repeated waves of testing user expectations and experiences, yielded complementary data. Conclusions: To the best of our knowledge, this study is the first to introduce a CBM training targeting fatigue. Furthermore, this study provides one of the first user evaluations of a CBM training, both among patients with kidney disease and their care providers. Overall, the training was evaluated positively, although acceptability showed mixed results. Applicability was positive although barriers were identified. The proposed solutions require further testing, preferably following the same frameworks, as the iteration in this study contributed positively to the quality of the training. Therefore, future research should follow the same frameworks and consider stakeholders and end users in eHealth intervention design. UR - https://formative.jmir.org/2023/1/e43636 UR - http://dx.doi.org/10.2196/43636 UR - http://www.ncbi.nlm.nih.gov/pubmed/37247217 ID - info:doi/10.2196/43636 ER - TY - JOUR AU - May, P. Heather AU - Griffin, M. Joan AU - Herges, R. Joseph AU - Kashani, B. Kianoush AU - Kattah, G. Andrea AU - Mara, C. Kristin AU - McCoy, G. Rozalina AU - Rule, D. Andrew AU - Tinaglia, G. Angeliki AU - Barreto, F. Erin PY - 2023/5/22 TI - Comprehensive Acute Kidney Injury Survivor Care: Protocol for the Randomized Acute Kidney Injury in Care Transitions Pilot Trial JO - JMIR Res Protoc SP - e48109 VL - 12 KW - acute kidney injury KW - acute renal failure KW - care transitions KW - chronic kidney disease KW - nephrologists KW - randomized controlled trials N2 - Background: Innovative care models are needed to address gaps in kidney care follow-up among acute kidney injury (AKI) survivors. We developed the multidisciplinary AKI in Care Transitions (ACT) program, which embeds post-AKI care in patients? primary care clinic. Objective: The objective of this randomized pilot trial is to test the feasibility and acceptability of the ACT program and study protocol, including recruitment and retention, procedures, and outcome measures. Methods: The study will be conducted at Mayo Clinic in Rochester, Minnesota, a tertiary care center with a local primary care practice. Individuals who are included have stage 3 AKI during their hospitalization, do not require dialysis at discharge, have a local primary care provider, and are discharged to their home. Patients unable or unwilling to provide informed consent and recipients of any transplant within 100 days of enrollment are excluded. Consented patients are randomized to receive the intervention (ie, ACT program) or usual care. The ACT program intervention includes predischarge kidney health education from nurses and coordinated postdischarge laboratory monitoring (serum creatinine and urine protein assessment) and follow-up with a primary care provider and pharmacist within 14 days. The usual care group receives no specific study-related intervention, and any aspects of AKI care are at the direction of the treating team. This study will examine the feasibility of the ACT program, including recruitment, randomization and retention in a trial setting, and intervention fidelity. The feasibility and acceptability of participating in the ACT program will also be examined in qualitative interviews with patients and staff and through surveys. Qualitative interviews will be deductively and inductively coded and themes compared across data types. Observations of clinical encounters will be examined for discussion and care plans related to kidney health. Descriptive analyses will summarize quantitative measures of the feasibility and acceptability of ACT. Participants? knowledge about kidney health, quality of life, and process outcomes (eg, type and timing of laboratory assessments) will be described for both groups. Clinical outcomes (eg, unplanned rehospitalization) up to 12 months will be compared with Cox proportional hazards models. Results: This study received funding from the Agency for Health Care Research and Quality on April 21, 2021, and was approved by the Institutional Review Board on December 14, 2021. As of March 14, 2023, seventeen participants each have been enrolled in the intervention and usual care groups. Conclusions: Feasible and generalizable AKI survivor care delivery models are needed to improve care processes and health outcomes. This pilot trial will test the ACT program, which uses a multidisciplinary model focused on primary care to address this gap. Trial Registration: ClinicalTrials.gov NCT05184894; https://www.clinicaltrials.gov/ct2/show/NCT05184894 International Registered Report Identifier (IRRID): DERR1-10.2196/48109 UR - https://www.researchprotocols.org/2023/1/e48109 UR - http://dx.doi.org/10.2196/48109 UR - http://www.ncbi.nlm.nih.gov/pubmed/37213187 ID - info:doi/10.2196/48109 ER - TY - JOUR AU - Yang, Ju-Yeh AU - Shu, Kai-Hsiang AU - Peng, Yu-Sen AU - Hsu, Shih-Ping AU - Chiu, Yen-Ling AU - Pai, Mei-Fen AU - Wu, Hon-Yen AU - Tsai, Wan-Chuan AU - Tung, Kuei-Ting AU - Kuo, N. Raymond PY - 2023/5/3 TI - Physician Compliance With a Computerized Clinical Decision Support System for Anemia Management of Patients With End-stage Kidney Disease on Hemodialysis: Retrospective Electronic Health Record Observational Study JO - JMIR Form Res SP - e44373 VL - 7 KW - clinical decision support system KW - erythropoietin-stimulating agent KW - end-stage kidney disease KW - hemodialysis KW - physician compliance KW - kidney disease KW - clinical decision support KW - electronic health records KW - decision support KW - anemia management KW - patient outcome N2 - Background: Previous studies on clinical decision support systems (CDSSs) for the management of renal anemia in patients with end-stage kidney disease undergoing hemodialysis have previously focused solely on the effects of the CDSS. However, the role of physician compliance in the efficacy of the CDSS remains ill-defined. Objective: We aimed to investigate whether physician compliance was an intermediate variable between the CDSS and the management outcomes of renal anemia. Methods: We extracted the electronic health records of patients with end-stage kidney disease on hemodialysis at the Far Eastern Memorial Hospital Hemodialysis Center (FEMHHC) from 2016 to 2020. FEMHHC implemented a rule-based CDSS for the management of renal anemia in 2019. We compared the clinical outcomes of renal anemia between the pre- and post-CDSS periods using random intercept models. Hemoglobin levels of 10 to 12 g/dL were defined as the on-target range. Physician compliance was defined as the concordance of adjustments of the erythropoietin-stimulating agent (ESA) between the CDSS recommendations and the actual physician prescriptions. Results: We included 717 eligible patients on hemodialysis (mean age 62.9, SD 11.6 years; male n=430, 59.9%) with a total of 36,091 hemoglobin measurements (average hemoglobin and on-target rate were 11.1, SD 1.4, g/dL and 59.9%, respectively). The on-target rate decreased from 61.3% (pre-CDSS) to 56.2% (post-CDSS) owing to a high hemoglobin percentage of >12 g/dL (pre: 21.5%; post: 29%). The failure rate (hemoglobin <10 g/dL) decreased from 17.2% (pre-CDSS) to 14.8% (post-CDSS). The average weekly ESA use of 5848 (SD 4211) units per week did not differ between phases. The overall concordance between CDSS recommendations and physician prescriptions was 62.3%. The CDSS concordance increased from 56.2% to 78.6%. In the adjusted random intercept model, the post-CDSS phase showed increased hemoglobin by 0.17 (95% CI 0.14-0.21) g/dL, weekly ESA by 264 (95% CI 158-371) units per week, and 3.4-fold (95% CI 3.1-3.6) increased concordance rate. However, the on-target rate (29%; odds ratio 0.71, 95% CI 0.66-0.75) and failure rate (16%; odds ratio 0.84, 95% CI 0.76-0.92) were reduced. After additional adjustments for concordance in the full models, increased hemoglobin and decreased on-target rate tended toward attenuation (from 0.17 to 0.13 g/dL and 0.71 to 0.73 g/dL, respectively). Increased ESA and decreased failure rate were completely mediated by physician compliance (from 264 to 50 units and 0.84 to 0.97, respectively). Conclusions: Our results confirmed that physician compliance was a complete intermediate factor accounting for the efficacy of the CDSS. The CDSS reduced failure rates of anemia management through physician compliance. Our study highlights the importance of optimizing physician compliance in the design and implementation of CDSSs to improve patient outcomes. UR - https://formative.jmir.org/2023/1/e44373 UR - http://dx.doi.org/10.2196/44373 UR - http://www.ncbi.nlm.nih.gov/pubmed/37133912 ID - info:doi/10.2196/44373 ER - TY - JOUR AU - Horton, Anna AU - Loban, Katya AU - Nugus, Peter AU - Fortin, Marie-Chantal AU - Gunaratnam, Lakshman AU - Knoll, Greg AU - Mucsi, Istvan AU - Chaudhury, Prosanto AU - Landsberg, David AU - Paquet, Michel AU - Cantarovich, Marcelo AU - Sandal, Shaifali PY - 2023/3/7 TI - Health System?Level Barriers to Living Donor Kidney Transplantation: Protocol for a Comparative Case Study Analysis JO - JMIR Res Protoc SP - e44172 VL - 12 KW - transplantation KW - living donor kidney transplantation KW - health systems KW - barriers KW - resource based theory KW - complex adaptive systems N2 - Background: Living donor kidney transplantation (LDKT) is the best treatment option for patients with kidney failure and offers significant medical and economic advantages for both patients and health systems. Despite this, rates of LDKT in Canada have stagnated and vary significantly across Canadian provinces, the reasons for which are not well understood. Our prior work has suggested that system-level factors may be contributing to these differences. Identifying these factors can help inform system-level interventions to increase LDKT. Objective: Our objective is to generate a systemic interpretation of LDKT delivery across provincial health systems with variable performance. We aim to identify the attributes and processes that facilitate the delivery of LDKT to patients, and those that create barriers and compare these across systems with variable performance. These objectives are contextualized within our broader goal of increasing rates of LDKT in Canada, particularly in lower-performing provinces. Methods: This research takes the form of a qualitative comparative case study analysis of 3 provincial health systems in Canada that have high, moderate, and low rates of LDKT performance (the percentage of LDKT to all kidney transplantations performed). Our approach is underpinned by an understanding of health systems as complex adaptive systems that are multilevel and interconnected, and involve nonlinear interactions between people and organizations, operating within a loosely bounded network. Data collection will comprise semistructured interviews, document reviews, and focus groups. Individual case studies will be conducted and analyzed using inductive thematic analysis. Following this, our comparative analysis will operationalize resource-based theory to compare case study data and generate explanations for our research question. Results: This project was funded from 2020 to 2023. Individual case studies were carried out between November 2020 and August 2022. The comparative case analysis will begin in December 2022 and is expected to conclude in April 2023. Submission of the publication is projected for June 2023. Conclusions: By investigating health systems as complex adaptive systems and making comparisons across provinces, this study will identify how health systems can improve the delivery of LDKT to patients with kidney failure. Our resource-based theory framework will provide a granular analysis of the attributes and processes that facilitate or create barriers to LDKT delivery across multiple organizations and levels of practice. Our findings will have practice and policy implications and help inform transferrable competencies and system-level interventions conducive to increasing LDKT. International Registered Report Identifier (IRRID): DERR1-10.2196/44172 UR - https://www.researchprotocols.org/2023/1/e44172 UR - http://dx.doi.org/10.2196/44172 UR - http://www.ncbi.nlm.nih.gov/pubmed/36881454 ID - info:doi/10.2196/44172 ER - TY - JOUR AU - Luo, Xiao-Qin AU - Kang, Yi-Xin AU - Duan, Shao-Bin AU - Yan, Ping AU - Song, Guo-Bao AU - Zhang, Ning-Ya AU - Yang, Shi-Kun AU - Li, Jing-Xin AU - Zhang, Hui PY - 2023/1/5 TI - Machine Learning?Based Prediction of Acute Kidney Injury Following Pediatric Cardiac Surgery: Model Development and Validation Study JO - J Med Internet Res SP - e41142 VL - 25 KW - cardiac surgery KW - acute kidney injury KW - pediatric KW - machine learning N2 - Background: Cardiac surgery?associated acute kidney injury (CSA-AKI) is a major complication following pediatric cardiac surgery, which is associated with increased morbidity and mortality. The early prediction of CSA-AKI before and immediately after surgery could significantly improve the implementation of preventive and therapeutic strategies during the perioperative periods. However, there is limited clinical information on how to identify pediatric patients at high risk of CSA-AKI. Objective: The study aims to develop and validate machine learning models to predict the development of CSA-AKI in the pediatric population. Methods: This retrospective cohort study enrolled patients aged 1 month to 18 years who underwent cardiac surgery with cardiopulmonary bypass at 3 medical centers of Central South University in China. CSA-AKI was defined according to the 2012 Kidney Disease: Improving Global Outcomes criteria. Feature selection was applied separately to 2 data sets: the preoperative data set and the combined preoperative and intraoperative data set. Multiple machine learning algorithms were tested, including K-nearest neighbor, naive Bayes, support vector machines, random forest, extreme gradient boosting (XGBoost), and neural networks. The best performing model was identified in cross-validation by using the area under the receiver operating characteristic curve (AUROC). Model interpretations were generated using the Shapley additive explanations (SHAP) method. Results: A total of 3278 patients from one of the centers were used for model derivation, while 585 patients from another 2 centers served as the external validation cohort. CSA-AKI occurred in 564 (17.2%) patients in the derivation cohort and 51 (8.7%) patients in the external validation cohort. Among the considered machine learning models, the XGBoost models achieved the best predictive performance in cross-validation. The AUROC of the XGBoost model using only the preoperative variables was 0.890 (95% CI 0.876-0.906) in the derivation cohort and 0.857 (95% CI 0.800-0.903) in the external validation cohort. When the intraoperative variables were included, the AUROC increased to 0.912 (95% CI 0.899-0.924) and 0.889 (95% CI 0.844-0.920) in the 2 cohorts, respectively. The SHAP method revealed that baseline serum creatinine level, perfusion time, body length, operation time, and intraoperative blood loss were the top 5 predictors of CSA-AKI. Conclusions: The interpretable XGBoost models provide practical tools for the early prediction of CSA-AKI, which are valuable for risk stratification and perioperative management of pediatric patients undergoing cardiac surgery. UR - https://www.jmir.org/2023/1/e41142 UR - http://dx.doi.org/10.2196/41142 UR - http://www.ncbi.nlm.nih.gov/pubmed/36603200 ID - info:doi/10.2196/41142 ER - TY - JOUR AU - Fleming, N. James AU - Cober, Timothy AU - Hickey, Janelle AU - Stach, Leslie AU - Kawano, Allison AU - Szczepanik, Amanda AU - Watson, Alicia AU - Imamura, Yuka AU - Weems, Juston AU - West-Thielke, Patricia PY - 2022/12/14 TI - Clinical Utility of the OmniGraf Biomarker Panel in the Care of Kidney Transplant Recipients (CLARITY): Protocol for a Prospective, Multisite Observational Study JO - JMIR Res Protoc SP - e41020 VL - 11 IS - 12 KW - kidney transplant KW - biomarker KW - adverse event KW - adverse drug event KW - renal function KW - eGFR KW - clinical trial KW - allograft KW - nephrology KW - patient outcome KW - renal KW - kidney KW - transplant KW - observational study KW - medication monitoring KW - quality of life KW - chronic condition KW - medication management N2 - Background: Death with a functioning allograft has become the leading category of graft loss in kidney transplant recipients at all time points. Previous analyses have demonstrated that causes of death in kidney transplant recipients are predominated by comorbidities strongly associated with immunosuppressant medications. Adverse drug events (ADEs) have been strongly associated with nonadherence, health care utilization, and graft loss; clinicians face a difficult decision on whether making immunosuppressant adjustments in the face of ADEs will improve symptomology or simply increase the risk of acute rejection. Clinicians also face a treatment quandary in 50% of kidney transplant recipients with stage 3 or worse chronic kidney disease at 1 year post transplantation, as progressive decline in renal function has been strongly associated with inferior allograft survival. Objective: The primary objective of the CLinical Utility of the omnigrAf biomarkeR Panel In The Care of kidneY Transplant Recipients (CLARITY) trial is to evaluate change in renal function over time in kidney transplant recipients who are undergoing OmniGraf monitoring in conjunction with monitoring of their medication-related symptom burden (MRSB). A secondary objective of this study is to identify the impact of OmniGraf use in conjunction with patient-reported MRSB as part of clinical care on patients? self-efficacy and quality of life. Methods: CLARITY is a 3-year prospective, multisite, observational study of 2000 participants with a matched control, measuring the impact of real-time patients? MRSB and the OmniGraf biomarker panel on change in renal function over time. Secondary outcome measures include the Patient-Reported Outcomes Measurement Information System (PROMIS) Self-Efficacy for Managing Chronic Conditions?Managing Medications and Treatment?Short Form 4a; the PROMIS-29 Profile (version 2.1); the PROMIS Depression Scale, hospitalizations?subcategorized for hospitalizations owing to infections; treated rejections, MRSB, and proportion of participants with overall graft survival at year 3 post transplantation; graft loss or death during the 3-year study follow-up period; and change in provider satisfaction. Results: The primary outcome measure of the study will be a comparison of the slope change in estimated glomerular filtration rate from baseline to the end of follow-up between study participants and a matched control group. Secondary outcome measures include changes over time in PROMIS Self-Efficacy for Managing Chronic Conditions?Managing Medications and Treatment?Short Form 4a, the PROMIS-29 Profile (version 2.1), and PROMIS Depression Scale in the study group, as well as a comparison of hospitalizations and causes, rejections, and graft and patient survival compared between participants and a matched cohort. The anticipated first enrollment in the study is October 2022 with data analysis and publication expected in October 2027. Conclusions: Through this report, we describe the study design, methods, and outcome measures that will be utilized in the ongoing CLARITY trial. Trial Registration: ClinicalTrials.gov NCT05482100; https://clinicaltrials.gov/ct2/show/NCT05482100 International Registered Report Identifier (IRRID): PRR1-10.2196/41020 UR - https://www.researchprotocols.org/2022/12/e41020 UR - http://dx.doi.org/10.2196/41020 UR - http://www.ncbi.nlm.nih.gov/pubmed/36515980 ID - info:doi/10.2196/41020 ER - TY - JOUR AU - Vilasi, Antonio AU - Panuccio, Antonio Vincenzo AU - Morante, Salvatore AU - Villa, Antonino AU - Versace, Carmela Maria AU - Mezzatesta, Sabrina AU - Mercuri, Sergio AU - Inguanta, Rosalinda AU - Aiello, Giuseppe AU - Cutrupi, Demetrio AU - Puglisi, Rossella AU - Capria, Salvatore AU - Li Vigni, Maurizio AU - Tripepi, Giovanni AU - Torino, Claudia PY - 2022/11/15 TI - Monitoring Risk Factors and Improving Adherence to Therapy in Patients With Chronic Kidney Disease (Smit-CKD Project): Pilot Observational Study JO - JMIR Bioinform Biotech SP - e36766 VL - 3 IS - 1 KW - SMIT-CKD KW - mHealth KW - eHealth KW - CKD KW - therapy adherence KW - risk factor KW - kidney KW - adherence KW - integrated system KW - health app KW - monitoring KW - cardiology KW - cardiac KW - renal KW - chronic kidney disease KW - cardiovascular KW - mobile health KW - mobile app N2 - Background: Chronic kidney disease is a major public health issue, with about 13% of the general adult population and 30% of the elderly affected. Patients in the last stage of this disease have an almost uniquely high risk of death and cardiovascular events, with reduced adherence to therapy representing an additional risk factor for cardiovascular morbidity and mortality. Considering the increased penetration of mobile phones, a mobile app could educate patients to autonomously monitor cardiorenal risk factors. Objective: With this background in mind, we developed an integrated system of a server and app with the aim of improving self-monitoring of cardiovascular and renal risk factors and adherence to therapy. Methods: The software infrastructure for both the Smit-CKD server and Smit-CKD app was developed using standard web-oriented development methodologies preferring open source tools when available. To make the Smit-CKD app suitable for Android and iOS, platforms that allow the development of a multiplatform app starting from a single source code were used. The integrated system was field tested with the help of 22 participants. User satisfaction and adherence to therapy were measured by questionnaires specifically designed for this study; regular use of the app was measured using the daily reports available on the platform. Results: The Smit-CKD app allows the monitoring of cardiorenal risk factors, such as blood pressure, weight, and blood glucose. Collected data are transmitted in real time to the referring general practitioner. In addition, special reminders improve adherence to the medication regimen. Via the Smit-CKD server, general practitioners can monitor the clinical status of their patients and their adherence to therapy. During the test phase, 73% (16/22) of subjects entered all the required data regularly and sent feedback on drug intake. After 6 months of use, the percentage of regular intake of medications rose from 64% (14/22) to 82% (18/22). Analysis of the evaluation questionnaires showed that both the app and server components were well accepted by the users. Conclusions: Our study demonstrated that a simple mobile app, created to self-monitor modifiable cardiorenal risk factors and adherence to therapy, is well tolerated by patients affected by chronic kidney disease. Further studies are required to clarify if the use of this integrated system will have long-term effects on therapy adherence and if self-monitoring of risk factors will improve clinical outcomes in this population. UR - https://bioinform.jmir.org/2022/1/e36766 UR - http://dx.doi.org/10.2196/36766 UR - http://www.ncbi.nlm.nih.gov/pubmed/ ID - info:doi/10.2196/36766 ER - TY - JOUR AU - Tuot, S. Delphine AU - Crowley, T. Susan AU - Katz, A. Lois AU - Leung, Joseph AU - Alcantara-Cadillo, K. Delly AU - Ruser, Christopher AU - Talbot-Montgomery, Elizabeth AU - Vassalotti, A. Joseph PY - 2022/9/28 TI - Usability Testing of the Kidney Score Platform to Enhance Communication About Kidney Disease in Primary Care Settings: Qualitative Think-Aloud Study JO - JMIR Form Res SP - e40001 VL - 6 IS - 9 KW - chronic kidney disease KW - CKD KW - awareness KW - usability KW - kidney KW - renal KW - think aloud KW - self-management KW - patient education KW - health education N2 - Background: Patient awareness of chronic kidney disease (CKD) is low in part due to suboptimal testing for CKD among those at risk and lack of discussions about kidney disease between patients and clinicians. To bridge these gaps, the National Kidney Foundation developed the Kidney Score Platform, which is a web-based series of tools that includes resources for health care professionals as well as an interactive, dynamic patient-facing component that includes a brief questionnaire about risk factors for kidney disease, individualized assessment of risk for developing CKD, and self-management tools to manage one?s kidney disease. Objective: The aim of this study is to perform usability testing of the patient component of the Kidney Score platform among veterans with and at risk for kidney disease and among clinicians working as primary care providers in Veterans Affairs administration. Methods: Think-aloud exercises were conducted, during which participants (veterans and clinicians) engaged with the platform while verbalizing their thoughts and making their perceptions, reasonings, and decision points explicit. A usability facilitator observed participants? behaviors and probed selectively to clarify their comprehension of the tool?s instructions, content, and overall functionality. Thematic analysis on the audio-recording transcripts was performed, focusing on positive attributes, negative comments, and areas that required facilitator involvement. Results: Veterans (N=18) were 78% (14/18) male with a mean age of 58.1 years. Two-thirds (12/18) were of non-White race/ethnicity, 28% (5/18) had laboratory evidence of CKD without a formal diagnosis, and 50% (9/18) carried a diagnosis of hypertension or diabetes. Clinicians (N=19) were 29% (5/17) male, 30% (5/17) of non-White race/ethnicity, and had a mean of 17 (range 4-32) years of experience. Veterans and clinicians easily navigated the online tool and appreciated the personalized results page as well as the inclusion of infographics to deliver key educational messages. Three major themes related to content and communication about risk for CKD emerged from the think-aloud exercises: (1) tension between lay and medical terminology when discussing kidney disease and diagnostic tests, (2) importance of linking general information to concrete self-management actions, and (3) usefulness of the tool as an adjunct to the office visit to prepare for patient-clinician communication. Importantly, these themes were consistent among interviews involving both veterans and clinicians. Conclusions: Veterans and clinicians both thought that the Kidney Score Platform would successfully promote communication and discussion about kidney disease in primary care settings. Tension between using medical terminology that is used regularly by clinicians versus lay terminology to promote CKD awareness was a key challenge, and knowledge of this can inform the development of future CKD educational materials. UR - https://formative.jmir.org/2022/9/e40001 UR - http://dx.doi.org/10.2196/40001 UR - http://www.ncbi.nlm.nih.gov/pubmed/36170008 ID - info:doi/10.2196/40001 ER - TY - JOUR AU - Perry, M. Laura AU - Morken, Victoria AU - Peipert, D. John AU - Yanez, Betina AU - Garcia, F. Sofia AU - Barnard, Cynthia AU - Hirschhorn, R. Lisa AU - Linder, A. Jeffrey AU - Jordan, Neil AU - Ackermann, T. Ronald AU - Harris, Alexandra AU - Kircher, Sheetal AU - Mohindra, Nisha AU - Aggarwal, Vikram AU - Frazier, Rebecca AU - Coughlin, Ava AU - Bedjeti, Katy AU - Weitzel, Melissa AU - Nelson, C. Eugene AU - Elwyn, Glyn AU - Van Citters, D. Aricca AU - O'Connor, Mary AU - Cella, David PY - 2022/9/21 TI - Patient-Reported Outcome Dashboards Within the Electronic Health Record to Support Shared Decision-making: Protocol for Co-design and Clinical Evaluation With Patients With Advanced Cancer and Chronic Kidney Disease JO - JMIR Res Protoc SP - e38461 VL - 11 IS - 9 KW - patient-reported outcome measures KW - shared decision-making KW - medical informatics KW - coproduction KW - learning health system KW - cancer KW - chronic kidney disease N2 - Background: Patient-reported outcomes?symptoms, treatment side effects, and health-related quality of life?are important to consider in chronic illness care. The increasing availability of health IT to collect patient-reported outcomes and integrate results within the electronic health record provides an unprecedented opportunity to support patients? symptom monitoring, shared decision-making, and effective use of the health care system. Objective: The objectives of this study are to co-design a dashboard that displays patient-reported outcomes along with other clinical data (eg, laboratory tests, medications, and appointments) within an electronic health record and conduct a longitudinal demonstration trial to evaluate whether the dashboard is associated with improved shared decision-making and disease management outcomes. Methods: Co-design teams comprising study investigators, patients with advanced cancer or chronic kidney disease, their care partners, and their clinicians will collaborate to develop the dashboard. Investigators will work with clinic staff to implement the co-designed dashboard for clinical testing during a demonstration trial. The primary outcome of the demonstration trial is whether the quality of shared decision-making increases from baseline to the 3-month follow-up. Secondary outcomes include longitudinal changes in satisfaction with care, self-efficacy in managing treatments and symptoms, health-related quality of life, and use of costly and potentially avoidable health care services. Implementation outcomes (ie, fidelity, appropriateness, acceptability, feasibility, reach, adoption, and sustainability) during the co-design process and demonstration trial will also be collected and summarized. Results: The dashboard co-design process was completed in May 2020, and data collection for the demonstration trial is anticipated to be completed by the end of July 2022. The results will be disseminated in at least one manuscript per study objective. Conclusions: This protocol combines stakeholder engagement, health care coproduction frameworks, and health IT to develop a clinically feasible model of person-centered care delivery. The results will inform our current understanding of how best to integrate patient-reported outcome measures into clinical workflows to improve outcomes and reduce the burden of chronic disease on patients and health care systems. International Registered Report Identifier (IRRID): DERR1-10.2196/38461 UR - https://www.researchprotocols.org/2022/9/e38461 UR - http://dx.doi.org/10.2196/38461 UR - http://www.ncbi.nlm.nih.gov/pubmed/36129747 ID - info:doi/10.2196/38461 ER - TY - JOUR AU - Sarker, Rahman Mohammad Habibur AU - Moriyama, Michiko AU - Rashid, Ur Harun AU - Rahman, Moshiur Md AU - Chisti, Jobayer Mohammod AU - Das, Kumar Sumon AU - Saha, Kumar Samir AU - Arifeen, El Shams AU - Ahmed, Tahmeed AU - Faruque, G. A. S. PY - 2022/8/11 TI - Chronic Kidney Disease Awareness Campaign and Mobile Health Education to Improve Knowledge, Quality of Life, and Motivation for a Healthy Lifestyle Among Patients With Chronic Kidney Disease in Bangladesh: Randomized Controlled Trial JO - J Med Internet Res SP - e37314 VL - 24 IS - 8 KW - Bangladesh KW - health education KW - health knowledge KW - quality of life KW - motivation KW - randomized controlled trial KW - RCT KW - campaign KW - chronic kidney disease KW - knowledge KW - mobile health KW - mHealth KW - kidney KW - chronic disease KW - chronic condition KW - patient education KW - patient knowledge KW - low- and middle-income countries KW - LMIC N2 - Background: Chronic kidney disease (CKD) is linked to major health consequences and a poor quality of life. Despite the fact that CKD is becoming more prevalent, public knowledge of the disease remains low. Objective: This study aimed to evaluate the outcome of a health education intervention designed to enhance knowledge, health-related quality of life (QOL), and motivation about healthy lifestyle among adults with CKD. Methods: This study was a parallel-group (1:1), randomized controlled trial in the Mirzapur subdistrict of Bangladesh that compared 2 groups of patients with CKD. Adults with CKD (stages 1-3) were enrolled in November 2020 and randomly assigned the intervention or control group. The intervention group received health education through a CKD awareness campaign and mobile health technologies and was observed for 6 months, whereas the control group received standard treatment. The primary outcome was the evaluation of improved scores on the CKD knowledge questionnaire, and the secondary outcomes were improved QOL and changes in the levels of blood pressure (BP), BMI, serum creatinine, fasting blood sugar (FBS), hemoglobin, cholesterol, high-density lipoprotein cholesterol, triglyceride, serum uric acid, blood urea nitrogen (BUN), and albumin-to-creatinine ratio. Results: The study enrolled 126 patients (control: n=63; intervention: n=63) and performed intention-to-treat analysis. The analyses included repeated measures ANOVA, and the results were observed to be significantly different from within groups (P<.001), between groups (P<.001), and the interaction of group × time factor (P<.001) for knowledge score. Diastolic BP and BMI showed significant differences arising from within groups (P<.001 and P=.01, respectively) and the interaction of group × time factor (P=.001 and P=.02, respectively); food salinity and hip circumferences showed significant differences arising from within groups (P=.001 and P=.03, respectively) and between groups (P=.001 and P=.02, respectively). Moreover, systolic BP and waist circumference showed significant differences from within groups (P<.001 and P=.003, respectively). However, no significant differences were found arising from within groups, between groups, and the interactions of group × time for QOL, urine salinity, and mid-upper arm circumference. Regarding the laboratory findings, from baseline to 6 months, the mean (SD) FBS decreased by 0.51 (3.77) mmol/L in the intervention group and 0.10 (1.44) mmol/L in the control group (P=.03); however, blood urea nitrogen increased by 3.64 (7.17) mg/dL in the intervention group and 1.68 (10.10) mg/dL in the control group (P=.01). Conclusions: The health education strategy, which included a campaign and mobile health, showed promise for enhancing CKD knowledge among patients with CKD. This strategy may also aid patients with CKD in controlling their FBS and BP. The combined health education initiatives give evidence for scaling them up in Bangladesh and possibly other low- and middle-income countries, particularly in rural and peri-urban settings. Trial Registration: ClinicalTrials.gov NCT04094831; https://clinicaltrials.gov/ct2/show/NCT04094831. International Registered Report Identifier (IRRID): RR2-10.2196/30191 UR - https://www.jmir.org/2022/8/e37314 UR - http://dx.doi.org/10.2196/37314 UR - http://www.ncbi.nlm.nih.gov/pubmed/35969429 ID - info:doi/10.2196/37314 ER - TY - JOUR AU - Lukkanalikitkul, Eakalak AU - Kongpetch, Sawinee AU - Chotmongkol, Wijittra AU - Morley, G. Michael AU - Anutrakulchai, Sirirat AU - Srichan, Chavis AU - Thinkhamrop, Bandit AU - Chunghom, Theenatchar AU - Wiangnon, Pongsai AU - Thinkhamrop, Wilaiphorn AU - Morley, E. Katharine PY - 2022/7/6 TI - Optimization of the Chronic Kidney Disease?Peritoneal Dialysis App to Improve Care for Patients on Peritoneal Dialysis in Northeast Thailand: User-Centered Design Study JO - JMIR Form Res SP - e37291 VL - 6 IS - 7 KW - peritoneal KW - dialysis KW - peritoneum KW - mobile health KW - mHealth KW - rapid cycle process improvement methodology KW - home monitoring KW - near-field communication KW - monitor KW - kidney KW - rapid cycle improvement KW - quality improvement KW - process improvement KW - methodology KW - nephrology KW - nephrologist KW - internal medicine KW - computer program KW - Unified Theory of Acceptance and Use of Technology KW - UTAUT KW - usability KW - interface KW - metric capture KW - barrier KW - renal KW - mobile phone N2 - Background: The prevalence of peritoneal dialysis (PD) in Thailand is increasing rapidly in part because of Thailand?s Peritoneal Dialysis First policy. PD is a home-based renal replacement therapy in which patients with chronic kidney disease perform up to 4 exchanges of dialysate fluid per day in the peritoneal cavity. Overhydration is one of the most common complications in patients on PD and is associated with increased morbidity and mortality. To monitor hydration status, patients collect hydration metrics, including body weight, blood pressure, urine output, and ultrafiltration volume, from each dialysis cycle and enter this information into a PD logbook. This information is reviewed bimonthly at PD clinic appointments. The chronic kidney disease-PD (CKD-PD) app with near-field communication (NFC) and optical character recognition (OCR) was developed to automate hydration metric collection. The information was displayed in the app for self-monitoring and uploaded to a database for real-time monitoring by the PD clinic staff. Early detection and treatment of overhydration could potentially reduce the morbidity and mortality related to overhydration. Objective: This study aims to identify usability issues and technology adoption barriers for the CKD-PD app with NFC and OCR and a monitoring system and to use this information to make rapid cycle improvements. Methods: A multidisciplinary team of nephrologists, PD clinic nurses, computer programmers, and engineers trained and observed 2 groups of 5 participants in the use of the CKD-PD app with NFC and OCR and a monitoring system. The participants were observed using technology in their homes in 3 phases. The data collected included the Unified Theory of Acceptance and Use of Technology questionnaire, think-aloud observation, user ratings, completion of hydration metrics, and upload of hydration metrics to the central database. These results were used by the team between phases to improve the functionality and usefulness of the app. Results: The CKD-PD app with NFC and OCR and a monitoring system underwent 3 rapid improvement cycles. Issues were identified regarding the usability of the NFC and OCR data collection, app stability, user interface, hydration metric calculation, and display. NFC and OCR improved hydration metric capture; however, issues remained with their usability. App stability and user interface issues were corrected, and hydration metrics were successfully uploaded by the end of phase 3. Participants? scores on technology adoption decreased but were still high, and there was enthusiasm for the self-monitoring and clinical communication features. Conclusions: Our rapid cycle process improvement methodology identified and resolved key barriers and usability issues for the CKD-PD app with NFC and OCR and a monitoring system. We believe that this methodology can be accomplished with limited training in data collection, statistical analysis, and funding. UR - https://formative.jmir.org/2022/7/e37291 UR - http://dx.doi.org/10.2196/37291 UR - http://www.ncbi.nlm.nih.gov/pubmed/35793137 ID - info:doi/10.2196/37291 ER - TY - JOUR AU - Sharma, Videha AU - Eleftheriou, Iliada AU - van der Veer, N. Sabine AU - Brass, Andrew AU - Augustine, Titus AU - Ainsworth, John PY - 2022/4/21 TI - Modeling Data Journeys to Inform the Digital Transformation of Kidney Transplant Services: Observational Study JO - J Med Internet Res SP - e31825 VL - 24 IS - 4 KW - digital transformation KW - health information exchange KW - interoperability KW - medical informatics KW - data journey modelling KW - kidney transplantation N2 - Background: Data journey modeling is a methodology used to establish a high-level overview of information technology (IT) infrastructure in health care systems. It allows a better understanding of sociotechnical barriers and thus informs meaningful digital transformation. Kidney transplantation is a complex clinical service involving multiple specialists and providers. The referral pathway for a transplant requires the centralization of patient data across multiple IT solutions and health care organizations. At present, there is a poor understanding of the role of IT in this process, specifically regarding the management of patient data, clinical communication, and workflow support. Objective: To apply data journey modeling to better understand interoperability, data access, and workflow requirements of a regional multicenter kidney transplant service. Methods: An incremental methodology was used to develop the data journey model. This included review of service documents, domain expert interviews, and iterative modeling sessions. Results were analyzed based on the LOAD (landscape, organizations, actors, and data) framework to provide a meaningful assessment of current data management challenges and inform ways for IT to overcome these challenges. Results: Results were presented as a diagram of the organizations (n=4), IT systems (n>9), actors (n>4), and data journeys (n=0) involved in the transplant referral pathway. The diagram revealed that all movement of data was dependent on actor interaction with IT systems and manual transcription of data into Microsoft Word (Microsoft, Inc) documents. Each actor had between 2 and 5 interactions with IT systems to capture all relevant data, a process that was reported to be time consuming and error prone. There was no interoperability within or across organizations, which led to delays as clinical teams manually transferred data, such as medical history and test results, via post or email. Conclusions: Overall, data journey modeling demonstrated that human actors, rather than IT systems, formed the central focus of data movement. The IT landscape did not complement this workflow and exerted a significant administrative burden on clinical teams. Based on this study, future solutions must consider regional interoperability and specialty-specific views of data to support multi-organizational clinical services such as transplantation. UR - https://www.jmir.org/2022/4/e31825 UR - http://dx.doi.org/10.2196/31825 UR - http://www.ncbi.nlm.nih.gov/pubmed/35451983 ID - info:doi/10.2196/31825 ER - TY - JOUR AU - Chua, Horng-Ruey AU - Zheng, Kaiping AU - Vathsala, Anantharaman AU - Ngiam, Kee-Yuan AU - Yap, Hui-Kim AU - Lu, Liangjian AU - Tiong, Ho-Yee AU - Mukhopadhyay, Amartya AU - MacLaren, Graeme AU - Lim, Shir-Lynn AU - Akalya, K. AU - Ooi, Beng-Chin PY - 2021/12/24 TI - Health Care Analytics With Time-Invariant and Time-Variant Feature Importance to Predict Hospital-Acquired Acute Kidney Injury: Observational Longitudinal Study JO - J Med Internet Res SP - e30805 VL - 23 IS - 12 KW - acute kidney injury KW - artificial intelligence KW - biomarkers KW - clinical deterioration KW - electronic health records KW - hospital medicine KW - machine learning N2 - Background: Acute kidney injury (AKI) develops in 4% of hospitalized patients and is a marker of clinical deterioration and nephrotoxicity. AKI onset is highly variable in hospitals, which makes it difficult to time biomarker assessment in all patients for preemptive care. Objective: The study sought to apply machine learning techniques to electronic health records and predict hospital-acquired AKI by a 48-hour lead time, with the aim to create an AKI surveillance algorithm that is deployable in real time. Methods: The data were sourced from 20,732 case admissions in 16,288 patients over 1 year in our institution. We enhanced the bidirectional recurrent neural network model with a novel time-invariant and time-variant aggregated module to capture important clinical features temporal to AKI in every patient. Time-series features included laboratory parameters that preceded a 48-hour prediction window before AKI onset; the latter?s corresponding reference was the final in-hospital serum creatinine performed in case admissions without AKI episodes. Results: The cohort was of mean age 53 (SD 25) years, of whom 29%, 12%, 12%, and 53% had diabetes, ischemic heart disease, cancers, and baseline eGFR <90 mL/min/1.73 m2, respectively. There were 911 AKI episodes in 869 patients. We derived and validated an algorithm in the testing dataset with an AUROC of 0.81 (0.78-0.85) for predicting AKI. At a 15% prediction threshold, our model generated 699 AKI alerts with 2 false positives for every true AKI and predicted 26% of AKIs. A lowered 5% prediction threshold improved the recall to 60% but generated 3746 AKI alerts with 6 false positives for every true AKI. Representative interpretation results produced by our model alluded to the top-ranked features that predicted AKI that could be categorized in association with sepsis, acute coronary syndrome, nephrotoxicity, or multiorgan injury, specific to every case at risk. Conclusions: We generated an accurate algorithm from electronic health records through machine learning that predicted AKI by a lead time of at least 48 hours. The prediction threshold could be adjusted during deployment to optimize recall and minimize alert fatigue, while its precision could potentially be augmented by targeted AKI biomarker assessment in the high-risk cohort identified. UR - https://www.jmir.org/2021/12/e30805 UR - http://dx.doi.org/10.2196/30805 UR - http://www.ncbi.nlm.nih.gov/pubmed/34951595 ID - info:doi/10.2196/30805 ER - TY - JOUR AU - Markossian, W. Talar AU - Boyda, Jason AU - Taylor, Jennifer AU - Etingen, Bella AU - Modave, François AU - Price, Ron AU - Kramer, J. Holly PY - 2021/12/15 TI - A Mobile App to Support Self-management of Chronic Kidney Disease: Development Study JO - JMIR Hum Factors SP - e29197 VL - 8 IS - 4 KW - chronic kidney disease KW - mobile app KW - self-management KW - mHealth KW - mobile apps KW - digital health KW - kidney disease KW - smartphone N2 - Background: Chronic kidney disease (CKD) is a common and costly condition that is usually accompanied by multiple comorbidities including type 2 diabetes, hypertension, and obesity. Proper management of CKD can delay or prevent kidney failure and help mitigate cardiovascular disease risk, which increases as kidney function declines. Smart device apps hold potential to enhance patient self-management of chronic conditions including CKD. Objective: The objective of this study was to develop a mobile app to facilitate self-management of nondialysis-dependent CKD. Methods: Our stakeholder team included 4 patients with stage 3-4 nondialysis-dependent CKD; a kidney transplant recipient; a caretaker; CKD care providers (pharmacists, a nurse, primary care physicians, a nephrologist, and a cardiologist); 2 health services and CKD researchers; a researcher in biomedical informatics, nutrition, and obesity; a system developer; and 2 programmers. Focus groups and in-person interviews with the patients and providers were conducted using a focus group and interview guide based on existing literature on CKD self-management and the mobile app quality criteria from the Mobile App Rating Scale. Qualitative analytic methods including the constant comparative method were used to analyze the focus group and interview data. Results: Patients and providers identified and discussed a list of requirements and preferences regarding the content, features, and technical aspects of the mobile app, which are unique for CKD self-management. Requirements and preferences centered along themes of communication between patients and caregivers, partnership in care, self-care activities, adherence to treatment regimens, and self-care self-efficacy. These identified themes informed the features and content of our mobile app. The mobile app user can enter health data including blood pressure, weight, and blood glucose levels. Symptoms and their severity can also be entered, and users are prompted to contact a physician as indicated by the symptom and its severity. Next, mobile app users can select biweekly goals from a set of predetermined goals with the option to enter customized goals. The user can also keep a list of medications and track medication use. Our app includes feedback mechanisms where in-range values for health data are depicted in green and out-of-range values are depicted in red. We ensured that data entered by patients could be downloaded into a user-friendly report, which could be emailed or uploaded to an electronic health record. The mobile app also includes a mechanism that allows either group or individualized video chat meetings with a provider to facilitate either group support, education, or even virtual clinic visits. The CKD app also includes educational material on CKD and its symptoms. Conclusions: Patients with CKD and CKD care providers believe that a mobile app can enhance CKD self-management by facilitating patient-provider communication and enabling self-care activities including treatment adherence. UR - https://humanfactors.jmir.org/2021/4/e29197 UR - http://dx.doi.org/10.2196/29197 UR - http://www.ncbi.nlm.nih.gov/pubmed/34914614 ID - info:doi/10.2196/29197 ER - TY - JOUR AU - Sarker, Rahman Mohammad Habibur AU - Moriyama, Michiko AU - Rashid, Ur Harun AU - Rahman, Moshiur Md AU - Chisti, Jobayer Mohammod AU - Das, Kumar Sumon AU - Jahan, Yasmin AU - Saha, Kumar Samir AU - Arifeen, El Shams AU - Ahmed, Tahmeed AU - Faruque, G. A. S. PY - 2021/11/19 TI - Health Education Through a Campaign and mHealth to Enhance Knowledge and Quality of Life Among Patients With Chronic Kidney Disease in Bangladesh: Protocol for a Randomized Controlled Trial JO - JMIR Res Protoc SP - e30191 VL - 10 IS - 11 KW - chronic kidney disease KW - campaign KW - mHealth KW - knowledge KW - Bangladesh N2 - Background: Despite the growing burden of chronic kidney disease (CKD), disease knowledge and understanding are still lacking, especially in Bangladesh. Objective: The aim of this study was to evaluate the outcome of a health education intervention in order to enhance knowledge, health-related quality of life (QOL), and motivation regarding healthy lifestyles among rural and periurban adults suffering from CKD. Methods: A parallel-group (1:1) randomized controlled trial is ongoing in the Mirzapur subdistrict, Bangladesh, where two groups of patients with CKD are being compared. Patients aged 18 years and over with CKD (stages 1-3) were enrolled in November 2020. Patients were randomly allocated into either the intervention group (n=63) or the control group (n=63). The control group received usual treatment, while the intervention group received health education through a CKD campaign facilitated by a nephrologist and via mHealth (ie, periodic mobile phone calls) from community health workers. Both groups were followed up for a period of 6 months. The primary endpoint is patients? increased knowledge measured using the Chronic Kidney Disease Knowledge Questionnaire. The secondary endpoints are improved QOL measured using the standardized EuroQol 5-Dimension 5-Level (EQ-5D-5L) questionnaire as well as improvements in the levels of blood pressure, BMI, serum creatinine, fasting blood sugar, hemoglobin, cholesterol, high-density lipoprotein cholesterol, triglyceride, serum uric acid, blood urea nitrogen, and albumin to creatinine ratio. Results: Enrollment of participants began in November 2020; the intervention and follow-up were completed in May 2021. We enrolled 126 patients in the study. Patients? mean ages were 57.97 (SD 15.03) years in the control group and 57.32 (SD 14.37) years in the intervention group. There were 45 out of 63 (71%) females in the control group and 38 out of 63 (60%) females in the intervention group. In addition, there were 38 out of 63 (60%) literate patients in the control group and 33 out of 63 (52%) literate patients in the intervention group. Conclusions: It is expected that a combined approach, incorporating both a CKD campaign and mHealth, for health education may be an effective tool for increasing knowledge and improving QOL among patients with CKD. Trial Registration: ClinicalTrials.gov NCT04094831; https://clinicaltrials.gov/ct2/show/NCT04094831 International Registered Report Identifier (IRRID): DERR1-10.2196/30191 UR - https://www.researchprotocols.org/2021/11/e30191 UR - http://dx.doi.org/10.2196/30191 UR - http://www.ncbi.nlm.nih.gov/pubmed/34806998 ID - info:doi/10.2196/30191 ER - TY - JOUR AU - Yun, Donghwan AU - Cho, Semin AU - Kim, Chul Yong AU - Kim, Ki Dong AU - Oh, Kook-Hwan AU - Joo, Wook Kwon AU - Kim, Su Yon AU - Han, Seok Seung PY - 2021/10/1 TI - Use of Deep Learning to Predict Acute Kidney Injury After Intravenous Contrast Media Administration: Prediction Model Development Study JO - JMIR Med Inform SP - e27177 VL - 9 IS - 10 KW - acute kidney injury KW - artificial intelligence KW - contrast media KW - deep learning KW - machine learning KW - kidney injury KW - computed tomography N2 - Background: Precise prediction of contrast media?induced acute kidney injury (CIAKI) is an important issue because of its relationship with poor outcomes. Objective: Herein, we examined whether a deep learning algorithm could predict the risk of intravenous CIAKI better than other machine learning and logistic regression models in patients undergoing computed tomography (CT). Methods: A total of 14,185 patients who were administered intravenous contrast media for CT at the preventive and monitoring facility in Seoul National University Hospital were reviewed. CIAKI was defined as an increase in serum creatinine of ?0.3 mg/dL within 2 days or ?50% within 7 days. Using both time-varying and time-invariant features, machine learning models, such as the recurrent neural network (RNN), light gradient boosting machine (LGM), extreme gradient boosting machine (XGB), random forest (RF), decision tree (DT), support vector machine (SVM), ?-nearest neighbors, and logistic regression, were developed using a training set, and their performance was compared using the area under the receiver operating characteristic curve (AUROC) in a test set. Results: CIAKI developed in 261 cases (1.8%). The RNN model had the highest AUROC of 0.755 (0.708-0.802) for predicting CIAKI, which was superior to that obtained from other machine learning models. Although CIAKI was defined as an increase in serum creatinine of ?0.5 mg/dL or ?25% within 3 days, the highest performance was achieved in the RNN model with an AUROC of 0.716 (95% confidence interval [CI] 0.664-0.768). In feature ranking analysis, the albumin level was the most highly contributing factor to RNN performance, followed by time-varying kidney function. Conclusions: Application of a deep learning algorithm improves the predictability of intravenous CIAKI after CT, representing a basis for future clinical alarming and preventive systems. UR - https://medinform.jmir.org/2021/10/e27177 UR - http://dx.doi.org/10.2196/27177 UR - http://www.ncbi.nlm.nih.gov/pubmed/34596574 ID - info:doi/10.2196/27177 ER - TY - JOUR AU - Naqvi, Ali Syed Asil AU - Tennankore, Karthik AU - Vinson, Amanda AU - Roy, C. Patrice AU - Abidi, Raza Syed Sibte PY - 2021/8/27 TI - Predicting Kidney Graft Survival Using Machine Learning Methods: Prediction Model Development and Feature Significance Analysis Study JO - J Med Internet Res SP - e26843 VL - 23 IS - 8 KW - kidney transplantation KW - machine learning KW - predictive modeling KW - survival prediction KW - dimensionality reduction KW - feature sensitivity analysis N2 - Background: Kidney transplantation is the optimal treatment for patients with end-stage renal disease. Short- and long-term kidney graft survival is influenced by a number of donor and recipient factors. Predicting the success of kidney transplantation is important for optimizing kidney allocation. Objective: The aim of this study was to predict the risk of kidney graft failure across three temporal cohorts (within 1 year, within 5 years, and after 5 years following a transplant) based on donor and recipient characteristics. We analyzed a large data set comprising over 50,000 kidney transplants covering an approximate 20-year period. Methods: We applied machine learning?based classification algorithms to develop prediction models for the risk of graft failure for three different temporal cohorts. Deep learning?based autoencoders were applied for data dimensionality reduction, which improved the prediction performance. The influence of features on graft survival for each cohort was studied by investigating a new nonoverlapping patient stratification approach. Results: Our models predicted graft survival with area under the curve scores of 82% within 1 year, 69% within 5 years, and 81% within 17 years. The feature importance analysis elucidated the varying influence of clinical features on graft survival across the three different temporal cohorts. Conclusions: In this study, we applied machine learning to develop risk prediction models for graft failure that demonstrated a high level of prediction performance. Acknowledging that these models performed better than those reported in the literature for existing risk prediction tools, future studies will focus on how best to incorporate these prediction models into clinical care algorithms to optimize the long-term health of kidney recipients. UR - https://www.jmir.org/2021/8/e26843 UR - http://dx.doi.org/10.2196/26843 UR - http://www.ncbi.nlm.nih.gov/pubmed/34448704 ID - info:doi/10.2196/26843 ER - TY - JOUR AU - Kawai, Yuki AU - Sankoda, Akiko AU - Waki, Kayo AU - Miyake, Kana AU - Hayashi, Aki AU - Mieno, Makiko AU - Wakui, Hiromichi AU - Tsurutani, Yuya AU - Saito, Jun AU - Hirawa, Nobuhito AU - Yamakawa, Tadashi AU - Komiya, Shiro AU - Isogawa, Akihiro AU - Satoh, Shinobu AU - Minami, Taichi AU - Osada, Uru AU - Iwamoto, Tamio AU - Takano, Tatsuro AU - Terauchi, Yasuo AU - Tamura, Kouichi AU - Yamauchi, Toshimasa AU - Kadowaki, Takashi AU - Nangaku, Masaomi AU - Kashihara, Naoki AU - Ohe, Kazuhiko PY - 2021/8/17 TI - Efficacy of the Self-management Support System DialBetesPlus for Diabetic Kidney Disease: Protocol for a Randomized Controlled Trial JO - JMIR Res Protoc SP - e31061 VL - 10 IS - 8 KW - diabetic kidney disease KW - microalbuminuria KW - albuminuria KW - diabetes mellitus KW - self-management support system KW - mHealth KW - randomized controlled trial KW - diabetes KW - kidney KW - chronic disease KW - support KW - self-management KW - efficacy KW - protocol KW - therapy KW - intervention KW - self-care KW - behavior N2 - Background: Diabetic kidney disease (DKD) is one of the main complications of type 2 diabetes mellitus (T2DM). DKD is a known risk factor for end-stage renal disease, cardiovascular disease, and all-cause death. Effective intervention for early-stage DKD is vital to slowing down the progression of kidney disease and improve prognoses. Mobile health (mHealth) is reportedly effective in supporting patients? self-care and improving glycemic control, but the impact of mHealth on DKD has yet to be shown. Objective: The purpose of this study is to evaluate the efficacy of standard therapy with the addition of a self-management support system, DialBetesPlus, in patients with DKD and microalbuminuria. Methods: This study is a prospective, randomized, open-label, multicenter clinical trial. The target population consists of 160 patients diagnosed with T2DM accompanied by microalbuminuria. We randomly assigned the patients to 2 groups?the intervention group using DialBetesPlus in addition to conventional therapy and the control group using conventional therapy alone. DialBetesPlus is a smartphone application that supports patients? self-management of T2DM. The study period was 12 months, with a follow-up survey at 18 months. The primary outcome was a change in albuminuria levels at 12 months. Secondary outcomes included changes in physical parameters, blood test results (glycemic control, renal function, and lipid metabolism), lifestyle habits, self-management scores, medication therapy, and quality of life. Results: The study was approved in April 2018. We began recruiting patients in July 2018 and completed recruiting in August 2019. The final 18-month follow-up was conducted in March 2021. We recruited 159 patients and randomly allocated 70 into the intervention group and 61 into the control group, with 28 exclusions due to withdrawal of consent, refusal to continue, or ineligibility. The first results are expected to be available in 2021. Conclusions: This is the first randomized controlled trial assessing the efficacy of mHealth on early-stage DKD. We expect that albuminuria levels will decrease significantly in the intervention group due to improved glycemic control with ameliorated self-care behaviors. Trial Registration: UMIN-CTR UMIN000033261; https://upload.umin.ac.jp/cgi-open-bin/ctr/ctr_view.cgi?recptno=R000037924 International Registered Report Identifier (IRRID): DERR1-10.2196/31061 UR - https://www.researchprotocols.org/2021/8/e31061 UR - http://dx.doi.org/10.2196/31061 UR - http://www.ncbi.nlm.nih.gov/pubmed/34402802 ID - info:doi/10.2196/31061 ER - TY - JOUR AU - Kim, Kipyo AU - Yang, Hyeonsik AU - Yi, Jinyeong AU - Son, Hyung-Eun AU - Ryu, Ji-Young AU - Kim, Chul Yong AU - Jeong, Cheol Jong AU - Chin, Jun Ho AU - Na, Young Ki AU - Chae, Dong-Wan AU - Han, Seok Seung AU - Kim, Sejoong PY - 2021/4/16 TI - Real-Time Clinical Decision Support Based on Recurrent Neural Networks for In-Hospital Acute Kidney Injury: External Validation and Model Interpretation JO - J Med Internet Res SP - e24120 VL - 23 IS - 4 KW - acute kidney injury KW - recurrent neural network KW - prediction model KW - external validation KW - internal validation KW - kidney KW - neural networks N2 - Background: Acute kidney injury (AKI) is commonly encountered in clinical practice and is associated with poor patient outcomes and increased health care costs. Despite it posing significant challenges for clinicians, effective measures for AKI prediction and prevention are lacking. Previously published AKI prediction models mostly have a simple design without external validation. Furthermore, little is known about the process of linking model output and clinical decisions due to the black-box nature of neural network models. Objective: We aimed to present an externally validated recurrent neural network (RNN)?based continuous prediction model for in-hospital AKI and show applicable model interpretations in relation to clinical decision support. Methods: Study populations were all patients aged 18 years or older who were hospitalized for more than 48 hours between 2013 and 2017 in 2 tertiary hospitals in Korea (Seoul National University Bundang Hospital and Seoul National University Hospital). All demographic data, laboratory values, vital signs, and clinical conditions of patients were obtained from electronic health records of each hospital. We developed 2-stage hierarchical prediction models (model 1 and model 2) using RNN algorithms. The outcome variable for model 1 was the occurrence of AKI within 7 days from the present. Model 2 predicted the future trajectory of creatinine values up to 72 hours. The performance of each developed model was evaluated using the internal and external validation data sets. For the explainability of our models, different model-agnostic interpretation methods were used, including Shapley Additive Explanations, partial dependence plots, individual conditional expectation, and accumulated local effects plots. Results: We included 69,081 patients in the training, 7675 in the internal validation, and 72,352 in the external validation cohorts for model development after excluding cases with missing data and those with an estimated glomerular filtration rate less than 15 mL/min/1.73 m2 or end-stage kidney disease. Model 1 predicted any AKI development with an area under the receiver operating characteristic curve (AUC) of 0.88 (internal validation) and 0.84 (external validation), and stage 2 or higher AKI development with an AUC of 0.93 (internal validation) and 0.90 (external validation). Model 2 predicted the future creatinine values within 3 days with mean-squared errors of 0.04-0.09 for patients with higher risks of AKI and 0.03-0.08 for those with lower risks. Based on the developed models, we showed AKI probability according to feature values in total patients and each individual with partial dependence, accumulated local effects, and individual conditional expectation plots. We also estimated the effects of feature modifications such as nephrotoxic drug discontinuation on future creatinine levels. Conclusions: We developed and externally validated a continuous AKI prediction model using RNN algorithms. Our model could provide real-time assessment of future AKI occurrences and individualized risk factors for AKI in general inpatient cohorts; thus, we suggest approaches to support clinical decisions based on prediction models for in-hospital AKI. UR - https://www.jmir.org/2021/4/e24120 UR - http://dx.doi.org/10.2196/24120 UR - http://www.ncbi.nlm.nih.gov/pubmed/33861200 ID - info:doi/10.2196/24120 ER - TY - JOUR AU - Bandiera, Carole AU - Dotta-Celio, Jennifer AU - Locatelli, Isabella AU - Nobre, Dina AU - Wuerzner, Grégoire AU - Pruijm, Menno AU - Lamine, Faiza AU - Burnier, Michel AU - Zanchi, Anne AU - Schneider, Paule Marie PY - 2021/3/19 TI - Interprofessional Medication Adherence Program for Patients With Diabetic Kidney Disease: Protocol for a Randomized Controlled and Qualitative Study (PANDIA-IRIS) JO - JMIR Res Protoc SP - e25966 VL - 10 IS - 3 KW - medication adherence KW - patient compliance KW - diabetes mellitus KW - diabetes complications KW - diabetic nephropathies KW - chronic kidney disease KW - kidney failure KW - renal insufficiency KW - electronic monitoring KW - interprofessional program N2 - Background: Despite effective treatments, more than 30% of patients with diabetes will present with diabetic kidney disease (DKD) at some point. Patients with DKD are among the most complex as their care is multifactorial and involves different groups of health care providers. Suboptimal adherence to polypharmacy is frequent and contributes to poor outcomes. As self-management is one of the keys to clinical success, structured medication adherence programs are crucial. The PANDIA-IRIS (patients diabétiques et insuffisants rénaux: un programme interdisciplinaire de soutien à l?adhésion thérapeutique) study is based on a routine medication adherence program led by pharmacists. Objective: The aim of this study is to define the impact of the duration of this medication adherence program on long-term adherence and clinical outcomes in patients with DKD. Methods: This monocentric adherence program consists of short, repeated motivational interviews focused on patients? medication behaviors combined with the use of electronic monitors containing patients? medications. When patients open the electronic monitor cap to take their medication, the date and hour at each opening are registered. In total, 73 patients are randomized as 1:1 in 2 parallel groups; the adherence program will last 6 months in the first group versus 12 months in the second group. After the intervention phases, patients continue using their electronic monitors for a total of 24 months but without receiving feedback. Electronic monitors and pill counts are used to assess medication adherence. Persistence and implementation will be described using Kaplan-Meier curves and generalized estimating equation multimodeling, respectively. Longitudinal adherence will be presented as the product of persistence and implementation and modelized by generalized estimating equation multimodeling. The evolution of the ADVANCE (Action in Diabetes and Vascular disease: Preterax and Diamicron Modified-Release Controlled Evaluation) and UKPDS (United Kingdom Prospective Diabetes Study) clinical scores based on medication adherence will be analyzed with generalized estimating equation multimodeling. Patients? satisfaction with this study will be assessed through qualitative interviews, which will be transcribed verbatim, coded, and analyzed for the main themes. Results: This study was approved by the local ethics committee (Vaud, Switzerland) in November 2015. Since then, 2 amendments to the protocol have been approved in June 2017 and October 2019. Patients? recruitment began in April 2016 and ended in October 2020. This study was introduced to all consecutive eligible patients (n=275). Among them, 73 accepted to participate (26.5%) and 202 (73.5%) refused. Data collection is ongoing and data analysis is planned for 2022. Conclusions: The PANDIA-IRIS study will provide crucial information about the impact of the medication adherence program on the adherence and clinical outcomes of patients with DKD. Monitoring medication adherence during the postintervention phase is innovative and will shed light on the duration of the intervention on medication adherence. Trial Registration: Clinicaltrials.gov NCT04190251_PANDIA IRIS; https://clinicaltrials.gov/ct2/show/NCT04190251 International Registered Report Identifier (IRRID): DERR1-10.2196/25966 UR - https://www.researchprotocols.org/2021/3/e25966 UR - http://dx.doi.org/10.2196/25966 UR - http://www.ncbi.nlm.nih.gov/pubmed/33739292 ID - info:doi/10.2196/25966 ER - TY - JOUR AU - Dale, L. Bethany AU - Bose, Subhasish AU - Kuo, Sheng AU - Burns, Alana AU - Daou, Pierre AU - Short, Jenna AU - Miles, Jake PY - 2021/3/15 TI - Transition of Renal Patients Using AlloSure Into Community Kidney Care (TRACK): Protocol for Long-Term Allograft Surveillance in Renal Transplant Recipients JO - JMIR Res Protoc SP - e25941 VL - 10 IS - 3 KW - donor-derived cell free DNA (dd-cfDNA) KW - molecular inflammation KW - molecular injury KW - acute rejection KW - allograft injury KW - allograft surveillance KW - renal transplant KW - renal KW - transplant KW - injury KW - graft rejection KW - kidney KW - kidney disease KW - transplantation N2 - Background: Patients with end-stage kidney disease require complex and expensive medical management. Kidney transplantation remains the treatment of choice for end-stage kidney disease and is considered superior to all other modalities of renal replacement therapy or dialysis. However, access to kidney transplant is limited by critical supply and demand, making it extremely important to ensure longevity of transplanted kidneys. This is prevented through lifelong immunosuppression, with caution not to overly suppress the immune system, resulting in toxicity and harm. Transition of care to community nephrologists after initial kidney transplantation and monitoring at a transplant center is an important process to ensure delivery of effective and patient-centric care closer to home. Once transplanted, laborious surveillance of the immune system and monitoring for potential rejection and injury are undertaken through an armamentarium of screening modalities. Posttransplant surveillance for kidney function and injury remains key to follow-up care. While kidney function, quantified by estimated glomerular filtration rate and serum creatinine, and kidney injury, measured by proteinuria and hematuria, are standard biomarkers used to monitor injury and rejection posttransplant, they have recently been demonstrated to be inferior in performance to that of AlloSure (CareDx Inc, Brisbane, CA) circulating donor-derived, cell-free DNA (dd-cfDNA). Objective: The outcomes and methods of monitoring renal transplant recipients posttransplant have remained stagnant over the past 15 years. The aim of this study is to consider intensive surveillance using AlloSure dd-cfDNA in an actively managed protocol, assessing whether it increases long-term allograft survival in kidney transplant recipients compared with current standard clinical care in community nephrology. Methods: The study protocol will acquire data from a phase IV observational trial to assess a cohort of renal transplant patients managed using AlloSure dd-cfDNA and patient care managers versus 1000 propensity-matched historic controls using United Network for Organ Sharing U.S. Scientific Registry of Transplant Recipients data. Data will be managed in a centralized electronic data server. The primary outcome will be superior allograft survival, as a composite of return to dialysis, retransplant, death due to allograft failure, and death with a functional graft (infection, malignancy, and cardiovascular death). The secondary endpoints will assess improved kidney function through decline in estimated glomerular filtration rate and immune activity through development of donor-specific antibodies. Results: The total sample is anticipated to be 3500 (2500 patients managed with AlloSure dd-cfDNA and 1000 propensity-matched controls). Active enrollment began in November 2020. Conclusions: Based on a significant literature base, we believe implementing the surveillance of dd-cfDNA in the kidney transplant population will have a positive impact on graft survival. Through early identification of rejection and facilitating timely intervention, prolongation of allograft survival versus those not managed by dd-cfDNA surveillance protocol should be superior. International Registered Report Identifier (IRRID): PRR1-10.2196/25941 UR - https://www.researchprotocols.org/2021/3/e25941 UR - http://dx.doi.org/10.2196/25941 UR - http://www.ncbi.nlm.nih.gov/pubmed/33720033 ID - info:doi/10.2196/25941 ER - TY - JOUR AU - Donald, Maoliosa AU - Beanlands, Heather AU - Straus, E. Sharon AU - Smekal, Michelle AU - Gil, Sarah AU - Elliott, J. Meghan AU - Herrington, Gwen AU - Harwood, Lori AU - Waldvogel, Blair AU - Delgado, Maria AU - Sparkes, Dwight AU - Tong, Allison AU - Grill, Allan AU - Novak, Marta AU - James, Thomas Matthew AU - Brimble, Scott K. AU - Samuel, Susan AU - Tu, Karen AU - Farragher, Janine AU - Hemmelgarn, R. Brenda PY - 2021/2/9 TI - A Web-Based Self-Management Support Prototype for Adults With Chronic Kidney Disease (My Kidneys My Health): Co-Design and Usability Testing JO - JMIR Form Res SP - e22220 VL - 5 IS - 2 KW - chronic kidney disease KW - knowledge-to-action framework KW - integrated knowledge translation KW - patient engagement KW - patient-oriented research KW - self-management KW - web-based intervention N2 - Background: Supporting patients to self-manage their chronic kidney disease (CKD) has been identified as a research priority by patients with CKD and those who care for them. Self-management has been shown to slow CKD progression and improve the quality of life of individuals living with the disease. Previous work has identified a need for a person-centered, theory-informed, web-based tool for CKD self-management that can be individualized to a patient?s unique situation, priorities, and preferences. We addressed this gap using an integrated knowledge translation method and patient engagement principles. Objective: The aim of this study is to conduct systematic co-design and usability testing of a web-based self-management prototype for adults with CKD (nondialysis and nontransplant) and their caregivers to enhance self-management support. Methods: A multistep, iterative system development cycle was used to co-design and test the My Kidneys My Health prototype. The 3-step process included creating website features and content using 2 sequential focus groups with patients with CKD and caregivers, heuristic testing using the 10 heuristic principles by Nielsen, and usability testing through in-person 60-minute interviews with patients with CKD and their caregivers. Patients with CKD, caregivers, clinicians, researchers, software developers, graphic designers, and policy makers were involved in all steps of this study. Results: In step 1, 18 participants (14 patients and 4 caregivers) attended one of the 2 sequential focus groups. The participants provided specific suggestions for simplifying navigation as well as suggestions to incorporate video, text, audio, interactive components, and visuals to convey information. A total of 5 reviewers completed the heuristic analysis (step 2), identifying items mainly related to navigation and functionality. Furthermore, 5 participants completed usability testing (step 3) and provided feedback on video production, navigation, features and functionality, and branding. Participants reported visiting the website repeatedly for the following features: personalized food tool, my health care provider question list, symptom guidance based on CKD severity, and medication advice. Usability was high, with a mean system usability score of 90 out of 100. Conclusions: The My Kidneys My Health prototype is a systematically developed, multifaceted, web-based CKD self-management support tool guided by the theory and preferences of patients with CKD and their caregivers. The website is user friendly and provides features that improve user experience by tailoring the content and resources to their needs. A feasibility study will provide insights into the acceptability of and engagement with the prototype and identify preliminary patient-reported outcomes (eg, self-efficacy) as well as potential factors related to implementation. This work is relevant given the shift to virtual care during the current pandemic times and provides patients with support when in-person care is restricted. UR - https://formative.jmir.org/2021/2/e22220 UR - http://dx.doi.org/10.2196/22220 UR - http://www.ncbi.nlm.nih.gov/pubmed/33560245 ID - info:doi/10.2196/22220 ER - TY - JOUR AU - Li, Wen-Yi AU - Chiu, Fu-Chun AU - Zeng, Jyun-Kai AU - Li, Yao-Wei AU - Huang, Su-Hua AU - Yeh, Hui-Chin AU - Cheng, Bor-Wen AU - Yang, Feng-Jung PY - 2020/12/15 TI - Mobile Health App With Social Media to Support Self-Management for Patients With Chronic Kidney Disease: Prospective Randomized Controlled Study JO - J Med Internet Res SP - e19452 VL - 22 IS - 12 KW - chronic kidney disease KW - self-management KW - self-efficacy KW - quality of life KW - health management platform KW - wearable device N2 - Background: Chronic kidney disease (CKD) is a global health burden. Self-management plays a key role in improving modifiable risk factors. Objective: The aim of this study was to evaluate the effectiveness of wearable devices, a health management platform, and social media at improving the self-management of CKD, with the goal of establishing a new self-management intervention model. Methods: In a 90-day prospective experimental study, a total of 60 people with CKD at stages 1-4 were enrolled in the intervention group (n=30) and control group (n=30). All participants were provided with wearable devices that collected exercise-related data. All participants maintained dietary diaries using a smartphone app. All dietary and exercise information was then uploaded to a health management platform. Suggestions about diet and exercise were provided to the intervention group only, and a social media group was created to inspire the participants in the intervention group. Participants? self-efficacy and self-management questionnaire scores, Kidney Disease Quality of Life scores, body composition, and laboratory examinations before and after the intervention were compared between the intervention and control groups. Results: A total of 49 participants completed the study (25 in the intervention group and 24 in the control group); 74% of the participants were men and the mean age was 51.22 years. There were no differences in measured baseline characteristics between the groups except for educational background. After the intervention, the intervention group showed significantly higher scores for self-efficacy (mean 171.28, SD 22.92 vs mean 142.21, SD 26.36; P<.001) and self-management (mean 54.16, SD 6.71 vs mean 47.58, SD 6.42; P=.001). Kidney Disease Quality of Life scores were also higher in the intervention group (mean 293.16, SD 34.21 vs mean 276.37, SD 32.21; P=.02). The number of steps per day increased in the intervention group (9768.56 in week 1 and 11,389.12 in week 12). The estimated glomerular filtration rate (eGFR) of the intervention group was higher than that of the control group (mean 72.47, SD 24.28 vs mean 59.69, SD 22.25 mL/min/1.73m2; P=.03) and the decline in eGFR was significantly slower in the intervention group (?0.56 vs ?4.58 mL/min/1.73m2). There were no differences in body composition between groups postintervention. Conclusions: The use of wearable devices, a health management platform, and social media support not only strengthened self-efficacy and self-management but also improved quality of life and a slower eGFR decline in people with CKD at stages 1-4. These results outline a new self-management model to promote healthy lifestyle behaviors for patients with CKD. Trial Registration: ClinicalTrials.gov NCT04617431; https://www.clinicaltrials.gov/ct2/show/NCT04617431 UR - http://www.jmir.org/2020/12/e19452/ UR - http://dx.doi.org/10.2196/19452 UR - http://www.ncbi.nlm.nih.gov/pubmed/33320101 ID - info:doi/10.2196/19452 ER - TY - JOUR AU - Tuot, S. Delphine AU - Crowley, T. Susan AU - Katz, A. Lois AU - Leung, Joseph AU - Alcantara-Cadillo, K. Delly AU - Ruser, Christopher AU - Talbot-Montgomery, Elizabeth AU - Vassalotti, A. Joseph PY - 2020/10/19 TI - The Kidney Score Platform for Patient and Clinician Awareness, Communication, and Management of Kidney Disease: Protocol for a Mixed Methods Study JO - JMIR Res Protoc SP - e22024 VL - 9 IS - 10 KW - chronic kidney disease KW - CKD KW - awareness KW - implementation science KW - behavioral change wheel, RE-AIM N2 - Background: Patient awareness, clinician detection, and management of chronic kidney disease remain suboptimal, despite clinical practice guidelines and diverse education programs. Objective: This protocol describes a study to develop and investigate the impact of the National Kidney Foundation Kidney Score Platform on chronic kidney disease awareness, communication, and management, by leveraging the Behavior Change Wheel, an implementation science framework that helps identify behavioral intervention targets and functions that address barriers to behavior change. Methods: We interviewed 20 patients with chronic kidney disease and 11 clinicians to identify patient and clinician behaviors suitable for intervention and barriers to behavior change (eg, limited awareness of chronic kidney disease clinical practice guidelines within primary care settings, limited data analytics to highlight chronic kidney disease care gaps, asymptomatic nature of chronic kidney disease in conjunction with patient reliance on primary care clinicians to determine risk and order kidney testing). Leveraging the Behavior Change Wheel, the Kidney Score Platform was developed with a patient-facing online Risk Calculator and a clinician-facing Clinical Practice Toolkit. The Risk Calculator utilizes risk predictive analytics to provide interactive health information tailored to an individual?s chronic kidney disease risk and health status. The Clinical Practice Toolkit assists clinicians in discussing chronic kidney disease with individuals at risk for and with kidney disease and in managing their patient population with chronic kidney disease. The Kidney Score Platform will be tested in 2 Veterans Affairs primary health care settings using a pre?post study design. Outcomes will include changes in patient self-efficacy for chronic kidney disease management (primary outcome), quality of communication with clinicians about chronic kidney disease, and practitioners? knowledge of chronic kidney disease guidelines. Process outcomes will identify usability and adoption of different elements of the Kidney Score Platform using qualitative and quantitative methods. Results: As of September 2020, usability studies are underway with veterans and clinicians to refine the patient-facing components of the Kidney Score Platform before study initiation. Results and subsequent changes to the Kidney Score Platform will be published at a later date. The study is expected to be completed by December 2021. Conclusions: Results of this study will be used to inform integration of the Kidney Score Platform within primary care settings so that it can serve as a central component of the National Kidney Foundation public awareness campaign to educate, engage, and empower individuals at risk for and living with chronic kidney disease. International Registered Report Identifier (IRRID): PRR1-10.2196/22024 UR - http://www.researchprotocols.org/2020/10/e22024/ UR - http://dx.doi.org/10.2196/22024 UR - http://www.ncbi.nlm.nih.gov/pubmed/33074162 ID - info:doi/10.2196/22024 ER - TY - JOUR AU - Eaton, Cyd AU - Comer, Margaret AU - Pruette, Cozumel AU - Psoter, Kevin AU - Riekert, Kristin PY - 2020/8/14 TI - Text Messaging Adherence Intervention for Adolescents and Young Adults with Chronic Kidney Disease: Pilot Randomized Controlled Trial and Stakeholder Interviews JO - J Med Internet Res SP - e19861 VL - 22 IS - 8 KW - medication adherence KW - mobile health KW - pediatrics KW - kidney diseases KW - kidney KW - mHealth KW - adherence KW - adolescent KW - young adult KW - intervention N2 - Background: Up to one-third of adolescents and young adults (11-21 years old) with chronic kidney disease exhibit suboptimal rates of adherence to renal-protective antihypertensive medications. Mobile health interventions may promote higher adherence to these medicines in these individuals, but empirical research is needed to inform best practices for applying these modalities. Objective: In this multiphase investigation, we developed and tested a theoretically informed text messaging intervention based on the COM-B model, a well-established health intervention framework stating that capability, opportunity, and motivation interactively modify health behaviors, to improve participants? antihypertensive medication adherence in a pilot randomized controlled trial. Qualitative data on user experiences were obtained. Methods: In phase 1, intervention messages (Reminder+COM-B Message) were developed via stakeholder engagement of participants and pediatric nephrologists. In phase 2, the Reminder+COM-B Message intervention was tested against a Reminder-only Message active control condition in an 8-week pilot randomized controlled trial. The primary outcome was daily electronically monitored antihypertensive medication adherence and secondary outcomes included pre-post participant surveys of adherence self-efficacy, adherence barriers, outcome expectancies for taking medicine, and motivation for and importance of taking medicine. In phase 3, qualitative interviews related to user experiences were conducted with participants in the Reminder+COM-B Message intervention group. Results: Following phase 1, 34 participants (mean age 16.59 years, 41% female, 38% African American/Black, 35% hypertension diagnosis) completed the phase 2 pilot randomized controlled trial (n=18 in the Reminder+COM-B Message intervention group, n=16 in the Reminder-only Message active control group). All participants in the Reminder+COM-B Message intervention group completed a phase 3 qualitative interview. Overall, study procedures were feasible and the Reminder+COM-B Message intervention was acceptable to the participants (eg, 15/18 participants reported reading the majority of messages sent to them, 0/18 reported that the messages reduced their desire to take medicine). Prerandomization, there were no significant group differences in the rate of change in daily adherence over time. However, postrandomization, there was a significant group by time interaction (B=.01, P=.04) in which daily adherence decreased significantly over time in the Reminder-only Message active control group but remained stable in the Reminder+COM-B Message intervention group. There were no significant differences between groups in pre-post changes in survey responses. Qualitative interviews revealed participants? perceptions of how the Reminder+COM-B Message intervention changed adherence behavior and highlighted several areas for improving the intervention (eg, adapt messaging timing, intensity, and content to match daily adherence, send praise when medicine is taken). Conclusions: The Reminder+COM-B Message intervention was feasible and acceptable to adolescents/young adults and demonstrated potential to promote participants? daily medication adherence beyond simple reminders. Further research is needed to determine the Reminder+COM-B Message intervention?s mechanisms of adherence behavior change and to incorporate qualitative participant feedback into a modified version of this intervention to enhance its acceptability. Trial Registration: ClinicalTrials.gov NCT03651596; https://clinicaltrials.gov/ct2/show/NCT03651596 UR - http://www.jmir.org/2020/8/e19861/ UR - http://dx.doi.org/10.2196/19861 UR - http://www.ncbi.nlm.nih.gov/pubmed/32795983 ID - info:doi/10.2196/19861 ER - TY - JOUR AU - Hsu, Chien-Ning AU - Liu, Chien-Liang AU - Tain, You-Lin AU - Kuo, Chin-Yu AU - Lin, Yun-Chun PY - 2020/8/4 TI - Machine Learning Model for Risk Prediction of Community-Acquired Acute Kidney Injury Hospitalization From Electronic Health Records: Development and Validation Study JO - J Med Internet Res SP - e16903 VL - 22 IS - 8 KW - community-acquired acute kidney injury (CA-AKI) KW - hospitalization KW - treatment decision making KW - clinical decision support system KW - machine learning KW - feature selection with extreme gradient boost (XGBoost) KW - least absolute shrinkage and selection operator (LASSO) KW - risk prediction N2 - Background: Community-acquired acute kidney injury (CA-AKI)-associated hospitalizations impose significant health care needs and contribute to in-hospital mortality. However, most risk prediction models developed to date have focused on AKI in a specific group of patients during hospitalization, and there is limited knowledge on the baseline risk in the general population for preventing CA-AKI-associated hospitalization. Objective: To gain further insight into risk exploration, the aim of this study was to develop, validate, and establish a scoring system to facilitate health professionals in enabling early recognition and intervention of CA-AKI to prevent permanent kidney damage using different machine-learning techniques. Methods: A nested case-control study design was employed using electronic health records derived from a group of Chang Gung Memorial Hospitals in Taiwan from 2010 to 2017 to identify 234,867 adults with at least two measures of serum creatinine at hospital admission. Patients were classified into a derivation cohort (2010-2016) and a temporal validation cohort (2017). Patients with the first episode of CA-AKI at hospital admission were classified into the case group and those without CA-AKI were classified in the control group. A total of 47 potential candidate variables, including age, gender, prior use of nephrotoxic medications, Charlson comorbid conditions, commonly measured laboratory results, and recent use of health services, were tested to develop a CA-AKI hospitalization risk model. Permutation-based selection with both the extreme gradient boost (XGBoost) and least absolute shrinkage and selection operator (LASSO) algorithms was performed to determine the top 10 important features for scoring function development. Results: The discriminative ability of the risk model was assessed by the area under the receiver operating characteristic curve (AUC), and the predictive CA-AKI risk model derived by the logistic regression algorithm achieved an AUC of 0.767 (95% CI 0.764-0.770) on derivation and 0.761 on validation for any stage of AKI, with positive and negative predictive values of 19.2% and 96.1%, respectively. The risk model for prediction of CA-AKI stages 2 and 3 had an AUC value of 0.818 for the validation cohort with positive and negative predictive values of 13.3% and 98.4%, respectively. These metrics were evaluated at a cut-off value of 7.993, which was determined as the threshold to discriminate the risk of AKI. Conclusions: A machine learning?generated risk score model can identify patients at risk of developing CA-AKI-related hospitalization through a routine care data-driven approach. The validated multivariate risk assessment tool could help clinicians to stratify patients in primary care, and to provide monitoring and early intervention for preventing AKI while improving the quality of AKI care in the general population. UR - https://www.jmir.org/2020/8/e16903 UR - http://dx.doi.org/10.2196/16903 UR - http://www.ncbi.nlm.nih.gov/pubmed/32749223 ID - info:doi/10.2196/16903 ER - TY - JOUR AU - Lecamwasam, R. Ashani AU - Mohebbi, Mohammadreza AU - Ekinci, I. Elif AU - Dwyer, M. Karen AU - Saffery, Richard PY - 2020/7/31 TI - Identification of Potential Biomarkers of Chronic Kidney Disease in Individuals with Diabetes: Protocol for a Cross-sectional Observational Study JO - JMIR Res Protoc SP - e16277 VL - 9 IS - 7 KW - epigenetics KW - metabolomics KW - gut microbiome KW - diabetes KW - chronic kidney disease N2 - Background: The importance of identifying people with diabetes and progressive kidney dysfunction relates to the excess morbidity and mortality of this group. Rates of cardiovascular disease are much higher in people with both diabetes and kidney dysfunction than in those with only one of these conditions. By the time these people are identified in current clinical practice, proteinuria and renal dysfunction are already established, limiting the effectiveness of therapeutic interventions. The identification of an epigenetic or blood metabolite signature or gut microbiome profile may identify those with diabetes at risk of progressive chronic kidney disease, in turn providing targeted intervention to improve patient outcomes. Objective: This study aims to identify potential biomarkers in people with diabetes and chronic kidney disease (CKD) associated with progressive renal injury and to distinguish between stages of chronic kidney disease. Three sources of biomarkers will be explored, including DNA methylation profiles in blood lymphocytes, the metabolomic profile of blood-derived plasma and urine, and the gut microbiome. Methods: The cross-sectional study recruited 121 people with diabetes and varying stages (stages 1-5) of chronic kidney disease. Single-point data collection included blood, urine, and fecal samples in addition to clinical data such as anthropometric measurements and biochemical parameters. Additional information obtained from medical records included patient demographics, medical comorbidities, and medications. Results: Data collection commenced in January 2018 and was completed in June 2018. At the time of submission, 121 patients had been recruited, and 119 samples remained after quality control. There were 83 participants in the early diabetes-associated CKD group with a mean estimated glomerular filtration rate (eGFR) of 61.2 mL/min/1.73 m2 (early CKD group consisting of stage 1, 2, and 3a CKD), and 36 participants in the late diabetic CKD group with a mean eGFR of 23.9 mL/min/1.73 m2 (late CKD group, consisting of stage 3b, 4, and 5), P<.001. We have successfully obtained DNA for methylation and microbiome analyses using the biospecimens collected via this protocol and are currently analyzing these results together with the metabolome of this cohort of individuals with diabetic CKD. Conclusions: Recent advances have improved our understanding of the epigenome, metabolomics, and the influence of the gut microbiome on the incidence of diseases such as cancers, particularly those related to environmental exposures. However, there is a paucity of literature surrounding these influencers in renal disease. This study will provide insight into the fundamental understanding of the pathophysiology of CKD in individuals with diabetes, especially in novel areas such as epigenetics, metabolomics, and the kidney-gut axis. International Registered Report Identifier (IRRID): DERR1-10.2196/16277 UR - http://www.researchprotocols.org/2020/7/e16277/ UR - http://dx.doi.org/10.2196/16277 UR - http://www.ncbi.nlm.nih.gov/pubmed/32734931 ID - info:doi/10.2196/16277 ER - TY - JOUR AU - Waterman, D. Amy AU - Wood, H. Emily AU - Ranasinghe, N. Omesh AU - Faye Lipsey, Amanda AU - Anderson, Crystal AU - Balliet, Wendy AU - Holland-Carter, Lauren AU - Maurer, Stacey AU - Aurora Posadas Salas, Maria PY - 2020/7/21 TI - A Digital Library for Increasing Awareness About Living Donor Kidney Transplants: Formative Study JO - JMIR Form Res SP - e17441 VL - 4 IS - 7 KW - living donor kidney transplant KW - living donation KW - health education KW - informed decision-making KW - awareness KW - health literacy KW - video library KW - health technology KW - kidney diseases KW - diffusion of innovation KW - digital library KW - mobile phone N2 - Background: It is not common for people to come across a living kidney donor, let alone consider whether they would ever donate a kidney themselves while they are alive. Narrative storytelling, the sharing of first-person narratives based on lived experience, may be an important way to improve education about living donor kidney transplants (LDKTs). Developing ways to easily standardize and disseminate diverse living donor stories using digital technology could inspire more people to consider becoming living donors and reduce the kidney shortage nationally. Objective: This paper aimed to describe the development of the Living Donation Storytelling Project, a web-based digital library of living donation narratives from multiple audiences using video capture technology. Specifically, we aimed to describe the theoretical foundation and development of the library, a protocol to capture diverse storytellers, the characteristics and experiences of participating storytellers, and the frequency with which any ethical concerns about the content being shared emerged. Methods: This study invited kidney transplant recipients who had received LDKTs, living donors, family members, and patients seeking LDKTs to record personal stories using video capture technology by answering a series of guided prompts on their computer or smartphone and answering questions about their filming experience. The digital software automatically spliced responses to open-ended prompts, creating a seamless story available for uploading to a web-based library and posting to social media. Each story was reviewed by a transplant professional for the disclosure of protected health information (PHI), pressuring others to donate, and medical inaccuracies. Disclosures were edited. Results: This study recruited diverse storytellers through social media, support groups, churches, and transplant programs. Of the 137 storytellers who completed the postsurvey, 105/137 (76.6%) were white and 99/137 (72.2%) were female. They spent 62.5 min, on average, recording their story, with a final median story length of 10 min (00:46 seconds to 32:16 min). A total of 94.8% (130/137) of storytellers were motivated by a desire to educate the public; 78.1% (107/137) were motivated to help more people become living donors; and 75.9% (104/137) were motivated to dispel myths. The ease of using the technology and telling their story varied, with the fear of being on film, emotional difficulty talking about their experiences, and some technological barriers being reported. PHI, most commonly surnames and transplant center names, was present in 62.9% (85/135) of stories and was edited out. Conclusions: With appropriate sensitivity to ensure diverse recruitment, ethical review of content, and support for storytellers, web-based storytelling platforms may be a cost-effective and convenient way to further engage patients and increase the curiosity of the public in learning more about the possibility of becoming living donors. UR - https://formative.jmir.org/2020/7/e17441 UR - http://dx.doi.org/10.2196/17441 UR - http://www.ncbi.nlm.nih.gov/pubmed/32480362 ID - info:doi/10.2196/17441 ER - TY - JOUR AU - Bowman, Cassandra AU - Lunyera, Joseph AU - Alkon, Aviel AU - Boulware, Ebony L. AU - St Clair Russell, Jennifer AU - Riley, Jennie AU - Fink, C. Jeffrey AU - Diamantidis, Clarissa PY - 2020/5/28 TI - A Patient Safety Educational Tool for Patients With Chronic Kidney Disease: Development and Usability Study JO - JMIR Form Res SP - e16137 VL - 4 IS - 5 KW - patient safety KW - chronic kidney disease KW - patient education KW - mhealth N2 - Background: Chronic kidney disease (CKD) is a health condition that threatens patient safety; however, few interventions provide patient-centered education about kidney-specific safety hazards. Objective: We sought to develop and test the usability of a mobile tablet?based educational tool designed to promote patient awareness of relevant safety topics in CKD. Methods: We used plain language principles to develop content for the educational tool, targeting four patient-actionable safety objectives that are relevant for individuals with CKD. These four objectives included avoidance of nonsteroidal anti-inflammatory drugs (NSAIDs); hypoglycemia awareness (among individuals with diabetes); temporary cessation of certain medications during acute volume depletion to prevent acute kidney injury (ie, ?sick day protocol?); and contrast dye risk awareness. Our teaching strategies optimized human-computer interaction and content retention using audio, animation, and clinical vignettes to reinforce themes. For example, using a vignette of a patient with CKD with pain and pictures of common NSAIDs, participants were asked ?Which of the following pain medicines are safe for Mr. Smith to take for his belly pain?? Assessment methods consisted of preknowledge and postknowledge surveys, with provision of correct responses and explanations. Usability testing of the tablet-based tool was performed among 12 patients with any stage of CKD, and program tasks were rated upon completion as no error, noncritical error (self-corrected), or critical error (needing assistance). Results: The 12 participants in this usability study were predominantly 65 years of age or older (n=7, 58%) and female (n=7, 58%); all participants owned a mobile device and used it daily. Among the 725 total tasks that the participants completed, there were 31 noncritical errors (4.3%) and 15 critical errors (2.1%); 1 participant accounted for 30 of the total errors. Of the 12 participants, 10 (83%) easily completed 90% or more of their tasks. Most participants rated the use of the tablet as very easy (n=7, 58%), the activity length as ?just right? (rather than too long or too short) (n=10, 83%), and the use of clinical vignettes as helpful (n=10, 83%); all participants stated that they would recommend this activity to others. The median rating of the activity was 8 on a scale of 1 to 10 (where 10 is best). We incorporated all participant recommendations into the final version of the educational tool. Conclusions: A tablet-based patient safety educational tool is acceptable and usable by individuals with CKD. Future studies leveraging iterations of this educational tool will explore its impact on health outcomes in this high-risk population. UR - http://formative.jmir.org/2020/5/e16137/ UR - http://dx.doi.org/10.2196/16137 UR - http://www.ncbi.nlm.nih.gov/pubmed/32463366 ID - info:doi/10.2196/16137 ER - TY - JOUR AU - Kaiser, Paulina AU - Pipitone, Olivia AU - Franklin, Anthony AU - Jackson, R. Dixie AU - Moore, A. Elizabeth AU - Dubuque, R. Christopher AU - Peralta, A. Carmen AU - De Mory, C. Anthony PY - 2020/2/12 TI - A Virtual Multidisciplinary Care Program for Management of Advanced Chronic Kidney Disease: Matched Cohort Study JO - J Med Internet Res SP - e17194 VL - 22 IS - 2 KW - chronic kidney disease KW - end-stage renal disease KW - online social networking KW - patient education KW - renal dialysis N2 - Background: It is not well established whether a virtual multidisciplinary care program for persons with advanced chronic kidney disease (CKD) can improve their knowledge about their disease, increase their interest in home dialysis therapies, and result in more planned outpatient (versus inpatient) dialysis starts. Objective: We aimed to evaluate the feasibility and preliminary associations of program participation with disease knowledge, home dialysis modality preference, and outpatient dialysis initiation among persons with advanced CKD in a community-based nephrology practice. Methods: In a matched prospective cohort, we enrolled adults aged 18 to 85 years with at least two estimated glomerular filtration rates (eGFRs) of less than 30 mL/min/1.73 m2 into the Cricket Health program and compared them with controls receiving care at the same clinic, matched on age, gender, eGFR, and presence of heart failure and diabetes. The intervention included online education materials, a virtual multidisciplinary team (nurse, pharmacist, social worker, dietician), and patient mentors. Prespecified follow-up time was nine months with extended follow-up to allow adequate time to determine the dialysis start setting. CKD knowledge and dialysis modality choice were evaluated in a pre-post survey among intervention participants. Results: Thirty-seven participants were matched to 61 controls by age (mean 67.2, SD 10.4 versus mean 68.8, SD 9.5), prevalence of diabetes (54%, 20/37 versus 57%, 35/61), congestive heart failure (22%, 8/37 versus 25%, 15/61), and baseline eGFR (mean 19, SD 6 versus mean 21, SD 5 mL/min/1.73 m2), respectively. At nine-month follow-up, five patients in each group started dialysis (P=.62). Among program participants, 80% (4/5) started dialysis as an outpatient compared with 20% (1/5) of controls (OR 6.28, 95% CI 0.69-57.22). In extended follow-up (median 15.7, range 11.7 to 18.1 months), 19 of 98 patients started dialysis; 80% (8/10) of the intervention group patients started dialysis in the outpatient setting versus 22% (2/9) of control patients (hazard ratio 6.89, 95% CI 1.46-32.66). Compared to before participation, patients who completed the program had higher disease knowledge levels (mean 52%, SD 29% versus mean 94%, SD 14% of questions correct on knowledge-based survey, P<.001) and were more likely to choose a home modality as their first dialysis choice (36%, 7/22 versus 68%, 15/22, P=.047) after program completion. Conclusions: The Cricket Health program can improve patient knowledge about CKD and increase interest in home dialysis modalities, and may increase the proportion of dialysis starts in the outpatient setting. UR - http://www.jmir.org/2020/2/e17194/ UR - http://dx.doi.org/10.2196/17194 UR - http://www.ncbi.nlm.nih.gov/pubmed/32049061 ID - info:doi/10.2196/17194 ER - TY - JOUR AU - Adeoye, Moshood Abiodun AU - Adebayo, Oladimeji AU - Abiola, Busayo AU - Iwalokun, Bamidele AU - Tayo, Bamidele AU - Charchar, Fadi AU - Ojo, Akinlolu AU - Cooper, Richard PY - 2020/1/17 TI - The Association Between Selected Molecular Biomarkers and Ambulatory Blood Pressure Patterns in African Chronic Kidney Disease and Hypertensive Patients Compared With Normotensive Controls: Protocol for a Longitudinal Study JO - JMIR Res Protoc SP - e14820 VL - 9 IS - 1 KW - chronic kidney disease KW - cardiovascular disease KW - ambulatory blood pressure N2 - Background: Chronic kidney disease (CKD) is a burgeoning epidemic in sub-Saharan Africa. Abnormal blood pressure variations are prevalent in CKD and potentiate the risk of cardiovascular morbidity and mortality. Certain genetic variants (angiotensin II receptor type 1 1166 A>C and angiotensin-converting enzyme insertion and deletion polymorphisms) and biomarkers such as interleukin?6, tumor necrosis factor, soluble (s) E-selectin, homocysteine, and highly sensitive C-reactive protein have been shown to affect blood pressure variability among non-African CKD, hypertensive. and nonhypertensive CKD population. However, the contributions of the pattern, genetic, and environmental determinants of ambulatory blood pressure in African CKD have not been characterized. Understanding these interactions may help to develop interventions to prevent major cardiovascular events among people with CKD. Objective: The overarching objective of this study is to identify, document, and develop approaches to address related phenomic, genetic, and environmental determinants of ambulatory blood pressure patterns in African CKD and non-CKD hypertensive patients compared with normotensive controls. Methods: This is a longitudinal short-term follow-up study of 200 adult subjects with CKD and 200 each of age-matched hypertensives without CKD and apparently healthy controls. Demographic information, detailed clinical profile, electrocardiography, echocardiography, and 24-hr ambulatory blood pressure measurements will be obtained. Blood samples will be collected to determine albumin-creatinine ratio, fasting plasma glucose, lipid profile, electrolytes, urea and creatinine, C-reactive protein, serum homocysteine, fibroblast growth factor?23, and complete blood count, while 2 mL blood aliquot will be collected in EDTA (ethylenediaminetetraacetic acid) tubes and mixed using an electronic rolling system to prevent blood clots and subsequently used for DNA extraction and genetic analysis. Results: A total of 239 participants have been recruited so far, and it is expected that the recruitment phase will be complete in June 2020. The follow-up phase will continue with data analysis and publications of results. Conclusions: This study will help stratify Nigerian CKD patients phenotypically and genotypically in terms of their blood pressure variations with implications for targeted interventions and timing of medications to improve prognosis. International Registered Report Identifier (IRRID): DERR1-10.2196/14820 UR - https://www.researchprotocols.org/2020/1/e14820 UR - http://dx.doi.org/10.2196/14820 UR - http://www.ncbi.nlm.nih.gov/pubmed/31951214 ID - info:doi/10.2196/14820 ER - TY - JOUR AU - Peng, Suyuan AU - Shen, Feichen AU - Wen, Andrew AU - Wang, Liwei AU - Fan, Yadan AU - Liu, Xusheng AU - Liu, Hongfang PY - 2019/12/10 TI - Detecting Lifestyle Risk Factors for Chronic Kidney Disease With Comorbidities: Association Rule Mining Analysis of Web-Based Survey Data JO - J Med Internet Res SP - e14204 VL - 21 IS - 12 KW - chronic kidney disease KW - association rule mining KW - Behavioral Risk Factor Surveillance System KW - noncommunicable diseases N2 - Background: The rise in the number of patients with chronic kidney disease (CKD) and consequent end-stage renal disease necessitating renal replacement therapy has placed a significant strain on health care. The rate of progression of CKD is influenced by both modifiable and unmodifiable risk factors. Identification of modifiable risk factors, such as lifestyle choices, is vital in informing strategies toward renoprotection. Modification of unhealthy lifestyle choices lessens the risk of CKD progression and associated comorbidities, although the lifestyle risk factors and modification strategies may vary with different comorbidities (eg, diabetes, hypertension). However, there are limited studies on suitable lifestyle interventions for CKD patients with comorbidities. Objective: The objectives of our study are to (1) identify the lifestyle risk factors for CKD with common comorbid chronic conditions using a US nationwide survey in combination with literature mining, and (2) demonstrate the potential effectiveness of association rule mining (ARM) analysis for the aforementioned task, which can be generalized for similar tasks associated with noncommunicable diseases (NCDs). Methods: We applied ARM to identify lifestyle risk factors for CKD progression with comorbidities (cardiovascular disease, chronic pulmonary disease, rheumatoid arthritis, diabetes, and cancer) using questionnaire data for 450,000 participants collected from the Behavioral Risk Factor Surveillance System (BRFSS) 2017. The BRFSS is a Web-based resource, which includes demographic information, chronic health conditions, fruit and vegetable consumption, and sugar- or salt-related behavior. To enrich the BRFSS questionnaire, the Semantic MEDLINE Database was also mined to identify lifestyle risk factors. Results: The results suggest that lifestyle modification for CKD varies among different comorbidities. For example, the lifestyle modification of CKD with cardiovascular disease needs to focus on increasing aerobic capacity by improving muscle strength or functional ability. For CKD patients with chronic pulmonary disease or rheumatoid arthritis, lifestyle modification should be high dietary fiber intake and participation in moderate-intensity exercise. Meanwhile, the management of CKD patients with diabetes focuses on exercise and weight loss predominantly. Conclusions: We have demonstrated the use of ARM to identify lifestyle risk factors for CKD with common comorbid chronic conditions using data from BRFSS 2017. Our methods can be generalized to advance chronic disease management with more focused and optimized lifestyle modification of NCDs. UR - https://www.jmir.org/2019/12/e14204 UR - http://dx.doi.org/10.2196/14204 UR - http://www.ncbi.nlm.nih.gov/pubmed/31821152 ID - info:doi/10.2196/14204 ER - TY - JOUR AU - Therkildsen, Bülow Signe AU - Hansen, Houlind Linda AU - Jensen, Dinesen Laura Emilie AU - Finderup, Jeanette PY - 2019/11/21 TI - A Patient Decision Aid App for Patients With Chronic Kidney Disease: Questionnaire Study JO - JMIR Form Res SP - e13786 VL - 3 IS - 4 KW - mobile phone KW - app KW - patient decision aid KW - dialysis KW - decisional conflict KW - usability N2 - Background: The Dialysis Guide (DG) is a patient decision aid (PDA) available as an app and developed for mobile phones for patients with chronic kidney disease facing the decision about dialysis modality. Objective: The aim of this study was to uncover the applicability of the DG as a PDA. Methods: The respondents completed a questionnaire before and after using the DG. The respondents' decisional conflicts were examined using the Decisional Conflict Scale, and the usability of the app was examined using the System Usability Scale (SUS). The change in decisional conflict was determined with a paired t test. Results: A total of 22 respondents participated and their mean age was 65.05 years; 20 out of 22 (90%) had attended a patient school for kidney disease, and 13 out of 22 (59%) had participated in a conversation about dialysis choice with a health professional. After using the DG, the respondents' decisional conflicts were reduced, though the reduction was not statistically significant (P=.49). The mean SUS score was 66.82 (SD 14.54), corresponding to low usability. Conclusions: The DG did not significantly reduce decisional conflict, though the results indicate that it helped the respondents decide on dialysis modality. Attending a patient school and having a conversation about dialysis modality choice with a health professional is assumed to have had an impact on the decisional conflict before using the DG. The usability of the DG was not found to be sufficient, which might be caused by the respondents? average age. Thus, the applicability of the DG cannot be definitively determined. UR - http://formative.jmir.org/2019/4/e13786/ UR - http://dx.doi.org/10.2196/13786 UR - http://www.ncbi.nlm.nih.gov/pubmed/31750836 ID - info:doi/10.2196/13786 ER - TY - JOUR AU - Shen, Hongxia AU - van der Kleij, J. Rianne M. J. AU - van der Boog, M. Paul J. AU - Chang, Xinwei AU - Chavannes, H. Niels PY - 2019/11/5 TI - Electronic Health Self-Management Interventions for Patients With Chronic Kidney Disease: Systematic Review of Quantitative and Qualitative Evidence JO - J Med Internet Res SP - e12384 VL - 21 IS - 11 KW - eHealth KW - self-management KW - systematic review KW - chronic kidney disease N2 - Background: Chronic kidney disease (CKD) poses a major challenge to public health. In CKD patients, adequate disease self-management has been shown to improve both proximal and distal outcomes. Currently, electronic health (eHealth) interventions are increasingly used to optimize patients? self-management skills. Objective: This study aimed to systematically review the existing evidence regarding the implementation and effectiveness of eHealth self-management interventions for patients with CKD. Methods: Following a search in 8 databases (up to November 2017), quantitative and qualitative data on process and effect outcomes were extracted from relevant studies. Quality was appraised using the Crowe Critical Appraisal Tool; narrative synthesis was performed to analyze the data extracted. Results: Of the 3307 articles retrieved, 24 (comprising 23 studies) were included in this review; of these, almost half were appraised to be of low to moderate quality. There was considerable heterogeneity in the types of interventions used and the outcomes measured. A total of 10 effect and 9 process outcome indicators were identified. The most frequently reported effect outcome indicators were specific laboratory tests and blood pressure (BP), whereas satisfaction was the most frequently reported process outcome indicator. Positive effects were found for proximal outcomes (eg, BP control and medication adherence), and mixed effects were found for more distal outcomes (eg, quality of life). High feasibility, usability, and acceptability of and satisfaction with eHealth self-management interventions were reported. The determinant ability of health care professionals to monitor and, if necessary, anticipate on patient measurements online was mostly cited to influence patients? adherence to interventions. Conclusions: eHealth self-management interventions have the potential to improve disease management and health outcomes. To broaden the evidence base and facilitate intervention upscaling, more detailed descriptions and thorough analysis of the intervention components used are required. In addition, our review reveals that outcomes closely related to the scope and duration of the intervention implemented are most likely to be impacted. For instance, if a 4-week Web-based training to optimize disease management skills is implemented, the outcome perceived control would more likely be affected than kidney function. Although this seems obvious, most studies evaluate only distal outcomes and thereby fail to capture intervention effects that might contribute to long-term health improvement. We advise future researchers to carefully consider their choice of outcomes based on their sensitivity for change. In this way, we ensure that relevant effects are captured and legitimate conclusions are drawn. UR - https://www.jmir.org/2019/11/e12384 UR - http://dx.doi.org/10.2196/12384 UR - http://www.ncbi.nlm.nih.gov/pubmed/31687937 ID - info:doi/10.2196/12384 ER - TY - JOUR AU - Massierer, Daniela AU - Sapir-Pichhadze, Ruth AU - Bouchard, Vanessa AU - Dasgupta, Kaberi AU - Fernandez, Nicolas AU - da Costa, Deborah AU - Ahmed, Sara AU - Fortin, Marie-Chantal AU - Langevin, Rosalie AU - Mayo, Nancy AU - Janaudis-Ferreira, Tania PY - 2019/06/24 TI - Web-Based Self-Management Guide for Kidney Transplant Recipients (The Getting on With Your Life With a Transplanted Kidney Study): Protocol for Development and Preliminary Testing JO - JMIR Res Protoc SP - e13420 VL - 8 IS - 6 KW - self-management KW - kidney transplantation KW - eHealth KW - quality of life KW - exercise KW - physical activity N2 - Background: Although it is well known that compared with dialysis, kidney transplantation improves the quality of life (QoL) of patients with end-stage renal disease, posttransplant recovery of physical health and other aspects of QoL remain well below age- and sex-matched norms. In addition, most transplant recipients are not physically active even years after the transplant and face several barriers to engaging in physical activity (PA). This is of concern as low levels of PA in transplant recipients has been associated with increased risk of mortality and poor graft function. Optimization of QoL needs a team approach involving the patients and the members of the health care team. While members of the health care team are focused on optimizing the biological responses to transplant, patients may have few or no tools at their disposal to engage in behaviors that optimize QoL. To accomplish the need of supporting these patients in the self-management of their condition and to facilitate engagement with PA, new tools tailored to this population are required. Objective: The aim of this protocol study is to develop a Web-based, patient-centered self-management intervention to promote a healthy lifestyle, increase daily PA, and improve QoL in kidney transplant recipients. Methods: We will use the Obesity-Related Behavioral Intervention Trials model for developing behavioral treatments for chronic diseases to guide the proposed project. We will follow a modified version of the iterative 10-step process that was used to develop educational material for people with multiple sclerosis. The development of the intervention will occur in partnership with patients and a multidisciplinary team of clinicians and researchers. A comprehensive needs assessment including data from our pilot study, literature review, and focus groups will be conducted. The focus groups will be conducted with 6 to 10 participants for each type of stakeholders: patients and professional experts to identify areas of concerns of kidney transplant recipients that are appropriate to address through self-management. The areas of concern identified through the assessment needs will be included in the website. Results: This study has received funding from the Kidney Foundation of Canada for 2 years (2018-2020) and was recently granted ethics approval. Investigators have begun conducting the needs assessment described in step 1 of the study. The study is expected to be completed by the end of 2020. Conclusions: This will be the first comprehensive, evidence- and experience-based self-management program for kidney transplant recipients. Once the intervention is developed, we anticipate improvements in patient experience, shared decision making, daily PA, QoL, and, in future studies, improvements in health outcomes and demonstrations of cost savings in posttransplant care. International Registered Report Identifier (IRRID): PRR1-10.2196/13420 UR - http://www.researchprotocols.org/2019/6/e13420/ UR - http://dx.doi.org/10.2196/13420 UR - http://www.ncbi.nlm.nih.gov/pubmed/31237243 ID - info:doi/10.2196/13420 ER - TY - JOUR AU - Khoong, C. Elaine AU - Karliner, Leah AU - Lo, Lowell AU - Stebbins, Marilyn AU - Robinson, Andrew AU - Pathak, Sarita AU - Santoyo-Olsson, Jasmine AU - Scherzer, Rebecca AU - Peralta, A. Carmen PY - 2019/6/7 TI - A Pragmatic Cluster Randomized Trial of an Electronic Clinical Decision Support System to Improve Chronic Kidney Disease Management in Primary Care: Design, Rationale, and Implementation Experience JO - JMIR Res Protoc SP - e14022 VL - 8 IS - 6 KW - chronic kidney disease KW - clinical decision support systems KW - pragmatic clinical trial KW - electronic health records N2 - Background: The diagnosis of chronic kidney disease (CKD) is based on laboratory results easily extracted from electronic health records; therefore, CKD identification and management is an ideal area for targeted electronic decision support efforts. Early CKD management frequently occurs in primary care settings where primary care providers (PCPs) may not implement all the best practices to prevent CKD-related complications. Few previous studies have employed randomized trials to assess a CKD electronic clinical decision support system (eCDSS) that provided recommendations to PCPs tailored to each patient based on laboratory results. Objective: The aim of this study was to report the trial design and implementation experience of a CKD eCDSS in primary care. Methods: This was a 3-arm pragmatic cluster-randomized trial at an academic general internal medicine practice. Eligible patients had 2 previous estimated-glomerular-filtration-rates by serum creatinine (eGFRCr) <60 mL/min/1.73m2 at least 90 days apart. Randomization occurred at the PCP level. For patients of PCPs in either of the 2 intervention arms, the research team ordered triple-marker testing (serum creatinine, serum cystatin-c, and urine albumin-creatinine-ratio) at the beginning of the study period, to be completed when acquiring labs for regular clinical care. The eCDSS launched for PCPs and patients in the intervention arms during a regular PCP visit subsequent to completing the triple-marker testing. The eCDSS delivered individualized guidance on cardiovascular risk-reduction, potassium and proteinuria management, and patient education. Patients in the eCDSS+ arm also received a pharmacist phone call to reinforce CKD-related education. The primary clinical outcome is blood pressure change from baseline at 6 months after the end of the trial, and the main secondary outcome is provider awareness of CKD diagnosis. We also collected process, patient-centered, and implementation outcomes. Results: A multidisciplinary team (primary care internist, nephrologists, pharmacist, and informaticist) designed the eCDSS to integrate into the current clinical workflow. All 81 PCPs contacted agreed to participate and were randomized. Of 995 patients initially eligible by eGFRCr, 413 were excluded per protocol and 58 opted out or withdrew, resulting in 524 patient participants (188 usual care; 165 eCDSS; and 171 eCDSS+). During the 12-month intervention period, 53.0% (178/336) of intervention patient participants completed triple-marker labs. Among these, 138/178 (77.5%) had a PCP appointment after the triple-marker labs resulted; the eCDSS was opened for 73.9% (102/138), with orders or education signed for 81.4% (83/102). Conclusions: Successful integration of an eCDSS into primary care workflows and high eCDSS utilization rates at eligible visits suggest this tailored electronic approach is feasible and has the potential to improve guideline-concordant CKD care. Trial Registration: ClinicalTrials.gov NCT02925962; https://clinicaltrials.gov/ct2/show/NCT02925962 (Archived by WebCite at http://www.webcitation.org/78qpx1mjR) International Registered Report Identifier (IRRID): DERR1-10.2196/14022 UR - http://www.researchprotocols.org/2019/6/e14022/ UR - http://dx.doi.org/10.2196/14022 UR - http://www.ncbi.nlm.nih.gov/pubmed/31199334 ID - info:doi/10.2196/14022 ER - TY - JOUR AU - Beck, Denise AU - Been-Dahmen, Janet AU - Peeters, Mariëlle AU - Grijpma, Willem Jan AU - van der Stege, Heleen AU - Tielen, Mirjam AU - van Buren, Marleen AU - Weimar, Willem AU - Ista, Erwin AU - Massey, Emma AU - van Staa, AnneLoes PY - 2019/03/01 TI - A Nurse-Led Self-Management Support Intervention (ZENN) for Kidney Transplant Recipients Using Intervention Mapping: Protocol for a Mixed-Methods Feasibility Study JO - JMIR Res Protoc SP - e11856 VL - 8 IS - 3 KW - chronic kidney disease KW - evidence-based nursing KW - self-management KW - transplantation N2 - Background: Optimal self-management in kidney transplant recipients is essential for patient and graft survival, reducing comorbidity and health care costs while improving the quality of life. However, there are few effective interventions aimed at providing self-management support after kidney transplantation. Objective: This study aims to systematically develop a nurse-led, self-management (support) intervention for kidney transplant recipients. Methods: The Intervention Mapping protocol was used to develop an intervention that incorporates kidney transplant recipients? and nurses? needs, and theories as well as evidence-based methods. The needs of recipients and nurses were assessed by reviewing the literature, conducting focus groups, individual interviews, and observations (step 1). Based on the needs assessment, Self-Regulation Theory, and the ?5A?s? model, change objectives were formulated (step 2). Evidence-based methods to achieve these objectives were selected and subsequently translated into practical implementation strategies (step 3). Then, program materials and protocols were developed accordingly (step 4). The implementation to test the feasibility and acceptability was scheduled for 2015-2017 (step 5). The last step of Intervention Mapping, evaluation of the intervention, falls outside the scope of this paper (step 6). Results: The intervention was developed to optimize self-management (support) after kidney transplantation and targeted both kidney transplant recipients and nurse practitioners who delivered the intervention. The intervention was clustered into four 15-minute sessions that were combined with regular appointments at the outpatient clinic. Nurses received a training syllabus and were trained in communication techniques based on the principles of Solution-Focused Brief Therapy and Motivational Interviewing; this entailed guiding the patients to generate their own goals and solutions and focus on strengths and successes. Kidney transplant recipients were encouraged to assess self-management challenges using the Self-Management Web and subsequently develop specific goals, action plans, and pursuit skills to solve these challenges. Conclusions: The Intervention Mapping protocol provided a rigorous framework to systematically develop a self-management intervention in which nurses and kidney transplant recipients? needs, evidence-based methods, and theories were integrated. International Registered Report Identifier (IRRID): DERR1-10.2196/11856 UR - https://www.researchprotocols.org/2019/3/e11856/ UR - http://dx.doi.org/10.2196/11856 UR - http://www.ncbi.nlm.nih.gov/pubmed/30821694 ID - info:doi/10.2196/11856 ER - TY - JOUR AU - Dubin, Ruth AU - Rubinsky, Anna PY - 2019/02/06 TI - A Digital Modality Decision Program for Patients With Advanced Chronic Kidney Disease JO - JMIR Form Res SP - e12528 VL - 3 IS - 1 KW - chronic kidney disease KW - end-stage renal disease KW - online social networking KW - patient education KW - renal dialysis N2 - Background: Patient education regarding end-stage renal disease (ESRD) has the potential to reduce adverse outcomes and increase the use of in-home renal replacement therapies. Objective: This study aimed to investigate whether an online, easily scalable education program can improve patient knowledge and facilitate decision making regarding renal replacement therapy options. Methods: We developed a 4-week online, digital educational program that included written information, short videos, and social networking features. Topics included kidney transplant, conservative management, peritoneal dialysis, in-home hemodialysis, and in-center hemodialysis. We recruited patients with advanced chronic kidney disease (stage IV and V) to enroll in the online program, and we evaluated the feasibility and potential impact of the digital program by conducting pre- and postintervention surveys in areas of knowledge, self-efficacy, and choice of ESRD care. Results: Of the 98 individuals found to be eligible for the study, 28 enrolled and signed the consent form and 25 completed the study. The average age of participants was 65 (SD 15) years, and the average estimated glomerular filtration rate was 21 (SD 6) ml/min/1.73 m2. Before the intervention, 32% of patients (8/25) were unable to make an ESRD treatment choice; after the intervention, all 25 participants made a choice. The proportion of persons who selected kidney transplant as the first choice increased from 48% (12/25) at intake to 84% (21/25) after program completion (P=.01). Among modality options, peritoneal dialysis increased as the first choice for 4/25 (16%) patients at intake to 13/25 (52%) after program completion (P=.004). We also observed significant increases in knowledge score (from 65 [SD 56] to 83 [SD 14]; P<.001) and self-efficacy score (from 3.7 [SD 0.7] to 4.3 [SD 0.5]; P<.001). Conclusions: Implementation of a digital ESRD education program is feasible and may facilitate patients? decisions about renal replacement therapies. Larger studies are necessary to understand whether the program affects clinical outcomes. Trial Registration: ClinicalTrials.gov NCT02976220; https://clinicaltrials.gov/ct2/show/NCT02976220 UR - http://formative.jmir.org/2019/1/e12528/ UR - http://dx.doi.org/10.2196/12528 UR - http://www.ncbi.nlm.nih.gov/pubmed/30724735 ID - info:doi/10.2196/12528 ER -