%0 Journal Article %@ 2291-5222 %I JMIR Publications %V 13 %N %P e53586 %T Efficacy, Safety, and Cost-Effectiveness of “Internet + Pharmacy Care” Via the Alfalfa App in Warfarin Therapy Management After Cardiac Valve Replacement: Randomized Controlled Trial %A Qian,Yiyi %A Chen,Weizhao %A Zhou,Bin %A Li,Jiangya %A Guo,Yuanyuan %A Weng,Zhiying %A Zhang,Jinhua %K smartphone %K mobile phone %K alfalfa app %K warfarin %K anticoagulation %K internet + pharmacy care %D 2025 %7 20.5.2025 %9 %J JMIR Mhealth Uhealth %G English %X Background: Anticoagulation management is important in preventing complications in patients undergoing cardiac valve replacement. The development of mobile apps offers new opportunities for the management of long-term anticoagulants. However, there is a lack of randomized controlled trials evaluating the effectiveness, safety, cost-effectiveness, and user demand for internet-based anticoagulation management. Objective: This study aimed to evaluate the efficacy, safety, and cost-effectiveness of a 3-month warfarin dose adjustment mobile app Alfalfa compared to offline management in patients postcardiac valve replacement. We also explored the app’s feasibility on user satisfaction and demand. Methods: This study was a randomized controlled trial with assessments conducted at baseline and at a 3-month follow-up. Participants were eligible if they had been on warfarin therapy for at least 3 months, received warfarin management either through the Alfalfa app or through pharmacist-led anticoagulation outpatient clinic visits, consented to regular follow-ups, and had not experienced serious bleeding or thrombotic events in the 3 months before warfarin treatment. A P value of ≤.05 was considered statistically significant. Results: A total of 405 participants were included in the analysis. The time in therapeutic range was significantly higher in the Alfalfa app group than in the offline group (66.46% vs 46.65%, P<.001). Participants in the Alfalfa app group had a higher monitoring frequency (8.14 vs 4.47, P<.001) and a greater percentage of international normalized ratio values within the target range (896/1660, 53.98% vs 346/899, 38.49%; P<.001) than those in the offline group. In addition, the Alfalfa app group exhibited lower rates of subtherapeutic (235/1660, 14.16% vs 152/899, 16.91%; P<.05) and extreme subtherapeutic international normalized ratio values (273/1660, 16.45% vs 186/899, 20.69%; P<.05) than the offline group. However, the incidence of minor bleeding was higher in the Alfalfa app group (12/204, 5.9% vs 3/201, 1.5%; P=.02). In terms of cost-effectiveness, the Alfalfa app group had a significantly lower average cost per test (42.37 vs 78.3, P<.001), average time per test (47.42 vs 90.74, P<.001), and cost-effectiveness ratio (385.9 vs 662.9) than the offline group. A total of 86 participants completed the satisfaction questionnaire, and the vast majority of participants expressed high levels of satisfaction with the Alfalfa App, while also providing further suggestions for improvement. Conclusions: The integration of “Internet+Pharmacy Care” using the Alfalfa App can improve the effectiveness of warfarin anticoagulation management in patients following heart valve surgery. The Alfalfa app provides a more efficient, secure, and cost-effective solution to warfarin management than traditional offline methods. Trial Registration: Chinese Clinical Trial Registry ChiCTR1900021920; https://www.chictr.org.cn/showproj.html?proj=36832 %R 10.2196/53586 %U https://mhealth.jmir.org/2025/1/e53586 %U https://doi.org/10.2196/53586 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 27 %N %P e70587 %T Multimodal Visualization and Explainable Machine Learning–Driven Markers Enable Early Identification and Prognosis Prediction for Symptomatic Aortic Stenosis and Heart Failure With Preserved Ejection Fraction After Transcatheter Aortic Valve Replacement: Multicenter Cohort Study %A Wang,Jun %A Zhu,Jiajun %A Li,Hui %A Wu,Shili %A Li,Siyang %A Yao,Zhuoya %A Zhu,Tongjian %A Tang,Bi %A Tang,Shengxing %A Liu,Jinjun %+ , Department of Cardiology, The First Affiliated Hospital of Bengbu Medical University, No. 287, Changhuai Road, Longzihu District, Bengbu, 233030, China, 86 05523086107, byyfyliujinjun@163.com %K machine learning %K interpretable models %K heart failure with preserved ejection fraction %K symptomatic aortic stenosis %K transcatheter aortic valve replacement %K major adverse cardiovascular and cerebrovascular events. %D 2025 %7 1.5.2025 %9 Original Paper %J J Med Internet Res %G English %X Background: Currently, there is a paucity of literature addressing personalized risk stratification using multimodal data in patients with symptomatic aortic stenosis and heart failure with preserved ejection fraction (HFpEF) following transcatheter aortic valve replacement (TAVR). Objective: This study aimed to enhance the performance of risk assessment models in this patient population by developing a predictive model for adverse outcomes using various machine learning (ML) techniques. Methods: This multicenter cohort study included 326 patients diagnosed with severe AS and HFpEF who underwent TAVR between January 2017 and December 2023. Patients were allocated to training (n=195) and independent validation (n=131) sets based on hospital affiliation. A dual-phase feature selection process, combining least absolute shrinkage and selection operator logistic regression and the Boruta algorithm, was used to identify relevant variables from the multimodal dataset. A total of 5 ML model-decision trees, K-nearest neighbors, random forest, support vector machine, and extreme gradient boosting were used to construct a visualization and explainable predictive framework to elucidate model decision-making processes. Results: The primary features identified included age, N-terminal pro-brain natriuretic peptide, fasting blood glucose, triglyceride/high-density lipoprotein cholesterol ratio, triglyceride glucose index, triglyceride glucose-BMI index, atherogenic index of plasma index, and Apolipoprotein B. Among the 5 models, the support vector machine demonstrated the best predictive performance for major adverse cardiovascular and cerebrovascular events in patients with severe AS and HFpEF following TAVR, achieving an area under the curve of 0.756 (95% CI 0.631-0.881) in the independent validation set. The model exhibited good calibration and robust predictive power in both training and validation sets and demonstrated the highest net benefit in decision curve analysis compared to other models. To extract significant variables influencing the algorithm and ensure model appropriateness, we interpreted cohort and personalized model predictions using Shapley Additive Explanations values. Conclusions: Our ML-based multimodal model, incorporating 8 readily accessible predictors, demonstrated robust predictive capability for 12 months of major adverse cardiovascular and cerebrovascular events risk. This model can be used to identify high-risk individuals with AS and HFpEF following TAVR, potentially aiding in risk stratification and personalized treatment strategies. %M 40310672 %R 10.2196/70587 %U https://www.jmir.org/2025/1/e70587 %U https://doi.org/10.2196/70587 %U http://www.ncbi.nlm.nih.gov/pubmed/40310672 %0 Journal Article %@ 2561-7605 %I JMIR Publications %V 7 %N %P e58980 %T Enhancing Frailty Assessments for Transcatheter Aortic Valve Replacement Patients Using Structured and Unstructured Data: Real-World Evidence Study %A Mardini,Mamoun T %A Bai,Chen %A Bavry,Anthony A %A Zaghloul,Ahmed %A Anderson,R David %A Price,Catherine E Crenshaw %A Al-Ani,Mohammad A Z %K transcatheter aortic valve replacement %K frailty %K cardiology %K machine learning %K TAVR %K minimally invasive surgery %K cardiac surgery %K real-world data %K topic modeling %K clinical notes %K electronic health record %K EHR %D 2024 %7 27.11.2024 %9 %J JMIR Aging %G English %X Background: Transcatheter aortic valve replacement (TAVR) is a commonly used treatment for severe aortic stenosis. As degenerative aortic stenosis is primarily a disease afflicting older adults, a frailty assessment is essential to patient selection and optimal periprocedural outcomes. Objective: This study aimed to enhance frailty assessments of TAVR candidates by integrating real-world structured and unstructured data. Methods: This study analyzed data from 14,000 patients between January 2018 and December 2019 to assess frailty in TAVR patients at the University of Florida. Frailty was identified using the Fried criteria, which includes weight loss, exhaustion, walking speed, grip strength, and physical activity. Latent Dirichlet allocation for topic modeling and Extreme Gradient Boosting for frailty prediction were applied to unstructured clinical notes and structured electronic health record (EHR) data. We also used least absolute shrinkage and selection operator regression for feature selection. Model performance was rigorously evaluated using nested cross-validation, ensuring the generalizability of the findings. Results: Model performance was significantly improved by combining unstructured clinical notes with structured EHR data, achieving an area under the receiver operating characteristic curve of 0.82 (SD 0.07), which surpassed the EHR-only model’s area under the receiver operating characteristic curve of 0.64 (SD 0.08). The Shapley Additive Explanations analysis found that congestive heart failure management, back problems, and atrial fibrillation were the top frailty predictors. Additionally, the latent Dirichlet allocation topic modeling identified 7 key topics, highlighting the role of specific medical treatments in predicting frailty. Conclusions: Integrating unstructured clinical notes and structured EHR data led to a notable enhancement in predicting frailty. This method shows great potential for standardizing frailty assessments using real-world data and improving patient selection for TAVR. %R 10.2196/58980 %U https://aging.jmir.org/2024/1/e58980 %U https://doi.org/10.2196/58980 %0 Journal Article %@ 2561-1011 %I JMIR Publications %V 8 %N %P e60503 %T Identifying the Severity of Heart Valve Stenosis and Regurgitation Among a Diverse Population Within an Integrated Health Care System: Natural Language Processing Approach %A Xie,Fagen %A Lee,Ming-sum %A Allahwerdy,Salam %A Getahun,Darios %A Wessler,Benjamin %A Chen,Wansu %+ Department of Research and Evaluation, Kaiser Permanente Southern California, 100 S Los Robles Ave, 2nd Floor, Pasadena, CA, 91101, United States, 1 6265643294, fagen.xie@kp.org %K echocardiography report %K heart valve %K stenosis %K regurgitation %K natural language processing %K algorithm %D 2024 %7 30.9.2024 %9 Original Paper %J JMIR Cardio %G English %X Background: Valvular heart disease (VHD) is a leading cause of cardiovascular morbidity and mortality that poses a substantial health care and economic burden on health care systems. Administrative diagnostic codes for ascertaining VHD diagnosis are incomplete. Objective: This study aimed to develop a natural language processing (NLP) algorithm to identify patients with aortic, mitral, tricuspid, and pulmonic valve stenosis and regurgitation from transthoracic echocardiography (TTE) reports within a large integrated health care system. Methods: We used reports from echocardiograms performed in the Kaiser Permanente Southern California (KPSC) health care system between January 1, 2011, and December 31, 2022. Related terms/phrases of aortic, mitral, tricuspid, and pulmonic stenosis and regurgitation and their severities were compiled from the literature and enriched with input from clinicians. An NLP algorithm was iteratively developed and fine-trained via multiple rounds of chart review, followed by adjudication. The developed algorithm was applied to 200 annotated echocardiography reports to assess its performance and then the study echocardiography reports. Results: A total of 1,225,270 TTE reports were extracted from KPSC electronic health records during the study period. In these reports, valve lesions identified included 111,300 (9.08%) aortic stenosis, 20,246 (1.65%) mitral stenosis, 397 (0.03%) tricuspid stenosis, 2585 (0.21%) pulmonic stenosis, 345,115 (28.17%) aortic regurgitation, 802,103 (65.46%) mitral regurgitation, 903,965 (73.78%) tricuspid regurgitation, and 286,903 (23.42%) pulmonic regurgitation. Among the valves, 50,507 (4.12%), 22,656 (1.85%), 1685 (0.14%), and 1767 (0.14%) were identified as prosthetic aortic valves, mitral valves, tricuspid valves, and pulmonic valves, respectively. Mild and moderate were the most common severity levels of heart valve stenosis, while trace and mild were the most common severity levels of regurgitation. Males had a higher frequency of aortic stenosis and all 4 valvular regurgitations, while females had more mitral, tricuspid, and pulmonic stenosis. Non-Hispanic Whites had the highest frequency of all 4 valvular stenosis and regurgitations. The distribution of valvular stenosis and regurgitation severity was similar across race/ethnicity groups. Frequencies of aortic stenosis, mitral stenosis, and regurgitation of all 4 heart valves increased with age. In TTE reports with stenosis detected, younger patients were more likely to have mild aortic stenosis, while older patients were more likely to have severe aortic stenosis. However, mitral stenosis was opposite (milder in older patients and more severe in younger patients). In TTE reports with regurgitation detected, younger patients had a higher frequency of severe/very severe aortic regurgitation. In comparison, older patients had higher frequencies of mild aortic regurgitation and severe mitral/tricuspid regurgitation. Validation of the NLP algorithm against the 200 annotated TTE reports showed excellent precision, recall, and F1-scores. Conclusions: The proposed computerized algorithm could effectively identify heart valve stenosis and regurgitation, as well as the severity of valvular involvement, with significant implications for pharmacoepidemiological studies and outcomes research. %M 39348175 %R 10.2196/60503 %U https://cardio.jmir.org/2024/1/e60503 %U https://doi.org/10.2196/60503 %U http://www.ncbi.nlm.nih.gov/pubmed/39348175 %0 Journal Article %@ 2561-1011 %I JMIR Publications %V 5 %N 2 %P e23464 %T A Patient Decision Aid for Anticoagulation Therapy in Patients With Nonvalvular Atrial Fibrillation: Development and Pilot Study %A de Castro,Kim Paul %A Chiu,Harold Henrison %A De Leon-Yao,Ronna Cheska %A Almelor-Sembrana,Lorraine %A Dans,Antonio Miguel %+ Department of Medicine, Philippine General Hospital, University of the Philippines, Taft Avenue, Manila, Philippines, 63 9989563472, kimpauldecastro@gmail.com %K shared decision-making %K patient decision aid %K atrial fibrillation %K anticoagulation %K stroke prevention %K mHealth %K mobile health %D 2021 %7 12.8.2021 %9 Original Paper %J JMIR Cardio %G English %X Background: Atrial fibrillation (AF) is one of the most common predisposing factors for ischemic stroke worldwide. Because of this, patients with AF are prescribed anticoagulant medications to decrease the risk. The availability of different options for oral anticoagulation makes it difficult for some patients to decide a preferred choice of medication. Clinical guidelines often recommend enhancing the decision-making process of patients by increasing their involvement in health decisions. In particular, the use of patient decision aids (PDAs) in patients with AF was associated with increased knowledge and increased likelihood of making a choice. However, the majority of available PDAs are from Western countries. Objective: We aimed to develop and pilot test a PDA to help patients with nonvalvular AF choose an oral anticoagulant for stroke prevention in the local setting. Outcomes were (1) reduction in patient decisional conflict, (2) improvement in patient knowledge, and (3) patient and physician acceptability. Methods: We followed the International Patient Decision Aid Standards (IPDAS) to develop a mobile app–based PDA for anticoagulation therapy in patients with nonvalvular AF. Focus group discussions identified decisional needs, which were subsequently incorporated into the PDA to compare choices for anticoagulation. Based on recommendations, the prototype PDA was rendered by at least 30 patients and 30 physicians. Decisional conflict and patient knowledge were tested before and after the PDA was implemented. Patient acceptability and physician acceptability were measured after each encounter. Results: Anticoagulant options were compared by the PDA using three factors that were identified (impact on stroke and bleeding risk, and price). The comparisons were presented as tables and graphs. The prototype PDA was rendered by 30 doctors and 37 patients for pilot testing. The mean duration of the encounters was 15 minutes. The decisional conflict score reduced by 35 points (100-point scale; P<.001). The AF knowledge score improved from 10 to 15 (P<.001). The PDA was acceptable for both patients and doctors. Conclusions: Our study showed that an app-based PDA for anticoagulation therapy in patients with nonvalvular AF (1) reduced patient decisional conflict, (2) improved patient knowledge, and (3) was acceptable to patients and physicians. A PDA is potentially acceptable and useful in our setting. A randomized controlled trial is warranted to test its effectiveness compared to usual care. PDAs for other conditions should also be developed. %M 34385138 %R 10.2196/23464 %U https://cardio.jmir.org/2021/2/e23464 %U https://doi.org/10.2196/23464 %U http://www.ncbi.nlm.nih.gov/pubmed/34385138 %0 Journal Article %@ 2561-1011 %I JMIR Publications %V 2 %N 1 %P e9 %T Remote Monitoring of Patients Undergoing Transcatheter Aortic Valve Replacement: A Framework for Postprocedural Telemonitoring %A Hermans,Mathilde C %A Van Mourik,Martijn S %A Hermens,Hermie J %A Baan Jr,Jan %A Vis,Marije M %+ Heart Centre, Academic Medical Centre, University of Amsterdam, PO Box 22660, Amsterdam, 1100 DD, Netherlands, 31 5666555, m.m.vis@amc.uva.nl %K transcatheter aortic valve replacement %K postoperative care %K electrocardiography %K telemonitoring %K telemedicine %D 2018 %7 16.03.2018 %9 Original Paper %J JMIR Cardio %G English %X Background: The postprocedural trajectory of patients undergoing transcatheter aortic valve replacement (TAVR) involves in-hospital monitoring of potential cardiac rhythm or conduction disorders and other complications. Recent advances in telemonitoring technologies create opportunities to monitor electrocardiogram (ECG) and vital signs remotely, facilitating redesign of follow-up trajectories. Objective: This study aimed to outline a potential set-up of telemonitoring after TAVR. Methods: A multidisciplinary team systematically framed the envisioned telemonitoring scenario according to the intentions, People, Activities, Context, Technology (iPACT) and Functionality, Interaction, Content, Services (FICS) methods and identified corresponding technical requirements. Results: In this scenario, a wearable sensor system is used to continuously transmit ECG and contextual data to a central monitoring unit, allowing remote follow-up of ECG abnormalities and physical deteriorations. Telemonitoring is suggested as an alternative or supplement to current in-hospital monitoring after TAVR, enabling early hospital dismissal in eligible patients and accessible follow-up prolongation. Together, this approach aims to improve rehabilitation, enhance patient comfort, optimize hospital capacity usage, and reduce overall costs. Required technical components include continuous data acquisition, real-time data transfer, privacy-ensured storage, automatic event detection, and user-friendly interfaces. Conclusions: The suggested telemonitoring set-up involves a new approach to patient follow-up that could bring durable solutions for the growing scarcities in health care and for improving health care quality. To further explore the potential and feasibility of post-TAVR telemonitoring, we recommend evaluation of the overall impact on patient outcomes and of the safety, social, ethical, legal, organizational, and financial factors. %M 31758782 %R 10.2196/cardio.9075 %U http://cardio.jmir.org/2018/1/e9/ %U https://doi.org/10.2196/cardio.9075 %U http://www.ncbi.nlm.nih.gov/pubmed/31758782