@Article{info:doi/10.2196/70414, author="Maw, M. Anna and Wright, C. Garth and Bean, R. Meagan and Allen, A. Larry and Matlock, D. Daniel and Cervantes, Lilia and Glasgow, E. Russell and Huebschmann, G. Amy and Colborn, L. Kathryn and Houston, K. Thomas and Trinkley, E. Katie", title="Frequency and Predictors of Virtual Visits in Patients With Heart Failure Within a Large Health System: Retrospective Cohort Study", journal="J Med Internet Res", year="2025", month="Aug", day="12", volume="27", pages="e70414", keywords="telehealth", keywords="virtual care", keywords="health equity", keywords="heart failure", keywords="digital technology", abstract="Background: Virtual care interventions have the potential to improve access to care and serial medication intensification for patients with chronic heart failure with reduced ejection fraction (HFrEF). However, concerns remain that these interventions might unintentionally create or widen existing disparities in care delivery and patient outcomes. Objective: This study aimed to characterize the health care use patterns of patients who have HFrEF, including specialty type and frequency of in-person and virtual visits. Methods: We conducted a retrospective cohort study of patients with HFrEF within a large health system. Inclusion criteria were patients alive with an ejection fraction ?40\% as of September 1, 2021, and at least one virtual or in-person outpatient visit to a primary care or cardiology clinician in the prior year. Descriptive statistics were used to evaluate baseline patient demographics and clinical use data and outcomes. Univariate analyses were performed both with virtual visits as a variable (received or did not receive) using the chi-square test for association and as a discrete outcome using the Wilcoxon rank-sum test to capture potentially important predictor variables that could influence use or frequency of using virtual visits. The primary outcome of interest was the odds of at least one virtual visit during the 1-year evaluation period from 2021 to 2022. Descriptive statistics were used to evaluate baseline patient demographics and care use. A logistic regression model was used to model at least one primary care or cardiology virtual visit. Results: A total of 8481 patients were included in the analysis. The mean age was 65.9 years (SD 15.1), 5672 (66.9\%) patients were male and 6608 (77.9\%) patients were non-Hispanic White. The majority of patients had no cardiology (7938/8481, 93.6\%) or primary care (7955/8481, 93.8\%) virtual visits during the evaluation period. Multivariable logistic regression showed significantly higher odds of having at least one virtual visit for patients with certain digital access---for example, email on file (odds ratio [OR] 9.3, P?.001), cell phone on file (OR 2.9, P?.001), and active electronic health record patient portal (OR 2.8, P?.001)---than those without. Age, race, ethnicity, rurality, and Social Vulnerability Index were not associated with virtual visits. Conclusions: Only a minority of patients with HFrEF were seen via virtual visits. Patients who regularly used digital technology were more likely to have virtual visits. Patients were more likely to be seen in a cardiology clinic than by a primary care provider. Although there was no evidence of an association between social determinants of health factors like race, ethnicity, or rurality with digital divide indicators, these findings should be interpreted with caution given the limitations of these data. Future studies should aim to replicate the findings of this study and explore ways to enhance the effective and equitable use of virtual visits. ", doi="10.2196/70414", url="https://www.jmir.org/2025/1/e70414" } @Article{info:doi/10.2196/69582, author="Schwerdtfeger, Richard Andreas and Tatschl, Martin Josef and Rominger, Christian", title="Effectiveness of 2 Just-in-Time Adaptive Interventions for Reducing Stress and Stabilizing Cardiac Autonomic Function: Microrandomized Trials", journal="J Med Internet Res", year="2025", month="Aug", day="7", volume="27", pages="e69582", keywords="cardiac vagal regulation", keywords="just-in-time adaptive intervention", keywords="JITAI", keywords="microrandomized trial", keywords="resonance breathing", keywords="mindful breathing", abstract="Background: Heart rate variability (HRV) indicates brain-body interaction and has been associated with a variety of mental and physical health indicators. Transient reductions in HRV, independent of bodily movement (so-called additional HRV reduction [AddHRVr]), may reflect moments of psychophysiological vulnerability. This metric is quantified by regressing bodily movement on the root mean square of successive differences and identifying reductions <0.5 SD of the predicted value in real time in everyday life. Objective: We aimed to apply this measure using wearables in everyday life to trigger low-threshold, 1-minute just-in-time adaptive interventions (JITAIs) to stabilize autonomic function and relieve perceived stress and ruminative thoughts. Thus, we compared moments of AddHRVr with random points in time with respect to the effects of brief JITAIs. Methods: In 2 preregistered microrandomized trials, participants underwent a 1-day calibration period to derive individualized trigger settings and then received AddHRVr-triggered and random prompts throughout the following 3 days, asking for perceived stress and rumination. In study 1, participants (N=60) underwent a slow breathing intervention (0.1 Hz slow-paced breathing) following each prompt, and in study 2, participants (N=50) were microrandomized to both an external attention (ie, focusing attention on a nonliving object) and a mindful breathing intervention. HRV was assessed before, during, and following each intervention by means of the root mean squares of successive differences, SD of normal-to-normal beats, and high- and low-frequency HRV. Participants also reported on perceived stress and ruminative thoughts before and after the interventions. Results: Following interventions in both studies, perceived stress and ruminative thoughts significantly declined irrespective of the kind of prompt and intervention (study 1---perceived stress: b=--0.12; P<.001 and ruminative thoughts: b=--0.11; P<.001 and study 2---perceived stress: b=--0.07; P=.01 and ruminative thoughts: b=--0.10; P=.002). AddHRVr-triggered prompts resulted in a stronger increase in HRV during the slow-paced breathing (b=0.08; P=.02) and mindful breathing interventions (b=0.10; P=.03), and elevated HRV metrics in a time frame of 10 minutes following the interventions in contrast to random prompts (study 1: b=0.12; P<.001 and study 2: b=0.10; P=.03). Conclusions: Both studies show, for the first time, that transient, nonmetabolic reductions in HRV (AddHRVr) can be used to trigger brief JITAIs in real time by wearables to stabilize autonomic function, thus potentially promoting cardiac health. The findings suggest that although psychological benefits emerged independent of the cardiac autonomic state, slow-paced breathing or directing attention for 1 minute to either the own body or nonliving objects seemed to boost autonomic flexibility when HRV was compromised. Trial Registration: German Clinical Trial Register DRKS00035684; https://www.drks.de/search/de/trial/DRKS00035684 and DRKS00035685; https://www.drks.de/search/de/trial/DRKS00035685 ", doi="10.2196/69582", url="https://www.jmir.org/2025/1/e69582", url="http://www.ncbi.nlm.nih.gov/pubmed/40773285" } @Article{info:doi/10.2196/76850, author="Nguyen, Thuy Quyen and Tran, Viet An and Nguyen, The Bao and Nguyen, Thai Hoa and Thai, Hong Nhung Thi and Phan, Huu Hen", title="Lipid Profile and Apolipoprotein B Serum Levels in the Vietnamese Population With Newly Diagnosed Elevated Low-Density Lipoprotein Cholesterol and Association With the Single-Nucleotide Variant rs676210: Cross-Sectional Study", journal="JMIR Cardio", year="2025", month="Aug", day="7", volume="9", pages="e76850", keywords="APOB rs676210 polymorphism", keywords="APOB", keywords="apolipoprotein B", keywords="lipid profile", keywords="elevated LDL-C", keywords="low-density lipoprotein cholesterol", abstract="Background: Apolipoprotein B (APOB) rs676210 polymorphism has been associated with altered lipid metabolism and cardiovascular risk in various populations; however, data from Vietnamese populations remain limited. Objective: This study aimed to investigate the association of the APOB rs676210 variant with lipid profiles among Vietnamese individuals newly diagnosed with elevated low-density lipoprotein cholesterol (LDL-C). Methods: A cross-sectional study was conducted among 69 Vietnamese adults newly diagnosed with elevated LDL-C (?130 mg/dL) at a tertiary hospital in Southern Vietnam. Participants were genotyped for APOB rs676210 using real-time polymerase chain reaction (PCR) with allele-specific probes. Lipid profile components, including LDL-C, high-density lipoprotein cholesterol (HDL-C), non--HDL-C, and ApoB, were compared across genotype groups (AA vs GA/GG) and alleles (A vs G). Statistical analyses involved t tests, chi-square tests, and multivariable linear regression adjusted for age, sex, the BMI, and diabetes. P<.05 was considered statistically significant. Results: Of the 69 participants, 32 (46.4\%) carried the AA genotype, while 37 (53.6\%) carried the GA or the GG genotype. The AA genotype was associated with significantly higher LDL-C (mean 5.19, SD 0.95, vs mean 4.37, SD 0.97, mmol/L; P<.001), non--HDL-C (mean 5.94, SD 1.08, vs mean 5.31, SD 1.22 mmol/L; P=.03), and ApoB (mean 149.5, SD 26.3, vs mean 136.9, SD 15.2, mg/dL; P=.02) and lower HDL-C (mean 1.26, SD 0.31, vs mean 1.44, SD 0.39, mmol/L; P=.03) compared to the GA/GG genotype. Allele-based analysis showed that carriers of the A allele (98/138, 71\%) also had higher LDL-C (mean 4.91, SD 1.02, vs mean 4.36, SD 0.97, mmol/L; P=.004) and ApoB (mean 145.6, SD 23.2, vs mean 135.9, SD 16.0, mg/dL; P=.02) than G allele carriers (40/138, 29\%). These associations remained significant after multivariate adjustment. Conclusions: APOB rs676210 polymorphism is associated with significant differences in lipid profiles among Vietnamese adults with elevated LDL-C. Specifically, the A allele and the AA genotype confer a more atherogenic profile, suggesting potential utility as a genetic marker in lipid screening and personalized cardiovascular risk management in this population. ", doi="10.2196/76850", url="https://cardio.jmir.org/2025/1/e76850", url="http://www.ncbi.nlm.nih.gov/pubmed/40773287" } @Article{info:doi/10.2196/64877, author="McKay, Nilufeur and Saunders, Rosemary and Metcalfe, Helene and Robinson, Sue and Palamara, Peter and Steer, Kellie and Yoo, Jeannie and Ranogajec, Miles and Whitehead, Lisa and Ewens, Beverley", title="Evaluation of a Virtual Home Health Heart Failure Program: Mixed Methods Study", journal="JMIR Cardio", year="2025", month="Jul", day="23", volume="9", pages="e64877", keywords="heart failure", keywords="patient care team", keywords="telemedicine", keywords="virtual health", keywords="mixed methods study", keywords="healthcare systems", keywords="supportive care", keywords="virtual healthcare", keywords="monitoring support program", keywords="quality of life", keywords="Australian", keywords="value-based healthcare", abstract="Background: Heart failure is a prevalent and debilitating condition, affecting millions globally and imposing a significant burden on patients, families, and health care systems. Despite advancements in medical treatments, the gap in effective, continuous, and personalized supportive care remains glaringly evident. To address this pressing issue, virtual health care services delivered by interdisciplinary teams represent a promising solution. Understanding the outcomes and experience of remote monitoring--enabled interdisciplinary chronic disease management programs can inform resource allocation and health care policy decisions. Objective: The purpose of this study was to evaluate the clinical and behavioral outcomes of patients undertaking a Virtual Home Health Heart Failure Program (VHHHFP) and explore the experiences of patients and health care practitioners (HCPs). Methods: The VHHHFP is a virtual postdischarge support service for patients with heart failure that includes an intensive 3-month period followed by a maintenance period delivered by an interdisciplinary team. A mixed methods study was conducted with patients and HCPs. Self-reported outcome data (KCCQ-12 [Kansas City Cardiomyopathy Questionnaire-12], PHQ-4 [Patient Health Questionnaire-4], PAM-13 [Patient Activation Measure-13], and PREMs [Patient Reported Experience Measures]) were obtained from the records of patients (N=49) who completed the intensive phase of the VHHHFP, and interviews were conducted with patients (n=9) and HCPs (n=6). A paired t test was used to compare quantitative data before and after the 3-month intervention, and a thematic qualitative analysis was undertaken of interview data. Results: Thirty-one of the 55 (77.5\%) patients completed the baseline and 3-month follow-up KCCQ-12 assessment. The mean KCCQ-12 summary score at 3 months was 72.20 (SD 20.2), which was significantly higher than the mean summary score at baseline of 50.51 (SD 17.59; P<.001). These findings were similar for the KCCCQ-12 subscales: physical limitations (mean 47.09, SD 29.7 and mean 69.43, SD 22.6; P<.001), quality of life (mean 43.75, SD 21.7 and mean 62.91, SD 25.7; P<.001), symptom frequency (median 60.40, IQR 1-100 and median 91.70, IQR 35.40; P<.001), and social limitation (median 50.0, IQR 1-100 and median 82.50, IQR 32.50; P<.001). The PHQ-4 measure of psychological health was completed by 32 (80\%) patients. The median scores at baseline and follow-up for total distress (median 1.50, IQR 0-7 and median 0.0, IQR 0-8; P<.02), and the anxiety subscale (median 1.0, IQR 0-6 and median 0.0, IQR 0-4; P<.02) reduced over time. Six hospital admissions were recorded (10.2\% of 49 patients) within 30 days. Nine patient interviews aligned with the value-based health care (VBHC) Capability, Comfort, and Calm (CCC) framework. Three themes were identified, which are as follows: (1) enhanced patient capability, (2) improved patient comfort, and (3) positive influences on calm. Six health care professionals shared experiences of the VHHHFP, with three emerging themes: (1) improved patient capability through shared decision-making, (2) improving capability through care practices, and (3) promoting comfort and calm through virtual coordination and collaboration. Conclusions: The use of technologies to support the management of HF is an area of growth. This study contributes to the understanding of how remote patient monitoring with interdisciplinary chronic disease support, integrated into an existing system, can improve clinical outcomes for patients. ", doi="10.2196/64877", url="https://cardio.jmir.org/2025/1/e64877" } @Article{info:doi/10.2196/71937, author="Lam, Sing Chun and Hua, Rong and Loong, Ho-Fung Herbert and Ngan, Chun-Kit and Cheung, Ting Yin", title="Association Between Comorbidity Clusters and Mortality in Patients With Cancer: Predictive Modeling Using Machine Learning Approaches of Data From the United States and Hong Kong", journal="JMIR Cancer", year="2025", month="Jul", day="16", volume="11", pages="e71937", keywords="comorbidity", keywords="multimorbidity", keywords="machine learning", keywords="cluster", keywords="clustering", keywords="cancer", keywords="mortality", keywords="oncology", keywords="multiple chronic conditions", keywords="metabolic", abstract="Background: Patients with cancer and cancer survivors often experience multiple chronic health conditions, which can impact symptom burden and treatment outcomes. Despite the high prevalence of multimorbidity, research on cancer prognosis has predominantly focused on cancers in isolation. There is growing interest in machine learning techniques for cancer studies. However, these methods have not been applied in the context of supportive care for patients with cancer who have multimorbidity. Furthermore, few studies have investigated the associations between comorbidity clusters and mortality outcomes. Objective: This study investigated comorbidity clusters among patients with cancer using machine learning and examined their associations with mortality outcomes in two large representative samples from the United States and Hong Kong. Methods: This study used data from the National Health and Nutrition Examination Survey (NHANES) and the Hospital Authority Data Collaboration Laboratory (HADCL). Participants aged ?20 years with a history of cancer were included. The study used a two-step framework to identify clusters of comorbidities in NHANES. In the first step, we used four machine learning techniques, including the Bernoulli mixture model and partition-based methods, to cluster the comorbidities. In the second step, domain experts reviewed and ranked the identified clusters to ensure clinical relevance. The clusters that had the highest average rank were selected for further analysis. The associations between comorbidity clusters and mortality outcomes were analyzed using Cox proportional hazards models. We conducted an external validation to evaluate the generalizability of the clusters identified in the NHANES cohort and their associations with mortality using HADCL. The same number of clusters was replicated based on the distinctive patterns and distribution of comorbidities observed within each cluster. Results: The study included 4390 participants in NHANES and 12,484 participants in HADCL. Four comorbidity clusters were identified: low comorbidity, metabolic, cardiovascular disease (CVD), and respiratory. In NHANES, participants in the respiratory cluster had the highest risk of all-cause mortality (adjusted hazard ratio [aHR] 1.62, 95\% CI 1.26?2.08; P<.001), followed by the CVD cluster (aHR 1.50, 95\% CI 1.26?1.80; P<.001) compared to the low comorbidity cluster. The 3 clusters were associated with higher risks of CVD-related mortality (aHR 1.48?3.05, 95\% CI 1.14-4.07; P<.003). The effects of comorbidity clusters on mortality were modified by income-to-poverty ratio (P for interaction=.04), diet quality (P for interaction=.02), and cancer prognosis (P for interaction=.005). In the HADCL (validation) cohort, participants in the respiratory and CVD clusters had a higher risk of all-cause mortality. Conclusions: High comorbidity burden clusters showed increased all-cause and CVD-related mortality in patients with cancer. These findings highlight the significance of considering comorbidity burden in cancer care. Machine learning approaches can provide valuable insights into complex multimorbidity profiles. Further research is needed to deepen understanding of the relationships between multimorbidity and cancer-specific outcomes. ", doi="10.2196/71937", url="https://cancer.jmir.org/2025/1/e71937" } @Article{info:doi/10.2196/73389, author="Kim, Myung-Rho and Shaikh, Taha and Wang, Shawn and Taylor, Spencer and Goel, Vidhani and Khetarpal, Kaur Banveet and Ahsan, Chowdhury and Batra, Kavita", title="A Higher-Than-Standard-Intensity International Normalized Ratio Goal for Patients Undergoing Mechanical Aortic Valve Replacement With Additional Thrombotic Risk Factors: Protocol for a Systematic Review and Meta-Analysis", journal="JMIR Res Protoc", year="2025", month="Jul", day="10", volume="14", pages="e73389", keywords="mechanical aortic valve replacement", keywords="MAVR", keywords="thromboembolic risk factors", keywords="thromboembolism", keywords="anticoagulation", keywords="international normalized ratio", keywords="warfarin", keywords="Coumadin", keywords="vitamin K antagonist", abstract="Background: Lifelong anticoagulation therapy with vitamin K antagonists is recommended following mechanical aortic valve replacement (MAVR) to prevent valve thrombosis. Current guidelines recommend a standard international normalized ratio (INR) of 2.5 for patients with MAVR without additional thromboembolic risk factors, and a higher INR goal of 3.0 for those with conditions such as atrial fibrillation, prior thromboembolism, or left ventricular dysfunction. However, limited clinical evidence exists to guide anticoagulation intensity in this high-risk subgroup, necessitating a systematic review. Objective: We aimed to assess the safety and efficacy of higher-intensity INR goals (>3.0) compared to standard-intensity goals (approximately 2.5) in patients with MAVR with additional thromboembolic risk factors. Methods: This protocol describes a systematic review and meta-analysis following PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) 2020 guidelines. A comprehensive literature search will be conducted across PubMed, Embase, and the Cochrane Library for studies published before December 18, 2024. Eligible studies include randomized controlled trials (RCTs), cohort studies, and follow-up studies involving adult patients with MAVR on warfarin therapy, stratified by the presence of additional thromboembolic risk factors. Non--English-language studies, case reports, editorials, and animal studies will be excluded. Results: The review will synthesize existing data to compare the risks and benefits of intensified anticoagulation in patients with MAVR with additional thromboembolic risk factors. Data analysis and manuscript preparation are scheduled for July-August 2025. Conclusions: This study will provide critical evidence on INR management in high-risk patients with MAVR, potentially informing future updates to clinical guidelines and optimizing the balance between thromboembolic prevention and bleeding risk. International Registered Report Identifier (IRRID): PRR1-10.2196/73389 ", doi="10.2196/73389", url="https://www.researchprotocols.org/2025/1/e73389" } @Article{info:doi/10.2196/60173, author="Cullen, J. Katelyn and Mir, Hassan and Natarajan, K. Madhu and Corovic, Marija and Mosleh, Karen and Crawshaw, Jacob and Mercuri, Mathew and Masoom, Hassan and Schwalm, JD", title="Exploring the Barriers and Facilitators to Implementing a Smartphone App for Physicians to Improve the Management of Acute Myocardial Infarctions: Multicenter, Mixed Methods, Observational Study", journal="JMIR Mhealth Uhealth", year="2025", month="Jul", day="8", volume="13", pages="e60173", keywords="ST-elevation myocardial infarction", keywords="digital health", keywords="mHealth", keywords="barriers and enablers", keywords="myocardial infarctions", keywords="myocardial", keywords="decision-making", keywords="decision", keywords="decision support", keywords="Ontario", keywords="Canada", keywords="survey", keywords="paramedics", keywords="privacy", keywords="app", keywords="application", keywords="cardiology", keywords="interprofessional communication", keywords="intervention", keywords="emergency medicine", keywords="ECG", abstract="Background: Timely and appropriate care is critical for patients with ST-elevation myocardial infarction (STEMI). Effective communication and prompt sharing of test results, particularly electrocardiograms (ECGs), between the referring emergency medicine (EM) physician or emergency medical service (EMS) paramedic and the interventional cardiologist (IC) are essential. This exchange relies on fax or SMS text messages. The SmartAMI-ACS (Strategic Management of Acute Reperfusion and Therapies in Acute Myocardial Infarction) App was developed to streamline communication. It is user friendly and privacy compliant, and enables rapid, secure ECG sharing to support faster, informed clinical decision-making. Objective: This paper details the results of targeted preimplementation surveys to establish barriers and enablers of using a smartphone app to transmit ECG images among ICs, EM physicians, and EMS paramedics to help tailor implementation interventions. Methods: To assess the proposed acceptability and uptake of the app, preimplementation surveys were disseminated to ICs, EM physicians, and EMS paramedics in one region of Ontario, Canada. Questions were generated based on selected components of the Consolidated Framework for Implementation Research, results from a pilot study carried out at a regional hospital where the SmartAMI-ACS app was previously implemented, and predicted barriers based on expert guidance. The preimplementation surveys consisted of 7-point Likert scale questions (1=strongly disagree and 7=strongly agree) and open-ended questions. Open-ended data were extracted verbatim and analyzed using an inductive qualitative approach, with transcripts coded into descriptive qualitative codes and then collapsed into themes. Results: Survey uptake was acceptable, with 9 of the invited 10 ICs, 51 of the invited 223 EM physicians, and 93 of the invited 1138 EMS paramedics responding. All groups recognized that current practices for sharing ECGs allowed room for improvement, accepting that fax can be inconvenient and SMS text messages may not be secure. When asked whether there was a need for a smartphone app to transmit ECGs, ICs (mean 6.67, SD 0.5), EM physicians (mean 5.57, SD 1.3), and EMS paramedics (mean 5.79, SD 1.45) consistently agreed. Commonly reported barriers were concerns over technological challenges, privacy issues, and cell phone reception strength. Through the identification of the barriers in each stakeholder group, implementation strategies were developed that facilitated the scale-up of this system-change intervention. Conclusions: Results from the 3 web-based preimplementation surveys to identify key barriers and enablers to the implementation of the app helped inform the selection of tailored implementation strategies to support the rollout of the app across the health region. The surveys identified key barriers around technology, privacy, and access to required Wi-Fi that needed to be addressed during app implementation to facilitate uptake and use. Results from the surveys, and ongoing evaluation of effectiveness, are informing the expansion of the app intervention to local ambulance services and other health regions. Trial Registration: ClinicalTrials.gov NCT05290389; https://clinicaltrials.gov/study/NCT05290389 International Registered Report Identifier (IRRID): RR2-10.2196/55506 ", doi="10.2196/60173", url="https://mhealth.jmir.org/2025/1/e60173" } @Article{info:doi/10.2196/67118, author="El-Malahi, Ouahiba and Mohajeri, Darya and B{\"a}uerle, Alexander and Mincu, Ileana Raluca and Rammos, Christos and Jansen, Christoph and Teufel, Martin and Rassaf, Tienush and Lortz, Julia", title="The Influence of eHealth Stress Management Interventions on Psychological Health Parameters in Patients With Cardiovascular Disease: Systematic Review and Meta-Analysis", journal="J Med Internet Res", year="2025", month="Jun", day="2", volume="27", pages="e67118", keywords="stress management", keywords="mHealth interventions", keywords="digital health intervention", keywords="psychological well-being", keywords="eHealth", keywords="psychological health", keywords="mental health", keywords="cardiovascular disease", keywords="CVD", keywords="heart", keywords="systematic review", keywords="meta-analysis", keywords="chronic stress", keywords="anxiety", keywords="depression", keywords="cardiovascular condition", keywords="mobile health", abstract="Background: Chronic stress is a critical factor influencing both physical and mental health. It can weaken the immune system, affect cardiovascular health, and lower quality of life, often leading to psychological disorders like anxiety and depression. Objective: This study aims to evaluate the effectiveness of eHealth stress management interventions on psychological health parameters, specifically anxiety, depression, stress, and quality of life in patients with cardiovascular disease (CVD). Methods: A comprehensive search was conducted across several databases, including the Cochrane Library, APA PsycInfo, Web of Science, PubMed, Embase, and clinical trial registers. Randomized controlled trials assessing the impact of eHealth stress management interventions, namely internet-based cognitive behavioral therapy (CBT), telephone-delivered CBT, internet-based stress management training, or telephone-delivered stress management training, on the specified psychological outcomes in patients with CVD were included. The control group comprised no intervention, a waitlist, (enhanced) usual care, or a web-based intervention not focusing on stress management. To evaluate potential bias, the Risk-of-Bias 2 tool was applied. A random-effects meta-analysis was performed using standard mean difference (SMD) as the effect size, with a sensitivity analysis using mean difference (MD). Results: A total of 6 randomized controlled studies were considered in the meta-analysis. In 5 studies internet-based CBT interventions were examined, while one study used an eHealth intervention based on a CBT approach. The control groups received either usual care, were placed on a waitlist, or participated in a web-based discussion forum. After the intervention period, which ranged from 8 weeks to 6 months, a significant reduction in depressive symptoms (SMD=?0.46, MD=?2.33; P<.001), as assessed by the Patient Health Questionnaire-9, was observed in the intervention group compared with the control group. Mental health--related quality of life, assessed by the subscale of the 12-Item Short-Form Health Survey, showed significant improvement (SMD=0.38, MD=3.89; P<.001) in the intervention group in comparison to the control group following the intervention period. Conclusions: The meta-analysis demonstrates that eHealth stress management interventions substantially improve psychological health parameters in patients with CVD. Given the significant positive impact, health care providers should consider integrating eHealth stress management programs into standard care for patients with CVD. These programs can be a valuable tool in mitigating the psychological burdens associated with chronic cardiovascular conditions, ultimately improving overall patient outcomes and quality of life. Trial Registration: PROSPERO CRD42024495179; https://www.crd.york.ac.uk/PROSPERO/view/CRD42024495179 ", doi="10.2196/67118", url="https://www.jmir.org/2025/1/e67118" } @Article{info:doi/10.2196/65127, author="Lee, Hocheol and Hwang, Seong Yu and Kim, Jun Ye and Park, Yukyung and Jo, Sug Heui", title="Experience of Cardiovascular and Cerebrovascular Disease Surgery Patients: Sentiment Analysis Using the Korean Bidirectional Encoder Representations from Transformers (KoBERT) Model", journal="JMIR Med Inform", year="2025", month="May", day="30", volume="13", pages="e65127", keywords="cardiovascular", keywords="cerebrovascular", keywords="KoBERT", keywords="artificial intelligence", keywords="TCM", keywords="transitional care model", keywords="hospitalization", keywords="care model", keywords="surgery", keywords="emotional experiences", keywords="emotion", keywords="South Korea", keywords="Asia", keywords="web portal", keywords="transformer", keywords="sentiment analysis", keywords="health status", keywords="rehabilitation", keywords="caregiver support", keywords="cost management", keywords="Korean Bidirectional Encoder Representations from Transformers", abstract="Background: Cardiovascular and cerebrovascular diseases significantly contribute to global mortality and disability. The shift to outpatient postoperative care, accelerated by the COVID-19 pandemic, emphasizes the need for effective management of postoperative outcomes. The high rates of cardiovascular and cerebrovascular diseases in Korea necessitate focused transitional care during patient discharge periods. However, limited research exists on the postoperative experiences of discharged patients, underscoring the necessity of establishing evidence-based services to optimize transitional care. Objective: The objective of this paper was to analyze the emotional experiences of patients who underwent cardiovascular and cerebrovascular surgeries using data from Naver, a major South Korean web portal. Methods: Posts were collected using specific keywords and processed with the Korean Bidirectional Encoder Representations from Transformers (KoBERT) model based on Transformer, which classified sentiments into positive, neutral, and negative categories. Model performance was validated according to precision, recall, F1-score, and support. Sentiment analysis was conducted within the Transitional Care Model (TCM) framework, divided into 5 domains: health status, care resources, care demand, interaction, and mental state. Results: The KoBERT model demonstrated high classification performance, achieving a precision of 96\%, recall of 94\%, and an F1-score of 94\%. Sentiment analysis revealed that compared with cardiovascular surgery patients, cerebrovascular surgery patients experienced higher negative emotions regarding health status, whereas cardiovascular surgery patients expressed more negative sentiments in care demands. Conclusions: Different patient groups experience distinct emotional and practical challenges postdischarge. Particularly, keywords within the TCM framework highlight that cerebrovascular surgery patients require robust rehabilitation and caregiver support, whereas cardiovascular surgery patients need better cost management. These findings underscore the importance of personalized transitional care strategies tailored for cardiovascular and cerebrovascular diseases. The insights derived from this study can guide health care policymakers in designing more targeted and patient-centered interventions to improve postdischarge care and patient-centered transitional care, ensuring continuous and effective postoperative management. ", doi="10.2196/65127", url="https://medinform.jmir.org/2025/1/e65127" } @Article{info:doi/10.2196/66161, author="Li, Yilan and Gu, Tianshu and Yang, Chengyuan and Li, Minghui and Wang, Congyi and Yao, Lan and Gu, Weikuan and Sun, DianJun", title="AI-Assisted Hypothesis Generation to Address Challenges in Cardiotoxicity Research: Simulation Study Using ChatGPT With GPT-4o", journal="J Med Internet Res", year="2025", month="May", day="15", volume="27", pages="e66161", keywords="cardiotoxicity", keywords="ChatGPT with GPT-4o", keywords="artificial intelligence", keywords="AI", keywords="heart", keywords="hypothesis generation", abstract="Background: Cardiotoxicity is a major concern in heart disease research because it can lead to severe cardiac damage, including heart failure and arrhythmias. Objective: This study aimed to explore the ability of ChatGPT with GPT-4o to generate innovative research hypotheses to address 5 major challenges in cardiotoxicity research: the complexity of mechanisms, variability among patients, the lack of detection sensitivity, the lack of reliable biomarkers, and the limitations of animal models. Methods: ChatGPT with GPT-4o was used to generate multiple hypotheses for each of the 5 challenges. These hypotheses were then independently evaluated by 3 experts for novelty and feasibility. ChatGPT with GPT-4o subsequently selected the most promising hypothesis from each category and provided detailed experimental plans, including background, rationale, experimental design, expected outcomes, potential pitfalls, and alternative approaches. Results: ChatGPT with GPT-4o generated 96 hypotheses, of which 13 (14\%) were rated as highly novel and 62 (65\%) as moderately novel. The average group score of 3.85 indicated a strong level of innovation in these hypotheses. Literature searching identified at least 1 relevant publication for 28 (29\%) of the 96 hypotheses. The selected hypotheses included using single-cell RNA sequencing to understand cellular heterogeneity, integrating artificial intelligence with genetic profiles for personalized cardiotoxicity risk prediction, applying machine learning to electrocardiogram data for enhanced detection sensitivity, using multi-omics approaches for biomarker discovery, and developing 3D bioprinted heart tissues to overcome the limitations of animal models. Our group's evaluation of the 30 dimensions of the experimental plans for the 5 hypotheses selected by ChatGPT with GPT-4o revealed consistent strengths in the background, rationale, and alternative approaches, with most of the hypotheses (20/30, 67\%) receiving scores of ?4 in these areas. While the hypotheses were generally well received, the experimental designs were often deemed overly ambitious, highlighting the need for more practical considerations. Conclusions: Our study demonstrates that ChatGPT with GPT-4o can generate innovative and potentially impactful hypotheses for overcoming critical challenges in cardiotoxicity research. These findings suggest that artificial intelligence--assisted hypothesis generation could play a crucial role in advancing the field of cardiotoxicity, leading to more accurate predictions, earlier detection, and better patient outcomes. ", doi="10.2196/66161", url="https://www.jmir.org/2025/1/e66161" } @Article{info:doi/10.2196/57749, author="Holm, Normann Nikolaj and Fr{\o}lich, Anne and Dominguez, Helena and Dalhoff, Peder Kim and Juul-Larsen, Gybel Helle and Andersen, Ove and Stockmarr, Anders", title="Co-Occurring Diseases and Mortality in Patients With Chronic Heart Disease, Modeling Their Dynamically Expanding Disease Portfolios: Nationwide Register Study", journal="JMIR Cardio", year="2025", month="Apr", day="25", volume="9", pages="e57749", keywords="survival analysis", keywords="interaction effects", keywords="chronic heart disease", keywords="multimorbidity", keywords="time-varying covariates", abstract="Background: Medical advances in managing patients with chronic heart disease (HD) permit the co-occurrence of other chronic diseases due to increased longevity, causing them to become multimorbid. Previous research on the effect of co-occurring diseases on mortality among patients with HD often considers disease counts or clusters at HD diagnosis, overlooking the dynamics of patients' disease portfolios over time, where new chronic diseases are diagnosed before death. Furthermore, these studies do not consider interactions among diseases and between diseases, biological and socioeconomic variables, which are essential for addressing health disparities among patients with HD. Therefore, a mapping of the effect of combinations of these co-occurring diseases on mortality among patients with HD considering such interactions in a dynamic setting is warranted. Objective: This study aimed to examine the effect of the co-occurring diseases of patients with HD on mortality, modeling their dynamically expanding chronic disease portfolios while identifying interactions between the co-occurring diseases, socioeconomic and biological variables. Methods: This study used data from the national Danish registries and algorithmic diagnoses of 15 chronic diseases to obtain a study population of all 766,596 adult patients with HD in Denmark from January 1, 1995, to December 31, 2015. The time from HD diagnosis until death was modeled using an extended Cox model involving chronic diseases and their interactions as time-varying covariates. We identified interactions between co-occurring diseases, socioeconomic and biological variables in a data-driven manner using a hierarchical forward-backward selection procedure and stability selection. We mapped the impact on mortality of (1) the most common disease portfolios, (2) the disease portfolios subject to the highest level of interaction, and (3) the most severe disease portfolios. Estimates from interaction-based models were compared to an additive model. Results: Cancer had the highest impact on mortality (hazard ratio=6.72 for male individuals and 7.59 for female individuals). Excluding cancer revealed schizophrenia and dementia as those with the highest mortality impact (top 5 hazard ratios in the 11.72-13.37 range for male individuals and 13.86-16.65 for female individuals for combinations of 4 diseases). The additive model underestimated the effects up to a factor of 1.4 compared to the interaction model. Stroke, osteoporosis, chronic obstructive pulmonary disease, dementia, and depression were identified as chronic diseases involved in the most complex interactions, which were of the fifth order. Conclusions: The findings of this study emphasize the importance of identifying and modeling disease interactions to gain a comprehensive understanding of mortality risk in patients with HD. This study illustrated that complex interactions are widespread among the co-occurring chronic diseases of patients with HD. Failing to account for these interactions can lead to an oversimplified attribution of risk to individual diseases, which may, in cases of multiple co-occurring diseases, result in an underestimation of mortality risk. ", doi="10.2196/57749", url="https://cardio.jmir.org/2025/1/e57749" } @Article{info:doi/10.2196/68929, author="Yang, Li-Tan and Wu, Chi-Han and Lee, Jen-Kuang and Wang, Wei-Jyun and Chen, Ying-Hsien and Huang, Ching-Chang and Hung, Chi-Sheng and Chiang, Kuang-Chien and Ho, Yi-Lwun and Wu, Hui-Wen", title="Effects of a Cloud-Based Synchronous Telehealth Program on Valvular Regurgitation Regression: Retrospective Study", journal="J Med Internet Res", year="2025", month="Apr", day="23", volume="27", pages="e68929", keywords="mitral regurgitation", keywords="tricuspid regurgitation", keywords="telehealth", keywords="telemedicine", keywords="cardiac remodeling", abstract="Background: Telemedicine has been associated with better cardiovascular outcomes, but its effects on the regression of mitral regurgitation (MR) and tricuspid regurgitation (TR) remain unknown. Objective: This study aimed to evaluate whether telemedicine could facilitate the regression of MR and TR compared to usual care and whether it was associated with better survival. Methods: This retrospective cohort study enrolled consecutive patients with moderate or greater MR or TR from 2010 through 2020, excluding those with concomitant aortic stenosis, aortic regurgitation, or mitral stenosis greater than mild severity. All patients underwent follow-up transthoracic echocardiography (TTE) at least 3 months apart. Patients receiving telehealth services for at least two weeks within 90 days of baseline TTE were categorized as the telehealth group; the remainder constituted the nontelehealth group. Telemedicine participants transmitted daily biometric data---blood pressure, pulse rate, blood glucose, electrocardiogram, and oxygen saturation---to a cloud-based platform for timely monitoring. Experienced case managers regularly contacted patients and initiated immediate action for concerning measurements. The primary endpoint was MR or TR regression from ?moderate to