JMIR Cardio最新文献

{"title":"Operationalizing AI-Enabled Cardiovascular Biomarkers: A Clinician-Centered Framework for Validation, Governance, and Workflow Integration.","authors":"Dabeluchi Chiedozie Ngwu","doi":"10.2196/103401","DOIUrl":"10.2196/103401","url":null,"abstract":"<p><strong>Unlabelled: </strong>Cardiovascular biomarkers are increasingly extracted or interpreted using artificial intelligence (AI) applied to electrocardiograms, imaging, laboratory measurements, electronic health records, wearable devices, and longitudinal data. Predictive performance alone, however, does not establish that a measurement is valid, that a model is transportable and calibrated, or that acting on its output improves care. Existing resources provide essential but complementary foundations: the US Food and Drug Administration-National Institutes of Health (FDA-NIH) BEST (Biomarkers, EndpointS, and other Tools) resource standardizes biomarker terminology; the V3 and V3+ frameworks address verification, analytical validation, clinical validation, and usability of digitally measured signals; prediction-model and trustworthy-AI guidance addresses reporting, risk of bias, early clinical evaluation, and deployability; and regulatory qualification pathways evaluate evidence within a defined context of use. A remaining practical challenge is translating these complementary requirements into accountable clinical action within cardiovascular workflows. This viewpoint proposes a clinician-centered operational framework organized around validation, governance, and workflow integration. For each pillar, it identifies accountable actors, required steps, documented outputs, and escalation or stop rules. Validation establishes whether the input measurement and model are fit for the intended population and decision. Governance assigns institutional authorization, clinical ownership, version control, monitoring, and authority to restrict, pause, or withdraw the intervention. Workflow integration specifies who receives the output, what confirmatory action follows, how disagreement is handled, and how decisions are documented. A worked example of an AI-enabled electrocardiographic screening output for possible left ventricular systolic dysfunction illustrates the pathway from local evaluation to echocardiographic confirmation and lifecycle monitoring. The framework does not replace established validation or regulatory standards; it operationalizes them as a clinician-centered and institutionally accountable pathway from validated signal to governed, patient-facing action.</p>","PeriodicalId":14706,"journal":{"name":"JMIR Cardio","volume":"10 ","pages":"e103401"},"PeriodicalIF":2.4,"publicationDate":"2026-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13524309/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148880537","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Addressing the Challenges in Using Synthetic Data for Health Research: Application to Cardiology.","authors":"Louise Baschet, Sebastien Marque, Stéphane Locret, Jens Jenssen, Vanessa Barbet, Patrick Jourdain","doi":"10.2196/92930","DOIUrl":"10.2196/92930","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Unlabelled: &lt;/strong&gt;Synthetic data offer significant potential for cardiology research by enabling data sharing, preserving privacy, and supporting machine learning model development. By generating artificial patient records that reflect real-world distributions, synthetic data can accelerate clinical research, improve model performance for rare cardiovascular conditions, and facilitate transnational collaborations that would otherwise be restricted by data-sharing barriers. Despite these advantages, the increasing use of synthetic data raises important ethical, regulatory, and methodological concerns that remain insufficiently addressed. Key challenges include assessing the validity and generalizability of synthetic datasets, understanding their limitations in representing complex and heterogeneous patient populations, and preventing the amplification of existing biases in cardiovascular care. Current regulatory frameworks, including the General Data Protection Regulation (GDPR) and Health Insurance Portability and Accountability Act (HIPAA), do not fully address emerging risks such as reidentification and data leakage, and there is no harmonized guidance to govern the use of synthetic data as stand-alone evidence for medical device evaluation or therapeutic research. In this viewpoint, we argue that responsible integration of synthetic data in cardiology requires, first, clear differentiation between synthetic data as a privacy-preserving distributional substitute and synthetic data as a counterfactual simulation tool, and, second, fit-for-purpose governance frameworks that pair rigorous utility and fidelity testing with explicit, adversary-aware privacy evaluation before synthetic cohorts are accepted as evidence in research or product evaluation. A prerequisite for that governance is conceptual clarity about what synthetic data are being used for. Synthetic data in health care serve 2 fundamentally distinct roles that carry entirely different validity requirements, failure modes, and regulatory implications, yet they are routinely conflated. The first role is as a privacy-preserving distributional substitute: the goal is statistical fidelity to the real data distribution, so that analyses of the synthetic dataset yield results equivalent to those of the original. The second role is as a tool for counterfactual simulation: the goal is to generate data that could not have been observed, such as rare conditions, hypothetical interventions, or extrapolations to new populations. These 2 roles are methodologically distinct. A dataset that accurately reflects real-world distributions may be inadequate for extrapolating findings to underrepresented subgroups. Conversely, a simulator optimized for novel scenario generation may systematically diverge from real-world distributions. This distinction informs every subsequent discussion of validity, bias, and regulation in this viewpoint and our proposed 4 concrete actions for the cardiology re","PeriodicalId":14706,"journal":{"name":"JMIR Cardio","volume":"10 ","pages":"e92930"},"PeriodicalIF":2.4,"publicationDate":"2026-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13427253/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148648690","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Novel Convolutional Neural Network-Based Algorithm for Heart Rate Measurement From Ballistocardiography Signals in Diverse Clinical Settings: Observational Study.","authors":"Kumar Chokalingam, Muthukumarasamy Saravanan, Ashish Kaushal, Srishti Rao, Inam Ur Rahman, Ashwathi Nambiar, Mudit Dandwate, Ravi Mahajan, Kunal Sarkar, Gaurav Parchani","doi":"10.2196/71224","DOIUrl":"10.2196/71224","url":null,"abstract":"<p><strong>Background: </strong>Continuous vital sign monitoring ensures early detection, prevents intensive care unit (ICU) admissions, and improves patient outcomes. Continuous heart rate (HR) monitoring methods often require direct skin contact, which can lead to patient discomfort. The rising popularity of ballistocardiography (BCG) offers a promising, noncontact solution for continuous vital sign monitoring with improved patient comfort.</p><p><strong>Objective: </strong>This study aims to develop and validate a novel HR measurement algorithm leveraging convolutional neural networks (CNNs) and BCG signals for accurate, noncontact, and continuous HR monitoring. By integrating time-domain peak detection with short-time Fourier transform and CNN models, the proposed approach seeks to enhance HR measurement accuracy across diverse health care settings. The study follows the Food and Drug Administration (FDA)'s Good Machine Learning Practice guidelines and evaluates the algorithm's robustness, generalizability, and clinical applicability through extensive testing on a diverse dataset, ensuring improved patient comfort and early detection of clinical deterioration.</p><p><strong>Methods: </strong>The proposed algorithm combines time-domain peak detection with short-time Fourier transform and CNNs to enhance HR measurement from BCG signals. The CNN model developed was trained on 129,976 data points from 373 participants (HR range: 36-230 bpm), including ICU patients, and was tuned on 75,970 data points from 192 participants (HR range: 46-169 bpm), with HR obtained from clinical-grade electrocardiography devices to improve generalizability. The algorithm was tested on 70,211 data points from 205 participants, including ICU patients, across 5 independent studies to demonstrate robust performance against diverse settings, demographics, and comorbidities. The methodology is in compliance with the FDA's Good Machine Learning Practice for Medical Device Development: Guiding Principles.</p><p><strong>Results: </strong>The algorithm achieved a mean absolute error of under 3 bpm and a detection rate exceeding 80%, underscoring its robustness. The Bland-Altman analysis indicates high accuracy with a minimal bias of 0.25 and limits of agreement within 8.59 bpm. Additionally, the Pearson correlation coefficient of 0.97 from the Deming regression further demonstrates strong alignment with reference HR measurements, reinforcing its precision and reliability for clinical applications.</p><p><strong>Conclusions: </strong>This CNN-based algorithm presents a robust solution for contactless HR monitoring, addressing the limitations of prior methods in noise management and adaptability. Its demonstrated accuracy, particularly in real-world, noisy clinical environments, highlights its potential for broad application in patient monitoring and improved comfort.</p>","PeriodicalId":14706,"journal":{"name":"JMIR Cardio","volume":"10 ","pages":"e71224"},"PeriodicalIF":2.4,"publicationDate":"2026-07-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13421845/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148630425","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Development and Internal Validation of the Soluble ST2, Age, and Estimated Glomerular Filtration Rate-Heart Failure Score for Predicting 30-Day Major Adverse Cardiovascular Events After Heart Failure Hospitalization in a Two-Center Vietnamese Cohort: Prospective Cohort Study.","authors":"An Viet Tran, Nguyen Khoi Quoc La, Van Anh Thi Phan, Bao The Nguyen, Hoa Thai Nguyen, Nhung Hong Thi Thai, Tho Anh Kieu Pham","doi":"10.2196/97879","DOIUrl":"10.2196/97879","url":null,"abstract":"<p><strong>Background: </strong>Patients hospitalized for heart failure (HF) face a high-risk early postdischarge period.</p><p><strong>Objective: </strong>We aimed to develop and internally validate the soluble ST2, age, and estimated glomerular filtration rate-heart failure (SAGE-HF) score, a simplified risk model incorporating soluble suppression of tumorigenicity 2 (sST2), age, and renal function, for predicting 30-day postdischarge major adverse cardiovascular events (MACE).</p><p><strong>Methods: </strong>This two-center prospective cohort study included 218 patients hospitalized with acute decompensated heart failure (ADHF) and/or heart failure with reduced ejection fraction (HFrEF) and 59 non-HF controls in Vietnam (July 2025-January 2026). Serum sST2 concentrations were measured at admission using enzyme-linked immunosorbent assay and compared between the ADHF/HFrEF cohort and controls, with adjustment for clinical covariates. Among the ADHF/HFrEF cohort, the primary endpoint was 30-day MACE, defined as a composite of all-cause death or HF rehospitalization. Candidate predictors were considered based on clinical relevance, biological plausibility, and exploratory univariable associations, and prespecified parsimonious logistic regression models were evaluated using discrimination (area under the receiver operating characteristic curve [AUC]), calibration, Brier score, and Akaike information criterion. Internal validation was performed using 1000 bootstrap resamples, and model robustness was assessed using Firth penalized logistic regression. The SAGE-HF score was derived from the final model. All data were analyzed using the R environment (version 4.5.5; R Foundation for Statistical Computing).</p><p><strong>Results: </strong>Among 277 participants, sST2 concentrations were significantly higher in patients with HF than in non-HF controls after adjustment. Among 218 patients, 47 (21.6%) experienced 30-day MACE. In multivariable analysis, age (odds ratio [OR] 1.04 per year, 95% CI 1.01-1.07; P=.02), sST2 (OR 1.06 per ng/mL, 95% CI 1.01-1.11; P=.01), and estimated glomerular filtration rate (OR 0.98 per unit, 95% CI 0.97-1.00; P=.02) were independently associated with MACE. The final model demonstrated acceptable discrimination (AUC 0.741, 95% CI 0.664-0.819) and good calibration, with stable performance after bootstrap validation (corrected AUC 0.725). Firth analysis yielded consistent results. The SAGE-HF score showed progressive risk stratification, with predicted 30-day MACE ranging from 7.0% to 60.1% and maintained acceptable discrimination and calibration.</p><p><strong>Conclusions: </strong>The SAGE-HF score, incorporating age, sST2, and estimated glomerular filtration rate, demonstrated acceptable performance for predicting 30-day MACE after HF hospitalization and may support early risk stratification, pending external validation.</p>","PeriodicalId":14706,"journal":{"name":"JMIR Cardio","volume":"10 ","pages":"e97879"},"PeriodicalIF":2.4,"publicationDate":"2026-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13418553/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148620047","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Involving Families in Cardiac Care Through Remote Patient and Family Management: Focus Group and Journey Mapping Study.","authors":"Julian Houwen, Veronica R Janssen, Sara M Hondmann, Niels H Chavannes, Maaike Kleinsmann, Douwe E Atsma, Valeria Pannunzio","doi":"10.2196/83055","DOIUrl":"10.2196/83055","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;In cardiovascular care, illness and recovery affect both patients and their families, particularly within home-based remote patient management (RPM). A recent scientific statement from the American Heart Association highlighted the importance of family involvement, identifying digital technologies as a key enabling opportunity. Despite this, research into the needs of families and the implications of RPM remains limited.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Objective: &lt;/strong&gt;This study explored the lived experiences and unmet needs of patients with cardiovascular disease (CVD) and their relatives within RPM-supported cardiac care, using perioperative care and myocardial infarction as representative trajectories. Based on the identified gaps, we proposed a set of features for remote patient and family management (RPFM) interventions to address these needs.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;This qualitative study was conducted at a Dutch university hospital with over a decade of experience in RPM across CVD pathways. A human-centered design approach was employed, including focus groups with 24 participants (13 patients with CVD and 11 relatives). Data analysis followed a framework analysis approach, combining inductive theme identification with deductive mapping onto existing frameworks. Care experiences and unmet needs were identified inductively and segmented along the Family Systems Illness Model's phases of illness. The needs were subsequently categorized deductively into the domains of the Supportive Care Framework. Based on these user-informed insights, RPFM features were generated through author ideation and internal team consensus. Finally, these experiences, needs, and features were synthesized into a visual journey map illustrating key care moments across 3 phases: preadmission, admission, and postadmission.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;We identified 47 unmet needs across 6 domains: informational (n=13), psychoemotional (n=13), social (n=7), physical (n=7), practical (n=6), and spiritual (n=1). The most significant gaps, described as \"black holes\" in care and support, emerged during the preoperative waiting period and early home recovery, which were characterized by a lack of information and psychoemotional support. To address the identified unmet needs, we generated 33 RPFM intervention feature ideas. These RPFM features were mapped across care phases and grouped into 7 categories: dynamic pathway navigation (n=8), on-demand support and information (n=5), family well-being modules (n=5), medical translation and consultation support (n=4), safety and assurance monitoring (n=4), collaborative lifestyle management (n=4), and peer support platform (n=3).&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusions: &lt;/strong&gt;This study identified experience gaps in RPM-supported cardiac care. It showed that most unmet needs for care and support extend beyond hospital admission and discharge and that most health behaviors and recovery occur in the home context, with","PeriodicalId":14706,"journal":{"name":"JMIR Cardio","volume":"10 ","pages":"e83055"},"PeriodicalIF":2.4,"publicationDate":"2026-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13412015/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148603127","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Mobile Apps for Heart Rate Variability: App Store Search and Content Analysis.","authors":"Eline de Jager, Brian Caulfield, Evgenia Angelidi, Sinead Holden","doi":"10.2196/84764","DOIUrl":"10.2196/84764","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;Heart rate variability (HRV) is a noninvasive indicator of autonomic nervous system activity that is increasingly used for health and performance monitoring. Digital and mobile technologies are increasingly providing opportunities for remote HRV monitoring outside of laboratory-based settings.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Objective: &lt;/strong&gt;This study aimed to describe the landscape of mobile apps that measure, analyze, and provide feedback on HRV, with a focus on how HRV is measured, analyzed, interpreted, and communicated to users. A secondary aim was to assess the transparency of these apps, including the extent to which they disclose the evidence underpinning their HRV metrics and feedback.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;This study was an app store search and content analysis. Searches were conducted in the Google Play Store and Apple iTunes Store. Apps were eligible for inclusion if they had functionality to record, analyze, or provide feedback on HRV and were available in English. Data were extracted from app descriptions, screenshots, websites, and, where necessary, contact with developers. Data were extracted on app metadata (developer, release and update dates, and pricing), alongside information about HRV measurement, analysis, and feedback. This included the type of sensor used; HRV measurement characteristics (sensor placement, recording duration, and body position or standardization procedures); methods to calculate and interpret HRV (ie, metrics derived and how they were interpreted for users); and additional app functionality such as reminders, the ability to log self-reported stressors, and the type of feedback or guidance provided based on HRV. We used previously published criteria for assessing the quality of information on the internet, which included authorship, scientific attribution, currency of updates, and data privacy.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;Of 746 apps identified, 206 met eligibility criteria. Of these, 132 were primary measurement apps, 59 were aggregators, and 15 were hybrid. Photoplethysmogram was the most common sensing modality (n=117, 56.8%), followed by multiple sensors (n=60, 29.1%). Full data extraction across app metadata and HRV measurement and analysis data was only achievable for 93 (45.1%) apps, representing a transparent subset with sufficient available information for content analysis. The most commonly reported HRV metrics were root mean square of successive differences (n=51) and SD of normal-to-normal intervals (n=48), while frequency-domain power (n=22) and low frequency to high frequency ratios (n=15) were less common. Most apps presented data as personalized trends or individualized ranges (76/93, 81.7%), emphasizing user-specific context rather than isolated values. Although 86% (80/93) offered contextual guidance (eg, readiness or recovery scores), many relied on proprietary algorithms that were not transparently described, limiting independent assessment of how these s","PeriodicalId":14706,"journal":{"name":"JMIR Cardio","volume":"10 ","pages":"e84764"},"PeriodicalIF":2.4,"publicationDate":"2026-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13378409/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148470583","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Bidirectional Automated Texting for Cardiovascular Health Among People Living With HIV: Observational Cohort Analysis of a Stepped-Wedge Cluster Randomized Controlled Trial.","authors":"Mechelle Sanders, Donald Harrington, Emma Sass, Marie Thomas, Tameir Holder, Yiqi Tian, Brent Johnson, Andrea Cassells, Jonathan N Tobin, Kevin Fiscella","doi":"10.2196/69098","DOIUrl":"10.2196/69098","url":null,"abstract":"<p><strong>Background: </strong>Feasible and potentially scalable strategies are needed to address the growing cardiovascular disease (CVD) risk among people living with HIV. Bidirectional automated texting (BAT) programs that remind and encourage adherence to evidence-based CVD-reducing interventions represent a potentially scalable strategy, but data on their feasibility are lacking.</p><p><strong>Objective: </strong>The goal of the study was to determine whether participant sociodemographic factors and technological constraints influenced engagement with a BAT CVD prevention program by people living with HIV. We conducted an observational cohort analysis embedded within a stepped-wedge cluster randomized trial.</p><p><strong>Methods: </strong>The BAT program was designed to address the \"Million Hearts\" ABCS (aspirin therapy, blood pressure control, cholesterol management, and smoking cessation) of cardiovascular health. The parent study was a stepped-wedge randomized trial that rolled out in 3 wedges across 8 practice sites that provided care to people living with HIV. Participants received and could engage with the text messages weekly using their own phones during the study period. We used a zero-inflated negative binomial model to identify factors associated with participants sending text messages during the study.</p><p><strong>Results: </strong>Of the 471 participants, 94% owned a smartphone capable of text messaging, and 70% reported monthly incomes less than US $1500. Overall, 60.3% (n=284) engaged with the BAT program at least once. Regarding texting behavior, participants aged ≥65 years were more likely to send a text than those aged <50 years (P=.047), although age did not influence the number of texts sent. White participants showed lower texting intensity than Black participants (incidence rate ratio 0.69; P=.04).</p><p><strong>Conclusions: </strong>Overall, 60.3% (n=284) of the participants in the study engaged with BAT at least once. The BAT intervention for ABCS appears to be a feasible intervention for people living with HIV. Only a few factors were associated with sending a text or with the number of text messages sent.</p>","PeriodicalId":14706,"journal":{"name":"JMIR Cardio","volume":"10 ","pages":"e69098"},"PeriodicalIF":2.4,"publicationDate":"2026-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13367532/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148446830","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Deep Neural Networks for Automatic Atrial Fibrillation Detection Using Long-Term Ambulatory Electrocardiography: Retrospective Diagnostic Accuracy Study.","authors":"Jagdeep Sedha, Jukka A Lipponen, Antti Aho, Helena Jäntti, Onni E Santala, Tomi P Laitinen, Tiina M Laitinen, Jari Halonen, Mika P Tarvainen, Eemu-Samuli Seljola, Noora S Naukkarinen, Olli Rantula, Tuomas T Rissanen, Juha E K Hartikainen, Tero J Martikainen","doi":"10.2196/83714","DOIUrl":"10.2196/83714","url":null,"abstract":"<p><strong>Background: </strong>Atrial fibrillation (AF), the most prevalent cardiac arrhythmia, affects 2% to 4% of the global adult population and is associated with an increased risk of stroke. Early diagnosis of AF and atrial flutter (AFL) is crucial due to their association with stroke risk and the challenge posed by their often asymptomatic and episodic nature. Traditional electrocardiogram (ECG) interpretation requires substantial expert input and can be challenging, especially with poor-quality ECGs.</p><p><strong>Objective: </strong>This study aimed to evaluate the performance of a deep neural network (DNN) model in detecting AF/AFL from a large, heterogeneous set of long-term ambulatory ECG recordings, including clinical data collected over 6 months at a university hospital, and assess its effectiveness in a setting reflecting the diversity and complexity of real-world clinical data.</p><p><strong>Methods: </strong>The research combined public datasets totaling 10,248 patients, ECG data from our previous studies (648 patients), and authentic long-term ECG recordings from 4346 patients at Kuopio University Hospital for development of the DNN model. Its clinical accuracy and generalizability were assessed using a separate test dataset consisting of 1039 pseudonymized long-term ECG recordings from 1010 patients, all thoroughly reviewed and annotated by experts.</p><p><strong>Results: </strong>The DNN model demonstrated high effectiveness, achieving 96.4% sensitivity and more than 99.99% specificity for time-level AF and AFL detection. At the recording level, it identified AF and AFL with 100% sensitivity and 98.77% specificity, producing false positives in only 1.2% (11/897) of recordings, of which 81.8% (9/11) had other non-AF/AFL arrhythmias. The model maintained high performance across diverse patient characteristics, including varying ages, comorbidities, coexisting arrhythmias, and poor-quality ECG recordings.</p><p><strong>Conclusions: </strong>The results demonstrate that the proposed DNN model may support automated screening for AF and AFL in long-term ambulatory ECG recordings and may reduce manual review workload in clinical practice.</p>","PeriodicalId":14706,"journal":{"name":"JMIR Cardio","volume":"10 ","pages":"e83714"},"PeriodicalIF":2.4,"publicationDate":"2026-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13318395/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148360932","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Impact of a Cloud-Based Care Coordination Platform on False Cardiac Catheterization Laboratory Activations and Unnecessary Team Mobilization: Retrospective Cohort Study.","authors":"William Gibson, Dawoud Al Kindi, François Brouillette, Ahmed Aldajani, Omar Chaabo, Yasmine Lachance, Elie Akl, Kshitij Badal Dandona, Giuseppe Martucci, Jean-Philippe Pelletier, Nicolo Piazza, Jeremy Y Levett, Tomer Moran, Marco Spaziano","doi":"10.2196/76932","DOIUrl":"10.2196/76932","url":null,"abstract":"<p><strong>Background: </strong>Rapid activation of the cardiac catheterization laboratory (CCL) for ST-segment elevation myocardial infarction (STEMI) is essential to minimize time to reperfusion. However, system-wide efforts to reduce treatment delays have been accompanied by increased false activations, defined as activations that do not result in emergent coronary intervention. False activations contribute to unnecessary team mobilization (UTM), staff fatigue, workflow disruption, and inefficient resource use.</p><p><strong>Objective: </strong>This study aimed to evaluate whether the implementation of a cloud-based care coordination platform (Stenoa) was associated with reductions in false activations and UTMs at a high-volume tertiary cardiac center.</p><p><strong>Methods: </strong>In September 2021, the McGill University Health Centre implemented Stenoa, a mobile, cloud-based STEMI coordination platform enabling systematic case validation using electrocardiographic and clinical data. A retrospective cohort study was conducted, including all CCL activations between September 2020 and December 2022. Activations were grouped as preimplementation (group 0: September 2020 to September 2021) and postimplementation (group 1: September 2021 to December 2022) periods. A false activation was defined as a CCL activation followed by case cancellation before any procedure was performed. The primary outcome was the rate of UTM.</p><p><strong>Results: </strong>In total, 632 activations were analyzed (group 0: n=288; group 1: n=344). UTM decreased from 8.7% (23/265) to 4.4% (14/316) following platform implementation (P=.04). False activation frequency decreased from 10.2% (27/265) to 6.9% (22/316), although this difference did not reach statistical significance (P=.16). Among false activations, the proportion resulting in UTM declined from 85% to 63% (P=.08).</p><p><strong>Conclusions: </strong>The implementation of a cloud-based STEMI coordination platform was associated with a significant reduction in unnecessary catheterization laboratory team mobilization. Structured digital communication may improve workflow efficiency and resource use in STEMI systems of care. Further multicenter evaluation is warranted.</p>","PeriodicalId":14706,"journal":{"name":"JMIR Cardio","volume":"10 ","pages":"e76932"},"PeriodicalIF":2.4,"publicationDate":"2026-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13282013/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148283636","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Cardiac Telerehabilitation Using a Smartwatch and a Gamified Smartphone App: Single-Arm Pre-Post Feasibility Study.","authors":"Yusaku Arima, Toshiki Kaihara, Megumi Nakamura, Keita Sone, Toshiya Yoshida, Yui Utsugi, Shiori Takizawa, Shunichi Doi, Kei Honda, Yoshikuni Kobayashi, Akira Kasagawa, Yasuhito Kawagoe, Takahiko Kai, Masashi Koga, Takumi Higuma, Yasuhiro Tanabe, Yoshihiro Akashi","doi":"10.2196/85808","DOIUrl":"10.2196/85808","url":null,"abstract":"<p><strong>Background: </strong>Home-based cardiac rehabilitation (CR) using digital health technologies (ie, cardiac telerehabilitation [CTR]) has emerged as a practical alternative to conventional center-based CR, particularly during and after the COVID-19 pandemic. However, maintaining sustained participation in CR remains challenging. Gamification holds the potential to enhance motivation and adherence in CR, but its role in CTR for patients with acute coronary syndrome (ACS) remains under-studied.</p><p><strong>Objective: </strong>This feasibility study evaluated the feasibility, acceptability, and safety of a combination of a gamification-enabled smartphone app and a smartwatch supporting CTR after ACS. We focused specifically on participation motivation, adherence to prescribed exercise intensity, and short-term physiological outcomes.</p><p><strong>Methods: </strong>This single-arm, pre-post intervention study was conducted at 2 Japanese institutions. Sixteen patients diagnosed with ST-elevated myocardial infarction or non-ST-elevated myocardial infarction and undergoing percutaneous coronary intervention were enrolled after discharge. Each patient received a smartphone and smartwatch connected to the Shin-po Kei app, through which participants earned points when exercising within their target anaerobic threshold heart rate (HR) range (±10 bpm). The 1-month intervention prescribed walking for 30 minutes or longer 3 to 5 days or more per week, at an intensity corresponding to a Borg scale score of 11 to 13. The primary end point was the number of app log-in days divided by the number of intervention days (app use rate). Secondary end points included the target HR exercise adherence rate (defined as the proportion of patients who exercised for 30 minutes within the target HR range on 3 or more days per week), changes in peak oxygen consumption, and changes in perceived exertion using a visual analog scale.</p><p><strong>Results: </strong>The mean patient age was 62 (SD 13) years, and 94% (15/16) were male. The mean intervention duration was 29 (SD 6) days, and the overall app use rate was 59.9%. The target HR exercise adherence rate was 50%. Peak oxygen consumption increased from 18.4 (SD 4.1) to 21.1 (SD 5.4) mL/kg/min, and the mean visual analog scale score for exercise-related hassle reduced from 57 (SD 33) to 32 (SD 26). No adverse events related to app use were identified, and 1 hospitalization unrelated to the intervention occurred during the study period.</p><p><strong>Conclusions: </strong>This feasibility study suggests that short-term engagement with a smartwatch-linked gamified smartphone app is achievable in CTR after ACS. However, adherence to predefined gamified exercise standards was lower than app use rates, suggesting that refinement of reward rules and target HR thresholds may be necessary.</p>","PeriodicalId":14706,"journal":{"name":"JMIR Cardio","volume":"10 ","pages":"e85808"},"PeriodicalIF":2.4,"publicationDate":"2026-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13256502/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148225412","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}