BACKGROUND:Pre-participation screening (PPS) in competitive athletes aims to identify cardiovascular diseases associated with sudden cardiac death (SCD). Although the 12‑lead electrocardiogram (ECG) represents the cornerstone of PPS, structural abnormalities may demonstrate limited or incomplete electrical expression, particularly in asymptomatic athletes with physiological remodeling. Artificial intelligence (AI)-enabled ECG models have shown promising performance in hospital-based populations, but their transportability to low-prevalence athlete screening environments remains uncertain. OBJECTIVES:To develop and externally validate a deep learning (DL)-based AI-ECG ensemble model for detecting imaging-confirmed structural heart disease in competitive athletes undergoing PPS. METHODS:A convolutional neural network (CNN) ensemble was trained using hospital-derived ECG images from Beth Israel Deaconess Medical Center (BIDMC, Boston, USA) and externally validated in the Italian Team for Athlete CARDiac evaluation and AI-based Risk prediction (ITACARD-AI) registry. Separate CNNs were developed for valvular heart disease (VHD) and cardiomyopathies (CM) and combined using XGBoost meta-learning. Model performance was assessed using area under the receiver operating characteristic curve (AUROC), subgroup analyses, and threshold-based evaluation. RESULTS:The ITACARD-AI cohort included 1115 competitive athletes (mean age 26 ± 13 years; 70% male), including 48 athletes (4.3%) with VHD and 30 (2.7%) with CM. External validation demonstrated substantial performance degradation compared with hospital-based internal validation. AUROC values decreased to 0.70 (95% CI 0.64-0.75) for VHD and 0.69 (95% CI 0.60-0.78) for CM, indicating only modest discrimination in the screening population. Threshold analyses showed high negative predictive values (∼99%) but persistently low positive predictive values (≤8%), reflecting limited disease enrichment and strong prevalence dependence. CONCLUSIONS:Hospital-trained AI-ECG models demonstrated limited transportability to real-world athlete screening populations. The marked reduction in external performance suggests that low disease prevalence, heterogeneous physiological remodeling, and incomplete ECG expression of structural abnormalities may substantially limit class separability in PPS environments. Although AI-ECG may provide cautious adjunctive support within physician-led workflows, these findings highlight the intrinsic challenges of applying ECG-based AI to low-prevalence sports cardiology screening populations.

Cardiac rehabilitation (CR) remains underutilized due to low referral rates, limited availability, and logistical constraints despite improving outcomes in patients following transcatheter aortic valve replacement (TAVR). “Rehabbing with Peloton”, a novel CR support model, aims to address this through use of digital exercise platforms to offer convenient, safe activity for patients post-TAVR. With unique limitations post-TAVR and the extensive content offered on Peloton, a systematic review and curation of exercise modules is needed to set the foundation for CR support models that can improve activity promotion and outcomes. To review and curate a list of exercise modules available through the Peloton application to support an activity promotion intervention appropriate for use with patients in the weeks following a TAVR procedure. Peloton modules were filtered by inclusion criteria including category (outdoor walking, strength, yoga, and low impact cardio), duration, and difficulty (beginner for all, intermediate for low-impact cardio). Additional filters included “low impact" and “warm-up” for low impact cardio, and “chair yoga" and “shavasana” for yoga. Videos were reviewed by trained personnel and excluded if they exceeded 20 minutes, required additional equipment, or included movements deemed risky by experts for patients post-TAVR (e.g., balancing, Valsalva-inducing, or producing significant afterload). 20% of videos were randomly selected for double review. Of over 8,000 Peloton videos screened—outdoor walking (n=639), strength (n=3,000+), yoga (n=4,000+), and low-impact cardio (n=457)— review yielded 231 videos meeting the inclusion and exclusion criteria: outdoor walking (n=116), strength (n=52), yoga (n=23), and low impact cardio (n=40). This project establishes a novel systematic, patient-centered review process to identify safe and appropriate digital exercise content for post-TAVR patients within extensive commercial fitness content. The framework models a key step for future CR support models, including “Rehabbing with Peloton”, that integrate virtual platforms to address challenges in access to CR and enhance accessibility for patients, potentially leading to improvements in clinical outcomes, patient engagement, and scalability. Through consideration of the limitations and needs in post-transcatheter aortic valve replacement patients, this project provides a novel outline of expert review and selection of widely available digital exercise content that programs like “Rehabbing with Peloton” and others can use in building modules to safely and effectively support cardiac rehabilitation. This crucial step allows this project and others to integrate virtual exercise platforms like Peloton to address significant challenges in access to cardiac rehabilitation, such as low availability and logistical constraints, and enhance accessibility for patients, which can potentially improve clinical outcomes, patient activity and engagement, and scalability. ClinicalTrials ID # NCT07008911

In characterizing sleep and circadian health, the day–to–day regularity of sleep–wake timing strongly predicts health outcomes, outperforming short sleep duration in prospective associations with mortality and new–onset disease. It is unknown whether biological (e.g., sleep and circadian physiology) and sociocultural (e.g., exposures that affect sleep-wake timing) sex differences lead to differences in day–to–day sleep-wake regularity or modify its prospective associations with health outcomes. Here, we present findings from a UK Biobank study of 506,582 person–days of accelerometer recordings across 73,647 middle–aged adults preceding 549,009 person–years of follow–up. We compared SRI scores between males and females and evaluated whether all–cause, cardiovascular, and cancer mortality differed across SRI groups by sex. Custom contrasts were used to compare estimated marginal means across specific SRI–sex combinations. Females were overrepresented among very high (SRI ≥90) and underrepresented among very low (SRI <60) groups. After adjustment for demographic, health, and behavioral covariates, males still had higher odds than females of exhibiting SRI <60. Low SRI and male sex were synergistically associated with higher mortality rate. Demographic, health, and behavioral covariate-adjusted models showed stronger and more dose–dependent associations between low SRI and mortality among males than females. Although the omnibus SRI × sex interaction terms were not statistically significant, the interaction contrast for SRI <60 versus ≥90 differed by sex, suggesting a possible sex difference in mortality rates at very low SRI. Together, our findings suggest that males may be more vulnerable than females to the mortality risk associated with highly irregular sleep-wake schedules.

BACKGROUND:Pulsed field ablation (PFA) of atrial fibrillation has been rapidly adopted, partly because of safety expectations compared with thermal ablation. Comparative safety data between the 2 modalities remain limited. METHODS:We conducted a prospective registry analyzing consecutive atrial fibrillation ablations at a high-volume US academic center between 2022 and 2026. The primary end point was stroke or transient ischemic attack (TIA) within 30 days, independently adjudicated by blinded neurologists. Secondary end points included death and other procedural complications. Propensity score methods with inverse probability of treatment weighting were used to balance differences in patient characteristics. Differences in the procedural workflow of PFA versus radiofrequency ablation (RFA) were evaluated with exploratory mediation analyses. RESULTS:A total of 4221 ablation procedures (2077 RFA and 2144 PFA: 68.7% Farawave, 23.3% Sphere 9, 6.9% Varipulse, 0.7% PulseSelect) was performed by 12 operators. Patients receiving PFA and RFA had similar baseline characteristics (mean age, 67 years; 31% female; 47% persistent atrial fibrillation; 7% previous stroke/TIA), which were balanced after inverse probability of treatment weighting. Compared with RFA, PFA procedures were shorter (108 versus 144 minutes) and included more frequent posterior wall isolation (57% versus 31%). The 30-day rate of stroke/TIA was significantly higher for PFA cases (10 events; 0.47%) compared with RFA (2 events; 0.10%) in both unweighted and propensity score-weighted analyses (weighted risk difference, 0.36% [95% CI, 0.03%-0.70%]; P=0.03). Stroke/TIA events were evenly distributed throughout the study period with no clustering around the time of PFA adoption or operators (with no operator associated with ≥2 events). Exploratory mediation models were limited by the low event rates and did not identify more extensive ablation as an independent risk factor for stroke. Weighted rates of other procedural complications were low (<1%) and similar between PFA and RFA groups. CONCLUSIONS:In a high-volume, single-center registry with high clinical granularity and systematic patient follow-up, PFA was associated with a significantly higher risk of stroke/TIA at 30 days compared with RFA. These results call for enhanced postmarket surveillance and dedicated prospective evaluation as the PFA procedural volume continues to exponentially increase.

The day-to-day regularity of sleep-wake timing refers to time-varying patterns of behavioral cycles, which co-occur with temporally associated environmental exposures and circadian rhythms. Introduced in 2017, the Sleep Regularity Index (SRI) has enabled rigorous study of the health and performance implications of the day-to-day regularity of sleep-wake timing. Since its introduction, multiple calculators have been published to facilitate SRI scoring from timestamped sleep-wake data; however, the comparability of these calculators had not previously been evaluated. Here, we sought to (1) estimate SRI usage and method of calculation in peer-reviewed studies published since its establishment; (2) compare SRI scores calculated by two widely used SRI calculators, sleepreg and GGIR; (3) compare results from prospective assessments of the relationship between sleepreg SRI scores versus GGIR SRI scores and previously examined health outcomes. We found that amidst increasing use of the SRI, non-disclosure, and heterogeneity in the method of SRI calculation are common. Additionally, among more than 70 000 adults with accelerometer-derived sleep-wake data, SRI scores calculated by two widely used open-source packages differed markedly, both in absolute and relative values. Applied to prospective clinical outcome models for all-cause mortality, incident type 2 diabetes, and incident atrial fibrillation or atrial flutter, the method of calculation alone meaningfully changed results and interpretations. In light of these findings, we developed and introduced a 14-item RIRI statement (Reporting Items for Regularity Indices) to standardize reporting and promote reproducibility in research involving the SRI or complementary regularity indices. Statement of Significance Health and performance implications of sleep variability have been unveiled in part due to improvements in operationalizing the day-to-day regularity of sleep-wake cycles using the Sleep Regularity Index (SRI). However, in a critical review of publications that have used the SRI, we identified marked non-disclosure and heterogeneity in the method of SRI calculation. Moreover, using the same accelerometer-based sleep-wake estimates among 73 794 adults, we found that SRI scores calculated by different calculators differ considerably and consequentially, including altered findings in prospective time-to-event models. We therefore developed and propose a 14-item RIRI statement (Reporting Items for Regularity Indices) to standardize reporting and promote reproducibility of research involving the SRI, with generalizability to all research that compares temporally matched data.

Contrastive learning is a widely adopted self-supervised pretraining strategy, yet its dependence on cohort composition remains underexplored. We present Contrasting by Augmented Patient Electrocardiograms (CAPE) foundation model and pretrain on four cohorts (n = 5,203,269), from diverse populations across three continents (North America, South America, Asia). We systematically assess how cohort demographics, health status, and population diversity influence the downstream performance for prediction tasks also including two additional cohorts from another continent (Europe). We find that downstream performance depends on the distributional properties of the pretraining cohort, including demographics and health status. Moreover, while pretraining with a multi-centre, demographically diverse cohort improves in-distribution accuracy, it reduces out-of-distribution (OOD) generalisation of our contrastive approach by encoding cohort-specific artifacts. To address this, we propose the In-Distribution Batch (IDB) strategy, which preserves intra-cohort consistency during pretraining, discourages learning of spurious cohort-specific features, and instead promotes clinically meaningful variability within cohorts. This leads to improved out-of-distribution robustness, with gains of 9-40% in downstream label prediction performance. This work provides insights into pretraining strategies for more clinically deployable and generalisable foundation models.

Background Although the availability of electrophysiology technologies has expanded in the United States, little is known about how sociodemographic factors influence their use. This national study of the Medicare population aims to (1) examine trends in the use of contemporary electrophysiology procedures and (2) assess associations between sociodemographic factors and population‐level use of these procedures. Methods We identified Medicare beneficiaries who underwent pulmonary vein isolation, left atrial appendage occlusion, leadless pacemaker implantation, or subcutaneous implantable cardioverter‐defibrillator implantation from 2018 to 2021 using Current Procedural Terminology codes. Procedural rates were calculated per 100 000 person‐years among all eligible Medicare beneficiaries. We compared the rates of each procedure across sociodemographic characteristics (including sex, race, census region, and social vulnerability index) by reporting rate ratios adjusted for age and comorbidities. Results A total of 425 242 electrophysiology procedures performed between 2018 and 2021 were included. During the study period, use rates of pulmonary vein isolation, left atrial appendage occlusion, and leadless pacemaker increased steadily, whereas subcutaneous implantable cardioverter‐defibrillator use declined. In adjusted models, individuals identifying as Black or categorized as other race were less likely to undergo a pulmonary vein isolation, left atrial appendage occlusion, and leadless pacemaker procedure but were more likely to receive a subcutaneous implantable cardioverter‐defibrillator. Beneficiaries in less socially vulnerable areas were more likely to receive pulmonary vein isolation, left atrial appendage occlusion, and leadless pacemaker but less likely to undergo subcutaneous implantable cardioverter‐defibrillator implantation. Regional variation showed higher use rates in the South and West of the United States. Conclusions Significant sociodemographic differences in the use of contemporary electrophysiology procedures persist across communities in the United States. Targeted efforts are needed to ensure that use aligns with clinical need.

Aims:A significant proportion of type 2 diabetes cases remain undiagnosed despite screening advances, carrying substantial cardiometabolic risk. Artificial intelligence-enhanced electrocardiography (AI-ECG) detects subtle ECG changes in subclinical disease, potentially enabling opportunistic screening. Methods and results:We developed AI-ECG Risk Estimator for Diabetes Mellitus (AIRE-DM), a convolutional neural network with discrete-time survival loss, for diagnosis of prevalent and prediction of incident type 2 diabetes. It was trained on 1 163 401 ECGs from 189 537 individuals from Beth Israel Deaconess Medical Center (BIDMC) and externally validated in UK Biobank (UKB; n = 65 606) and ELSA-Brasil (n = 13 739). AI-ECG Risk Estimator for Diabetes Mellitus demonstrated moderate discrimination for prevalent type 2 diabetes (area under the receiver operating characteristic curve: BIDMC 0.724, UKB 0.733, ELSA-Brasil 0.706) and incident type 2 diabetes (C-index: BIDMC 0.667, UKB 0.688, ELSA-Brasil 0.625). The highest AIRE-DM risk quartile had elevated incident diabetes risk vs. the lowest (hazard ratio: BIDMC 4.75, UKB 7.52, ELSA-Brasil 3.96). AI-ECG Risk Estimator for Diabetes Mellitus was non-inferior to the American Diabetes Association Diabetes Risk Test in BIDMC, with improved predictive accuracy when combined. In normoglycaemic patients, AIRE-DM was superior to glycated haemoglobin (HbA1c) for predicting incident diabetes in BIDMC and non-inferior in ELSA-Brasil. The highest risk quartile reached 5% cumulative type 2 diabetes mellitus incidence 5.4 years (BIDMC) and 4.8 years (ELSA-Brasil) earlier than the lowest risk quartile, after adjusting for HbA1c, age, and sex. Phenome- and genome-wide association studies revealed biologically plausible associations with glucose regulation, cardiac morphology, diastolic dysfunction, arterial stiffness, and lipid metabolism. Conclusion:AI-ECG Risk Estimator for Diabetes Mellitus detects prevalent type 2 diabetes and predicts incident disease, uniquely identifying high-risk individuals within the normoglycaemic range. Combined with clinical scores or biomarkers, it enhances risk stratification, enabling earlier intervention.

While the cardiometabolic benefits of exercise volume and intensity are well established, the clinical significance of exercise timing remains poorly understood, largely due to the limitations of short-term accelerometry. We leveraged minute-level heart rate data from 14,489 participants from the All of Us Research Program to define habitual exercise timing over a one-year period. Compared to daytime exercise, habitual morning exercise was associated with lower odds of coronary artery disease (OR 0.69; CI 0.55-0.87), hypertension (OR 0.82; CI 0.72-0.94), type 2 diabetes (OR 0.70; CI 0.58-0.85), hyperlipidemia (OR 0.79; CI 0.69-0.90), and obesity (OR 0.65; CI 0.55-0.77). These associations were independent of total physical activity volume and remained consistent across hour-of-day analyses, with the lowest risk nadir occurring between 07:00-08:00 for coronary artery disease. These findings suggest that exercise timing may represent a distinct, underappreciated dimension of exercise behavior linked to cardiometabolic health.

Background The COVID-19 pandemic presented unprecedented challenges to hospital system and critical care resources, leading to significant changes to operations and patient care. There are limited national data on these changes and instances of unsanctioned deviations from patient care, yet understanding the COVID response is key to future preparedness efforts. We sought to understand how hospitals and states navigated scarcity during COVID-19, particularly in the absence of a declaration of crisis standards of care.Methods Between February 2022 and September 2022 we conducted 34 interviews with 36 leaders of U.S. states' COVID-19 planning and response efforts. Interviews were transcribed verbatim and verified. We analyzed interviews using iterative inductive thematic analysis for descriptions of resource scarcity and changes to policies and procedures to prevent rationing lifesaving care.Results Nearly all participants described equipment and personnel scarcity in their home institution or state during COVID-19. Hospitals across regions and states developed formal and informal coordination processes for load and resource sharing in response to influxes of high-acuity patients, avoiding formal rationing of lifesaving resources in many regions. Participants also described unsanctioned patient triage, early discharge, and patients counseled to accept less aggressive care (e.g., premature transition to hospice) in states that had not declared crisis standards of care.Conclusions Extending limited resources and inter-institutional collaboration helped avoid formal rationing. Yet, patient care was unquestionably impacted due to scarcity, both real and perceived. Reports of using hospital triage protocols to deny patients lifesaving care outside of formally recognized crisis conditions and attempts to nudge patients to accept less-resource-intensive care are concerning. This may have had disproportionate effects on older adults, individuals with disabilities, and racial and ethnic minoritized groups. To avoid unsanctioned deviations from standard practice in future health emergencies, we recommend that transparent and equitable triage protocols are implemented with robust oversight.

Background:Despite ample evidence of the benefits of cardiac rehabilitation (CR), few transcatheter aortic valve replacement (TAVR) patients participate. Commercially available mobile health offers an opportunity to deliver activity-promotion content to populations that are challenged to participate in CR. This study aims to test the efficacy of clinically controlled, commercially available fitness programming for improving physical activity and cardiovascular health outcomes designed to be initiated while patients are on waitlists for traditional CR. Methods:The Cardio Heart Connect study is a hybrid type I effectiveness-implementation trial aiming to enroll N=200 patients who have been placed on a cardiac rehab waitlist following a TAVR procedure from the University of Colorado Hospital Heart and Vascular Center. Participants will be randomized 1:1 to the Cardio Heart Connect intervention with commercially available fitness or attention control, designed to control for technology access. At baseline, post-intervention (8 weeks), and follow-up (12 months), we will assess the primary outcome of participants' daily steps as measured by smartwatch accelerometer and secondary outcomes of interest including functional capacity (Duke Activity Status Index; VO2max), quality of life (Kansas City Cardiomyopathy Questionnaire), and cardiovascular health status (Life Essential 8). In addition, we will use mixed methodologies to evaluate the implementation of intervention using the Reach, Effectiveness, Adoption, Implementation, and Maintenance (RE-AIM) Framework. Conclusions:Commercially available fitness programs have the potential to provide more accessible opportunities for patients recovering from TAVR to engage in physical activity and may be preferred due to their customizability, convenience, and ease of scheduling. Overall, this study will provide insight into the use of commercial mHealth to promote activity following TAVR.

Background:Differentiating chronic from acute left bundle branch block (LBBB) is useful in various cardiac disorders. Peak QRS/T ratio, a measure of cardiac memory, can differentiate chronic from acute LBBB with high sensitivity and specificity, but its utility in post-transcatheter aortic valve replacement (TAVR) patients with LBBB is unclear. Objective:This study aimed to validate the QRS/T ratio for differentiating chronic/acute LBBB in post-TAVR patients and assess whether the spatial ventricular gradient (SVG), which integrates 3-dimensional depolarization/repolarization throughout the cardiac cycle, could also distinguish chronic from acute LBBB. Methods:This was a post hoc analysis of a prospective, observational, single-center TAVR study. After TAVR, patients were classified as acute (new <24 hours) or chronic/preexisting LBBB. Electrocardiograms were transformed into vectorcardiograms, and peak QRS/T ratio and SVG were calculated. Sensitivity, specificity, and area under the receiver-operating curve (AUROC) assessed how QRS/T ratio and SVG differentiated chronic from acute LBBB. Results:Of 409 patients, 21 had preexisting, and 53 had acute/new LBBB. Patients with chronic LBBB had a higher peak QRS/T ratio than patients with acute LBBB (median 3.8 vs 2.2; P < .0001). 3-dimensional SVG vectors significantly differed between chronic and acute LBBB (joint P < .0001), due to differences in the anterior/posterior (Z) direction, (SVGz): median 43.4 vs -14.2 mV·ms, P < .0001, for chronic vs acute LBBB, respectively. A peak QRS/T ratio of ≥2.7 and an SVGz of ≥15 mV·ms had 100% sensitivity, 98.1% specificity, and an AUROC of 0.991 for chronic LBBB. Over long-term follow-up, patients transitioned from acute to chronic LBBB vectorcardiographic phenotype. Conclusion:Peak QRS/T ratio and SVG differentiate chronic from acute LBBB in post-TAVR patients with 100% sensitivity, 98% specificity, and an AUROC of 0.99.