Heart

IntroductionAtrial fibrillation (AF), a common arrhythmia, is associated with impaired quality of life (QoL) and increased stroke risk and mortality. Clinical guidelines recommend leveraging digital technologies to support patient education and AF self-management. Conversational artificial intelligence (AI) technologies may support patient engagement with self-management by enabling human-like conversations. This study aims to evaluate the effectiveness of a conversational AI intervention (Conversational HeAlth supporT in Atrial Fibrillation Self-Management - CHAT-AF-S) in improving QoL in patients with AF. Methods and analysisCHAT-AF-S is a 3-month randomised controlled trial with 1:1 allocation and embedded process evaluation. We will randomise 480 adults (18 years of age and older) with documented AF to the CHAT-AF-S intervention or usual care. Primary outcome is the Atrial Fibrillation Effect on QualiTy-of-life (AFEQT) overall score. We will follow intention-to-treat principles and data analysts will be blinded. Intervention participants will be invited to complete a user experience survey and take part in an interview to explore the feasibility, acceptability, perceived utility, and barriers and enablers to implementing the intervention. Qualitative data will be analysed thematically. Ethics and disseminationEthics approval was obtained from the Western Sydney Local Health District Human Ethics Research Committee (2023/ETH00765). Written and informed consent will be obtained from all study participants before commencing any study procedures. Results will be disseminated via peer-reviewed publications and presentations at international conferences. Declaration of InterestsAll investigators report nil conflicts of interest. Data AvailabilityThe data that supports this project are available from the corresponding author upon reasonable request. Trial registrationAustralian New Zealand Clinical Trials Registry ACTRN12623000850673 https://anzctr.org.au/Trial/Registration/TrialReview.aspx?id=386249

BackgroundArtificial intelligence (AI) has emerged as a promising tool for interpreting 12-lead electrocardiograms (ECGs), with the potential to enhance diagnostic accuracy for arrhythmia detection. However, published studies vary widely in methodology and validation strategy, warranting a quantitative synthesis of diagnostic performance. MethodsA systematic review and meta-analysis was conducted according to the PRISMA-DTA 2018 guidelines and registered in PROSPERO (CRD420251027264). Searches were performed in MEDLINE, Embase, and Cochrane Library through September 2025 without language restrictions. Studies evaluating AI algorithms for arrhythmia detection using 12-lead ECGs were included. Data on sensitivity, specificity, and area under the curve (AUC) were extracted. Pooled estimates were generated using a bivariate random-effects model. Risk of bias was assessed with QUADAS-2, and the certainty of evidence was quantified using GRADE. Results20 studies were included in the meta-analysis, encompassing over 5.5 million ECGs. The pooled sensitivity, specificity, and AUC for AI-based arrhythmia detection were 94.0% (95% CI 90.8-96.2; I{superscript 2} = 96.9%), 98.7% (95% CI 97.3-99.3; I{superscript 2} = 98.3%), and 0.982 (95% CI 0.965-0.986), respectively. Detection of atrial fibrillation (AF) yielded a sensitivity of 92.6% (95% CI 86.4-96), a specificity of 99.1% (95% CI 98.4-99.5), and an AUC of 0.988. Convolutional neural networks (CNN) specifically demonstrated a sensitivity of 97.6%, specificity of 98.7%, and an AUC of 0.982 for overall arrhythmia detection. When limited to external validation (n=6), the sensitivity was 96.9% (95% CI 89.2-99.1), specificity was 95.6% (95% CI 77.6-99.3), and AUC was 0.983. No significant publication bias was detected, and the overall certainty of evidence was rated as high. ConclusionsAI models applied to 12-lead ECGs demonstrate excellent diagnostic performance for arrhythmia detection. Findings support potential integration into clinical workflows, particularly in settings with limited cardiology expertise. Given substantial heterogeneity, standardized datasets and multicenter prospective validation are essential to ensure effective and equitable implementation. What is KnownO_LIArtificial intelligence has been increasingly applied to 12-lead electrocardiograms for arrhythmia detection, with multiple studies reporting high diagnostic accuracy. C_LI What the Study AddsO_LIThis meta-analysis demonstrates consistently high diagnostic performance of artificial intelligence for arrhythmia detection on 12-lead ECGs, including atrial fibrillation and externally validated models. C_LIO_LIThe substantial heterogeneity observed underscores the need for standardized datasets and multicenter prospective validation before widespread clinical implementation. C_LI

ObjectiveTo validate case number correctness and time interval agreement in the Swedish Registry of Cardiopulmonary Resuscitation (SRCR) for out-of-hospital cardiac arrest (OHCA) by linkage to Emergency Medical Dispatch Centre (EMDC) data between 2015 and 2024. MethodsIn this retrospective validation study, OHCA records reported to the SCRC were linked with EMDC-indexed OHCA for validation and correction of EMS case numbers. We quantified the proportion of correct EMS case numbers reported as agreement for fully correct and partially correct EMS case numbers in SRCR. Time interval agreement was assessed by comparing dispatch to arrival (unit response time) and call start to arrival (total response time) between SRCR and EMDC. For each linked case, time differences were calculated as (SRCR - EMDC) in seconds. Median differences were estimated using Bayesian quantile regression. ResultsEMS case number completeness was high, but the proportion of fully correct case numbers was limited. Among 56,969 SRCR records, 1,004 (1.8%) lacked an EMS case number. The proportion of SRCR records with partially correct EMS case numbers was around 90% up to the year 2020 and declined to 85% in 2022-2024. Dispatch-related time intervals showed high agreement between sources, with a median difference of -0.3 seconds (95% CrI -3.9 to 4.0). In contrast, SRCR total response time (from dispatch call answer to arrival at scene) was shorter than EMDC, with a median difference of 80.9 seconds (95% CrI -84.7 to -77.0). ConclusionSRCR unit response time reflects EMDC operational recording. The SRCR total response times were consistently shorter than the interval at the EMDC, indicating a potential underestimation of the total EMS response time in the registry.

BackgroundLong-term electrocardiogram (ECG) monitoring with wearable devices enables large-scale characterisation of cardiac rhythms, but population-based evidence remains limited. The UK Biobank Cardiac Monitoring Study integrates 14-day patch-based ECG monitoring with accelerometry and detailed phenotypic and lifestyle data. Here, we report the acquisition protocol, data processing, and initial findings from 27,658 participants. MethodsParticipants in the UK Biobank imaging study were invited to undergo 14-day cardiac monitoring using a Zio XT (pilot phase; 2015-18) or BodyGuardian MINI (main phase; 2019- ongoing) monitor. ECGs were analysed by certified technicians and automated algorithms to identify atrial, ventricular, and conduction arrhythmias. In parallel, beat-to-beat RR intervals were derived using in-house algorithms, and physical activity from calibrated triaxial accelerometer data. Analyses assessed wear time, arrhythmia prevalence, circadian patterns, and repeatability. FindingsIn total, 27,658 participants (mean age 71 years; 49.9% women) were analysed, including 7,795 from the pilot phase and 21,141 from the main phase; 1,353 (4.9%) had repeat recordings. In the main phase, median wear time was 13.2 days (IQR 11.9-13.9), and undiagnosed atrial fibrillation (AF) was detected more frequently in men than women (3.2% vs 1.7%; p<0.001); 68% was paroxysmal, with 27.4% detected during week two. Ventricular tachycardia occurred in 12.1% (8.4% in women), with sustained episodes rare (0.4%) but observed. Arrhythmia timing varied markedly with activity, with AF peaking during nocturnal inactivity and ventricular ectopy increasing during activity, peaking at midday. Repeat assessments showed strong reproducibility of diurnal heart rate and activity profiles, with more modest arrhythmia consistency. InterpretationExtended ECG monitoring enables detection of subclinical arrhythmias and long-term physiological rhythms in older adults. Linkage to imaging, multi-omics, and clinical outcomes in UK Biobank will enable unprecedented evaluation of the natural history of asymptomatic rhythm disturbances and their impact on brain health. FundingBritish Heart Foundation and Wellcome Trust.

AimsCatheter ablation using radiofrequency (RF) or pulsed field (PF) energy is an effective treatment method for ventricular arrhythmia (VA). PF offers advantages in lesion formation in anatomically challenging regions. However, its acute effects on ventricular contractility during substrate modification require further elucidation. This study aimed to compare real-time hemodynamic changes associated with PF versus radiofrequency ablation in the left ventricle using stroke volume (SV) as a surrogate for myocardial response in regard to the safety of multiple lesion delivery within scarred myocardium. Methods and resultsWe conducted a prospective case series study of eight consecutive patients undergoing VA ablation using a dual-energy lattice-tip catheter (Sphere-9, Medtronic). Lesions were delivered to scarred regions identified via intracardiac echocardiography (ICE) and high-resolution 3D mapping. Hemodynamic monitoring was performed using a minimally invasive arterial waveform system (HemoSphere, Edwards Lifesciences). A total of 317 PFA and 41 RF lesions were delivered. PFA applications were associated with a transient SV reduction of 33.1{+/-}8.3 ml, with normalization post-delivery. RF lesions resulted in a minimal SV change ([&le;]10% from baseline value). SV reduction following PFA was consistent across lesion locations. All patients achieved post-procedural non-inducibility of clinical VT. ConclusionPF causes transient but reversible reductions in LV stroke volume during lesion delivery, likely reflecting acute electroporation-induced myocyte stunning rather than irreversible dysfunction. RF lesions did not produce similar changes. These findings suggest a favorable safety profile for PF in ventricular substrate ablation, including in cases of multiple lesion sets, and support its use in regions of scarring. Further studies are warranted to validate these observations and assess long-term outcomes.

BackgroundAdvances in wearable devices and machine-learning-based ECG analysis enable highly accurate detection of atrial fibrillation (AF) outside traditional clinical settings, leading to increasing identification of asymptomatic AF. However, the prognostic significance of AI-detected asymptomatic AF and its implications for downstream cardiovascular risk remain unclear. In contrast to clinically diagnosed AF, evidence guiding risk stratification and further evaluation in this population is limited. We therefore investigated the association between AI-detected asymptomatic AF and incident cardiovascular outcomes in a large population-based cohort. MethodsWe applied a validated open-source ECG-based deep learning model for atrial fibrillation detection (AI-AF) to 12-lead ECG recordings from participants in the UK Biobank. Participants with AI-detected AF on ECG and no prior clinical AF diagnosis were classified as asymptomatic AF (c). Kaplan-Meier curves and log-rank tests were used to compare the incidence of ischemic stroke and major adverse cardiovascular events (MACE: myocardial infarction, ischemic stroke, or cardiovascular death) across AF subgroups. Cox proportional hazards models were used to evaluate the association between AI-AF risk and incident MACE, adjusting for age, sex, current smoking, systolic blood pressure, total and HDL cholesterol, and prevalent type 2 diabetes. Follow-up was administratively censored at 6 years. ResultsThe study included 96,531 participants with mean [SD] age of 65 [8] years; 52% female; median follow-up [IQR] of 4.7 [1.6-7.2] years. ECG data were available for 64,029 participants and an additional 32,502 participants with clinically diagnosed atrial fibrillation (AF) without ECG recordings were included. Among participants without prior clinical AF and with available ECGs, 2,399 were classified as asympAF based on AI detection, while 58,879 were AF-free. Over 6 years of follow-up, the incidence of ischemic stroke was significantly higher in participants with asympAF compared with AF-free individuals (1.5% vs 0.52%, p = 7x10-7) and significantly lower than in participants with clinically diagnosed AF (1.5% vs 3.4%, p = 2x10-5). Similar patterns were observed for myocardial infarction and cardiovascular death. Using a more liberal AI-AF threshold corresponding to a 15% false-positive rate (asympAF15) yielded consistent findings: participants classified as asympAF15 had a 62% higher risk of incident MACE in adjusted Cox PH models (hazard ratio 1.6, 95% CI 1.2-2.2) over six years. ConclusionAI-detected asymptomatic AF identified individuals at elevated risk of ischemic stroke and major adverse cardiovascular events. As ischemic stroke is a hallmark complication of atrial fibrillation, these findings support the hypothesis that AI-ECG models may capture subclinical AF-related risk not detected by conventional clinical assessment. This approach may help extend the window for preventive interventions in populations without clinically diagnosed AF.

BackgroundAcute myocarditis is typically self-limiting and resolves spontaneously in most cases. However, ventricular arrhythmias (VA) complications, which may be life-threatening are associated with higher rates of in-hospital complications and mortality. Catheter ablation is occasionally required for acute myocarditis associated ventricular tachycardia (VT), but data on its procedural use and outcomes, in this setting, remain limited. We aimed to determine the prevalence of VA among patients hospitalized for acute myocarditis and to evaluate the subset who underwent in-hospital VT ablation, including their acute outcomes. MethodsRetrospective analyzed data from the National Inpatient Sample (NIS) database for U.S. hospitalizations with a diagnosis of myocarditis between 2016 and 2019. In-hospital outcomes were compared between patients with and without VA. Subgroup analysis examined patients with acute myocarditis associated VT stratified by whether VT ablation was performed. Patient demographics, comorbidities, procedures, and outcomes were identified using ICD-10-CM codes. ResultsAmong an estimated 17,845 hospitalizations for acute myocarditis, 8.4% (n=1,505) had VA (including 7.7% with VT). Patients with VA were more likely to have structural heart disease, renal disease, infectious etiologies, anemia, and atrial arrhythmias, despite lower prevalence of some traditional cardiac risk factors. VA was associated with markedly worse outcomes, including 5.5-fold higher in-hospital mortality (10% vs 1.6%; p<0.001). Multivariate analysis revealed that VA during admission for acute myocarditis was an independent significant risk factor for cardiac complications (aOR=4.8), total complications (aOR=4.2) and in hospital mortality (aOR=5.1) (p<0.001 for each analysis). Among patients with VT, catheter ablation was performed in 13.7% (n=190), more commonly with infectious etiologies. Ablated patients, compared to those without ablation, experienced significantly higher rates of in-hospital complications (73.7% vs 42.3%; p<0.001) and mortality (15.8% vs 6.7%; p<0.001). ConclusionsVA complicating acute myocarditis, portends significantly worse in-hospital outcomes. Although ablation was performed in approximately 1 in 7 patients with VT, those undergoing the procedure had less favorable acute results. Further prospective research is warranted to define optimal criteria for ablation and expected outcomes in this high-risk population.

BackgroundIn patients with atrial fibrillation (AF) and stable coronary artery disease beyond 1 year after percutaneous coronary intervention (PCI), oral anticoagulant monotherapy is guideline-recommended; however, its efficacy and safety in patients with complex PCI remain uncertain. MethodsWe conducted a post-hoc analysis of the randomized ADAPT AF-DES trial comparing NOAC monotherapy versus NOAC plus clopidogrel in AF patients [&ge;]12 months after second- or third-generation drug-eluting stent implantation. Complex PCI was defined by one of the following characteristics: [&ge;]3 stents, [&ge;]3 lesions, bifurcation with 2 stents, total stent length [&ge;]60 mm, left main PCI, or chronic total occlusion PCI. Net adverse clinical events (NACE), ischemic composite outcomes, and bleeding composite outcomes were evaluated according to PCI complexity. ResultsAmong 960 patients, 247 (25.7%) underwent complex PCI and 713 (74.3%) underwent noncomplex PCI. NOAC monotherapy was associated with a lower risk of NACE compared with combination therapy in both the complex PCI group (9.5% vs 21.5%; hazard ratio 0.42, 95% confidence interval 0.21-0.83; P=0.01) and the noncomplex PCI group (9.6% vs 15.7%; hazard ratio 0.59, 95% confidence interval 0.39-0.90; P=0.02), with no significant interaction. Ischemic outcomes did not differ significantly between treatment strategies regardless of PCI complexity, whereas bleeding outcomes were consistently lower with NOAC monotherapy in both complex and noncomplex PCI groups. ConclusionsIn this post hoc analysis of the randomized ADAPT AF-DES trial, the clinical benefits of NOAC monotherapy beyond 12 months after PCI--characterized by reduced bleeding without a significant increase in ischemic events--were consistent regardless of PCI complexity. While hypothesis-generating, these findings support a long-term antithrombotic strategy prioritizing bleeding reduction in patients with AF, irrespective of prior PCI complexity. Trial registrationURL: http://www.clinicaltrials.gov; Unique identifier: NCT04250116. Clinical perspectiveO_ST_ABSWhat is new?C_ST_ABSO_LIIn a randomized population of patients with AF and prior drug-eluting stent implantation, the efficacy and safety of NOAC monotherapy versus NOAC plus clopidogrel were evaluated according to anatomic PCI complexity. C_LIO_LIAmong patients with prior complex PCI, NOAC monotherapy was not associated with an increased risk of ischemic events and was associated with a substantial reduction in bleeding. C_LI What are the clinical implications?O_LINOAC monotherapy beyond 1 year after PCI was supported in patients with AF, including those with prior complex PCI. C_LIO_LILong-term antithrombotic decisions may place greater emphasis on bleeding risk than PCI complexity. C_LIO_LIThe optimal duration of combination antithrombotic therapy after complex PCI in patients with AF remains to be determined. C_LI

BackgroundGenerative artificial intelligence (AI) systems are increasingly used to produce medical illustrations for education; however, their anatomical accuracy in complex domains such as congenital heart disease (CHD) remains insufficiently validated. MethodsIn an assessor-blinded comparative study, we evaluated AI-generated CHD illustrations from two contemporary text-to-image platforms (ChatGPT-5/ChatGPT-Images and Gemini NanoBanana) against human-modified educational images. Twenty different CHD types were included, yielding 147 images that were assessed by 20 physicians (10 CHD experts and 10 non-specialists). Images were rated across four domains: anatomical accuracy, label usefulness, visual attractiveness, and suitability for medical education (total score range, 4-12). ResultsAmong 2,940 total image evaluations, the human-modified images demonstrated the highest anatomical accuracy (48.3% rated accurate), followed by NanoBanana (22.7%), while ChatGPT-generated images were predominantly rated as fabricated or incorrect (86.3% for ChatGPT-5 and 85.2% for ChatGPT-Images; p<0.001). Educational usability "as is" was highest for the human-modified images (37.9%) compared with NanoBanana (13.1%) and ChatGPT platforms ([&le;]2.1%; p<0.001). Median overall quality scores were 8 for the human-modified CHD images and NanoBanana, versus 4 for both ChatGPT systems (p<0.001). In multivariable analysis, NanoBanana images were the closest to the human-modified images in quality (95% CI, 0.91-0.98), while ChatGPT-Images (95% CI, 0.58-0.63) and ChatGPT-5 (95% CI, 0.55-0.59) showed marked quality reductions. ConclusionsThe current generative AI systems produced visually compelling but frequently anatomically inaccurate CHD illustrations, falling substantially short of the current educational standards. Model choice strongly influences performance, with Gemini NanoBanana outperforming ChatGPT-based systems yet remaining inferior to expert-designed human-modified images. AI-generated cardiac imagery should be used only within expert-reviewed educational workflows rather than as independent instructional resources.

BACKGROUNDConventional electrocardiography (ECG) has limited diagnostic accuracy for detecting coronary artery disease (CAD) in patients with stable chest pain. Advanced electrocardiography (A-ECG) may improve diagnostic performance. The study aimed to derive, externally validate, and prognostically validate an explainable A-ECG score for detecting CAD on coronary computed tomography angiography (CCTA). METHODSParticipants attending an outpatient rapid access chest pain clinic (RACC) underwent a standard 12-lead ECG and CCTA. Any CAD was defined as any calcified or non-calcified plaque. Elastic net with nested resampling was used to derive an A-ECG score using measures from the conventional ECG, derived vectorcardiography, and measures of waveform complexity. RESULTSIn the derivation cohort (n=171, age 59{+/-}13 years, 60% male), n=99 (58%) had any CAD on CCTA. A seven parameter A-ECG score to detect any CAD was derived. In an external validation cohort (n=773, age 57{+/-}12 years, 49% male, n=433 (56%) with any CAD), the score had an area under the receiver operating characteristic curve [95% confidence interval] of 0.66 [0.63-0.70] for detecting any CAD, and 0.72 [0.68-0.76] for detecting any coronary artery calcification. In the UK Biobank (n=27,239, 966 events, follow-up 1.9 [0.7-4.4] years, age 66{+/-}8 years, 50% female), higher A-ECG scores were associated with cardiovascular events even after adjusting for age, sex and cardiovascular risk factors (p<0.001). CONCLUSIONSAn explainable A-ECG model, incorporating demographic and electrocardiographic features, demonstrated modest but externally reproducible discrimination for CCTA-defined coronary atherosclerosis and independent prognostic association in a large population cohort. This scalable, low-cost approach may aid triage and risk stratification in chest pain pathways.

BackgroundElevated resting heart rate (HR) and atrial cardiopathy are each linked to higher mortality risk, yet their interrelationship and joint prognostic value remain unclear. MethodsWe analyzed 7,326 adults (mean age 59 {+/-} 13 years) without cardiovascular disease from the Third National Health and Nutrition Examination Survey with available electrocardiograms. Atrial cardiopathy was defined by electrocardiogram as abnormal P-wave axis or deep terminal P-wave negativity in V1. Multivariable logistic regression assessed cross-sectional associations between HR categories and atrial cardiopathy. Cox proportional hazards models evaluated independent and joint associations of HR categories and atrial cardiopathy with all-cause mortality. ResultsAtrial cardiopathy was present in 1,833 participants (13.5%). After adjustment, sinus tachycardia ([&ge;]100 bpm) was associated with higher odds of atrial cardiopathy (OR 1.76, 95% CI 1.06-2.92), whereas sinus bradycardia ([&le;]50 bpm) was associated with lower odds (OR 0.61, 95% CI 0.43-0.84). Each 10-bpm HR increase corresponded to 25% higher odds of atrial cardiopathy. Over a median 13.8-year follow-up, 2,415 deaths (33.0%) occurred. Sinus tachycardia (HR 3.58, 95% CI 2.61-4.91) and atrial cardiopathy (HR 1.27, 95% CI 1.16-1.39) were independently associated with mortality. Individuals with both conditions had the highest risk (HR 4.11, 95% CI 2.63-6.41). Associations varied by age and race. ConclusionsElevated resting HR is associated with higher odds of atrial cardiopathy, and their coexistence confers markedly increased mortality risk. Integrating resting HR into atrial cardiopathy metrics may enable granular population-level risk profiling.

BackgroundShort-video platforms have become increasingly important sources of health information for the general public. However, the informational quality and dissemination patterns of content related to specific therapeutic modalities, such as enhanced external counterpulsation (EECP), remain insufficiently characterized. This study aimed to evaluate the informational quality of EECP-related videos on a short-video platform and to examine the relationship between content quality and user engagement. MethodsA cross-sectional content analysis was conducted on EECP-related short videos identified through keyword-based searches. Informational quality was independently assessed using four validated instruments: the Global Quality Scale (GQS), the Journal of the American Medical Association (JAMA) benchmark criteria, the modified DISCERN instrument (mDISCERN), and the Video Information and Quality Index (VIQI). Video characteristics and user engagement metrics were extracted and analyzed. ResultsOverall, EECP-related videos demonstrated low-to-moderate informational quality across all assessment tools. Longer video duration was consistently associated with higher informational quality scores. In contrast, user engagement metrics, including the number of likes and comments, showed weak or negative associations with informational quality. Compared with videos addressing other coronary heart disease treatments, EECP-related videos were less frequently represented and received lower overall engagement. ConclusionsEECP-related content on short-video platforms is characterized by limited visibility and modest informational quality, with a notable misalignment between user engagement and informational value. These findings suggest that clinically relevant but complex therapies such as EECP may be structurally disadvantaged in short-video health communication environments.

BackgroundLeft atrial (LA) remodeling, a hallmark of chronically elevated LA pressure, is characterized by enlargement and functional impairment. While global and reservoir LA functions are well described, the role of LA booster function and its failure remains poorly defined. ObjectivesTo characterize LA booster function using cardiac computed tomography angiography (CCTA) and to evaluate the relationship between LA preload, booster performance, remodeling, and clinical outcomes. MethodsWe retrospectively analyzed 975 patients who underwent spiral CCTA between 2010 and 2018. Phasic LA and LV volumes were obtained, from which LA reservoir and booster functions were derived. LA performance curve was constructed by plotting LA pre-A volume (preload) against LA booster stroke volume. Clinical outcomes (heart failure, stroke, or cardiovascular death) were analyzed based on the LA performance curve. ResultsLA pre-A volume strongly correlated with LA end-systolic volume (r=0.92, p<0.001). The LA booster stroke volume displayed an inverted U-shaped relation to LA pre-A volume (linear coefficient 0.64, P<0.0001; squared coefficient-0.0029, P<0.0001). The atrial booster function curve reached its vertex at 107 mL (95% CI 90 to 113 mL), indicating that the booster pump response for the increased preload is exhausted at this point. Booster dysfunction was associated with impaired reservoir function (r=0.77, p<0.001) and reduced LA systolic flow rates (-0.79, P<0.001). Patients with increased LA pre-A volume but reduced booster volume ("LA failure") exhibited the highest event rate of the combined endpoint of heart failure, stroke or cardiovascular mortality (43.2%, 95% CI 33.6-54.2%). ConclusionsLA enlargement predominantly serves to increase LA pre-A volume to sustain booster function. LA contractile dysfunction affects global LA function via a concomitant reduction in LA reservoir volume. LA failure can be defined as reduced booster contraction despite elevated preload, portending poor clinical outcomes.

AimsCardiac myosin inhibitors (CMIs) have emerged as an alternative to septal reduction therapy (SRT) for obstructive hypertrophic cardiomyopathy (oHCM). However, comparative data on the time-trajectory of myocardial functional adaptation after septal myectomy (SM), alcohol septal ablation (ASA), and CMI are lacking. We compared temporal changes in echocardiographic parameters including LV global longitudinal strain (LVGLS) and LA reservoir strain (LASr) across these treatment strategies. Methods and ResultsIn this single-center retrospective cohort, symptomatic oHCM patients treated with SM (n=22), ASA (n=11), or CMI (n=47) underwent serial echocardiography with deep-learning-based automated strain analysis. Primary outcomes were temporal changes in LVGLS and LASr. Mixed-effects models adjusted for baseline clinical and echocardiographic variables were used to assess time-trajectories for up to 24 months. Treatment success rates were 86.4% (SM), 72.7% (ASA), and 93.6% (CMI). LVOT gradients were similarly reduced across groups. LVEF showed a subtle early decline after CMI (adjusted P-for-interaction=0.019). LVGLS gradually improved after SM and ASA but remained unchanged with CMI. LASr significantly improved after SM, showed minimal change after ASA, and demonstrated late attenuation beyond 9-12 months in the CMI group (adjusted P=0.029). ConclusionsDespite comparable LVOT gradient reduction, myocardial functional adaptation differed across therapies. Conventional SRT was associated with progressive improvement in LV and LA strain, whereas CMI therapy showed stable LVGLS with subtle early LVEF decline and late attenuation of LASr. These findings underscore the importance of longitudinal deformation imaging during CMI therapy and support reappraisal of SRT in selected patients requiring durable long-term management.

BackgroundCardiovascular disease remains the leading cause of global morbidity and mortality. The original My Heart Counts smartphone application demonstrated the feasibility of large-scale, fully digital recruitment and trial conduct, but was limited by platform exclusivity and the need for human experts to create text-based behavioral interventions. MethodsThe next-generation My Heart Counts smartphone application is a prospective, observational cohort study with an embedded randomized crossover trial, evaluating personalized text-based coaching prompts, available in both English and Spanish. All study and trial operations will be conducted via the My Heart Counts smartphone application, re-designed using the open-source Stanford Spezi framework to support iOS, with a planned Android release in 2027. The target enrollment is N=15,000 adults across the United States and United Kingdom. The study establishes a comprehensive digital biobank by synthesizing passive mobile health data (steps, flights climbed, heart rate, sleep, workouts), raw sensor data (e.g., accelerometry), longitudinal clinical surveys, active tasks (6-minute walk test and 12-minute Cooper run test), electrocardiograms (ECG), and electronic health record (EHR) data integrated via HL7 FHIR protocols. The embedded trial evaluates the effect of text-based coaching prompts generated by a large language model (LLM) grounded in the Transtheoretical Model of Change on daily physical activity, as compared to generic prompts. Planned AnalysisThe primary endpoint of the randomized crossover trial is change in daily step count between LLM-driven and generic text-based intervention arms, analyzed using mixed-effects models. Secondary endpoints include change in mean active minutes and calorie burn over each intervention week. Other analyses include the changes in submaximal (6-minute walk test) and maximal (Cooper 12-minute run test) cardiorespiratory fitness, changes to sensor-derived biomarkers (e.g., sleep quality, resting heart rate, and heart rate variability), and association of sensor-derived biomarkers with EHR-confirmed clinical outcomes. ConclusionsBy utilizing autonomous, LLM-driven coaching, modular software design, and cross-platform accessibility, our smartphone application-based study will provide a scalable model for inclusive and decentralized preventive care of patients with cardiovascular disease. Trial StatusRecruitment commenced in March 2026 and is ongoing.

O_FIG O_LINKSMALLFIG WIDTH=189 HEIGHT=200 SRC="FIGDIR/small/26346796v1_ufig1.gif" ALT="Figure 1"> View larger version (49K): org.highwire.dtl.DTLVardef@1303a7aorg.highwire.dtl.DTLVardef@14f223forg.highwire.dtl.DTLVardef@51672eorg.highwire.dtl.DTLVardef@4d273a_HPS_FORMAT_FIGEXP M_FIG O_FLOATNOCentral IllustrationC_FLOATNO C_FIG HIGHLIGHTSO_LIAtrial fibrillation and depression are linked via central autonomic network disruption, cardiovascular risk, and inflammation. C_LIO_LIHeightened inflammatory response and cardiovascular risk mediates the bidirectional relationship between atrial fibrillation and depression. C_LIO_LIAtrial fibrillation, depression, and their comorbidity exhibit distinct, non-additive neural and autonomic signatures. C_LI BACKGROUNDAtrial fibrillation (AF) and major depressive disorder (MDD) frequently co-occur and are each associated with adverse cardiovascular outcomes, yet the biological pathways linking these conditions remain poorly defined. Using the UK Biobank, we evaluated shared neurocardiac, inflammatory, and cardiovascular correlates underlying the AF-MDD association. OBJECTIVESTo assess bidirectional associations between AF and MDD and determine whether shared inflammatory, cardiovascular, autonomic, and neuroimaging correlates characterize their comorbidity. METHODSWe analyzed individuals with AF (N>1,716), MDD (N>4,550), comorbid AF-MDD (N>243), and healthy comparators (HCs; N>33,041). Bidirectional associations were examined using cross-sectional and Cox proportional hazard models. Mediation analyses evaluated contributions of inflammatory markers and cardiovascular risk. Central autonomic network structure and function was assessed using MRI-derived morphometry and resting-state connectivity. RESULTSAF and MDD demonstrated bidirectional associations: AF was associated with a 44% higher risk of incident MDD, and MDD with a 26% higher risk of incident AF. Inflammatory biomarkers and cardiovascular risk partially mediated these associations (6.85% and 32.01%, respectively). AF was associated with greater gray matter volume in ventromedial prefrontal and insular cortices and increased central autonomic network connectivity, whereas MDD showed opposite structural and functional patterns. The comorbid AF-MDD group exhibited distinct, non-additive neural profiles. CONCLUSIONSAF and MDD demonstrate bidirectional associations characterized by shared inflammatory, cardiovascular, and neural correlates, alongside distinct and non-additive alterations within central autonomic network circuits. These findings support a systems-level neurocardiac framework linking cardiac and psychiatric disease and highlight the importance of integrated approaches to risk assessment and multidisciplinary management in patients with AF-MDD comorbidity.

BACKGROUNDDilated cardiomyopathy (DCM) presents a highly heterogeneous spectrum, including a familial subset with elevated arrhythmic risk. Traditional demographic and imaging markers, such as late gadolinium enhancement, have been inadequate for identifying high-risk patients before arrhythmic events. Remodelling of the interventricular septum--central to ventricular mechanics and conduction--may offer improved risk stratification. OBJECTIVESTo identify differences in left ventricular (LV) morphology between arrhythmic and idiopathic dilated cardiomyopathy (aDCM vs iDCM), and to identify LV remodeling patterns that link to adverse outcomes. METHODSThree-dimensional LV shape models were constructed from end diastolic cardiovascular magnetic resonance images of 102 individuals subdivided by their idiopathic or arrhythmic subgroup allocation. A statistical shape model was built using principal component analysis. A linear discriminant analysis determined shape features of the arrhythmic subgroup and increased composite arrhythmic outcome of sudden cardiac death, aborted sudden cardiac death, and sustained ventricular tachycardia. RESULTSThe idiopathic DCM group displayed larger mass, length, diameter, mass to volume ratio, and a mild spiral pattern of thicker septal walls (p=0.004). The arrhythmic DCM group displayed a more conical (wider basal and mid wall to apical diameter) LV, and the lack of the spiral septal morphology was the most significant feature (p=0.006) to identify subjects that had the composite arrhythmic outcome. CONCLUSIONThe LV morphology derived suggests a differentiation of arrhythmic DCM patients beyond size, function and LGE presence. This was distinctive and captured shape features that suggest alternate mechanisms for arrhythmic risk linked to a pattern of remodeling. Graphical AbstractAssessing LV morphology signature of arrhythmic DCM phenotype O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=114 SRC="FIGDIR/small/26346514v1_ufig1.gif" ALT="Figure 1"> View larger version (39K): org.highwire.dtl.DTLVardef@1f47f7aorg.highwire.dtl.DTLVardef@dd5d08org.highwire.dtl.DTLVardef@106ef07org.highwire.dtl.DTLVardef@36eb76_HPS_FORMAT_FIGEXP M_FIG C_FIG

BackgroundBoth acute myocardial infarction (AMI) and acute myocarditis are characterised by cardiac troponin release as a marker of cardiomyocyte injury. While peak troponin is widely accepted as a surrogate marker for infarct size in AMI, its relationship with myocardial injury in acute myocarditis is unclear. This study aimed to quantify and compare the association between peak high-sensitivity cardiac troponin and cardiovascular magnetic resonance (CMR) late gadolinium enhancement (LGE) extent in patients with AMI versus acute myocarditis. MethodsPatients undergoing CMR imaging and measurement of high-sensitivity cardiac troponin I during hospital admission were retrospectively included. LGE extent was quantified in grams using the semi-automated expectation-maximization weighted intensity algorithm (EWA). ResultsCompared to patients with acute myocarditis (n=47), patients with AMI (n=49) had higher peak troponin levels (median [interquartile range] 32,470 [3,109-104,699] vs 7,295 [1,857-22,550] ng/L, p=0.002), larger LGE extent (25 [13-56] vs 10 [6-17] g, p<0.001), and lower left ventricular ejection fraction (45 [36- 52] vs 55 [49-58] %, p<0.001). Peak troponin was moderately positively correlated with LGE extent in both AMI (rho=0.56, p<0.001) and acute myocarditis (rho=0.58, p<0.001). However, the ratio of peak troponin to LGE mass was higher in AMI compared to acute myocarditis (1,299 [419-3233] vs 909 [310-1446] ng/L/g, p=0.02). ConclusionsPeak cardiac troponin correlates positively with LGE extent in both AMI and acute myocarditis, but the magnitude of LGE and LV systolic dysfunction is greater in AMI. Also, AMI typically has an approximately 40% greater amount of troponin release per unit LGE mass compared to acute myocarditis. This suggest that troponin-based estimates of myocardial injury size estimated by LGE are not directly interchangeable between ischaemic and inflammatory myocardial diseases.

BackgroundElevated resting heart rate is associated with increased mortality, but the underlying mechanisms remain incompletely understood. Subclinical myocardial injury (SCMI), defined by a Cardiac Infarction/Injury Score (CIIS) [&ge;]10, represents silent cardiac damage that predicts poor cardiovascular (CV) outcomes and may partially explain this association. MethodsWe analyzed 7,152 participants from NHANES III who underwent ECG recording and were free of cardiovascular disease. Heart rate was categorized as bradycardia ([&le;]50 bpm), normal (>50-<100 bpm), or tachycardia ([&ge;]100 bpm). Mortality was assessed through National Death Index linkage. Logistic and Cox regression models evaluated associations with SCMI and mortality, respectively, and attenuation was assessed by change in hazard ratios after adjusting for SCMI. ResultsSCMI was present in 1,744 (24.3%) participants. Tachycardia was associated with increased odds of SCMI (adjusted OR 2.34, 95% CI 1.42-3.88). Over 13.9 years median follow-up, 2,311 (32.3%) died from all causes and 933 (13.1%) from CV causes. Tachycardia was associated with increased all-cause mortality (HR 3.58, 95% CI 2.63-4.88) and CV mortality (HR 2.05, 95% CI 1.06-3.79). Adjustment for SCMI attenuated the tachycardia-CV mortality association by 8.6% and all-cause mortality by 5%. Bradycardia was not associated with SCMI or mortality. ConclusionThese findings suggest that SCMI partially mediates the heart rate-mortality relationship and that ECG-based assessment of SCMI may enhance risk stratification in individuals with elevated resting heart rate.

PurposeDespite strong evidence, real-world adoption of guideline-directed medical therapy (GDMT) for heart failure with reduced ejection fraction (HFrEF) remains suboptimal. The Get With The Guidelines-Heart Failure (GWTG-HF) program was designed to close gaps in care. We evaluated whether hospital participation in GWTG-HF is associated with greater GDMT intensity and improved outcomes. MethodsWe conducted a retrospective analysis (2013-2021) of Medicare beneficiaries with Part A and Part D hospitalized with HFrEF. Using a multiple baseline time series design, we compared changes in GDMT prescribing and outcomes at hospitals before and after GWTG-HF enrollment with hospitals that never participated. The primary outcome was a 90-day post-discharge prescription-fill GDMT score summarizing use and dose of beta blockers, renin-angiotensin system inhibitors (RASI; ACE inhibitor/ARB/ARNI), and mineralocorticoid receptor antagonists (MRA). Secondary outcomes included class-specific medication fills, achievement of [&ge;]50% target doses, and 30-day, 90-day, and 1-year all-cause and HF readmission and mortality. We adjusted for baseline hospital performance, patient characteristics, and temporal trends. ResultsAmong 1,274,863 Medicare beneficiaries hospitalized for HFrEF, 53.5% were treated at hospitals that never participated in GWTG-HF and 9.6% at hospitals that joined GWTG-HF before hospitalization. Unadjusted median GDMT scores increased from 3.0 in both groups to 4.0 in non-participating hospitals and 4.5 in GWTG-HF hospitals at 90 days (p<0.001). Hospital enrollment was associated with a higher 90-day GDMT score (+0.15 points; 95% CI 0.12-0.18; p<0.001), and greater use of beta blockers, RASI, and MRA, but not ARNI. HF readmission did not differ significantly; however, GWTG-HF participation was associated with lower all-cause mortality at 30 days (OR 0.95; 95% CI:0.92-0.98), 90 days (OR: 0.97; 95% CI: 0.95-0.99), and 1 year (0.97; 95% CI: 0.95-.0.99; all p<0.05). ConclusionHospital participation in GWTG-HF was associated with higher GDMT intensity and lower mortality, supporting structured quality programs to improve HFrEF care.