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.

Structured AbstractO_ST_ABSBackgroundC_ST_ABSGuideline recommendations for infective endocarditis (IE) prophylaxis have narrowed significantly over the past decade. However, these recommendations are derived from adult data and may not adequately account for the unique risk factors for IE in pediatric and congenital heart disease (CHD) patients with cardiac implantable electronic devices (CIEDs). ObjectiveTo characterize contemporary IE cases and prophylaxis practices among members of the Pediatric and Congenital Electrophysiology Society (PACES) and assess how these practices align with or diverge from current international guidelines or practice recommendations. MethodsA cross-sectional, web-based survey was distributed to PACES members worldwide. Questions addressed prophylaxis practices for CIED implantation, reinterventions, and bacteremia-inducing procedures, as well as clinician experience with IE in patients with CIED. Responses were analyzed descriptively. ResultsSubstantial practice heterogeneity was identified across multiple clinical scenarios. Although most clinicians aligned with guideline recommendations for patients with structurally normal hearts, nearly all respondents (92.3%) reported recommending lifelong prophylaxis for patients with complex or repaired CHD. Among 35 reported IE cases, 97% occurred in transvenous systems, 77% occurred >6 months post-implantation, and 90% lacked a clear procedural or infectious trigger. Despite successful device extraction in 77% of cases, significant morbidity and mortality were observed. ConclusionCurrent practice patterns among pediatric and congenital electrophysiologists reflect uncertainty regarding the applicability of adult-derived IE prophylaxis guidelines to younger patients with CIEDs. High observed morbidity, long-term device exposure, and distinct anatomic considerations highlight the need for pediatric-specific risk stratification and updated guidance.

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.

BackgroundVentricular arrhythmias (VAs) are a proximate mechanism of sudden cardiac death, yet national patterns in place of death (POD) and their determinants remain sparsely described. We quantified 25-year trends and factors associated with POD among UAs decedents in the United States. MethodsWe analyzed CDC WONDER Multiple Cause of Death data (1999-2024) for adults [&ge;]25 years with ventricular arrhythmias (ICD-10 I47.2, I49.0) as underlying cause. POD was categorized as inpatient, outpatient/emergency department (ED), home, hospice/nursing, or other/unknown. Covariates included age, sex, race, Hispanic origin, and urbanization. We calculated Annual and Average Annual Percent Changes (AAPCs and APC) using Age-Adjusted Mortality Rates (AAMRs), and fit multinomial logistic regression (reference = inpatient) to obtain adjusted odds ratios (ORs, 95% CIs). ResultsAmong 433,988 ventricular arrhythmia (VA) deaths, POD was inpatient 62.0%, outpatient/ED 18.1%, home 11.1%, hospice/nursing 5.8%, other 3.1%. Inpatient deaths increased from 57.8% (1999) to 66.1% (2024). AAMRs declined sharply from 13.3 per 100,000 in 1999 to 6.3-6.5 during 2010-2019, then rose to 7.4 in 2021 and fell to 6.8 in 2024. In home vs inpatient: [&ge;]85 years, medium/small metropolitan counties and rural counties had higher odds of VA deaths, whereas younger age groups, females, Black, American Indian, Asian/Pacific Islander individuals and Hispanic individuals had lower odds. In outpatient/ED vs inpatient: 25-44 years, 45-64 years, males, Black, American Indian and Asian/Pacific Islander individuals had higher odds, whereas [&ge;]85 years and females had lower odds. In hospice/nursing facilities: [&ge;]85 years, females, Whites, non-Hispanic individuals, medium/small metropolitan counties and rural counties had higher odds of VA deaths, whereas younger age groups, Black, American Indian, Asian/Pacific Islander individuals and Hispanic individuals had markedly lower odds. ConclusionFrom 1999-2024, VA deaths shifted toward hospitals. Persistent disparities by age, sex, race/ethnicity, and rurality highlight the need to expand equitable advance care planning and device deactivation discussions.

BackgroundCatheter ablation is an established rhythm control therapy for atrial fibrillation. However, as the extent of ablation increases, the risk of complications may also rise. This has motivated strategies that achieve pulmonary vein isolation with less lesion creation while preserving safety and effectiveness. MethodsIn this prospective, multicenter, randomized non-inferiority trial, 130 patients undergoing first-time ablation for paroxysmal or non-paroxysmal atrial fibrillation were assigned 1:1 to a voltage-guided stepwise pulmonary vein isolation approach or conventional circumferential antral pulmonary vein isolation with voltage blinded to operators. The primary end point was recurrence of atrial tachyarrhythmia within 12 months after ablation. ResultsAt 12 months, recurrence occurred in 23/65 (35.4%) in the stepwise group versus 13/65 (20.0%) in the control group (risk difference 15.4 percentage points; 90% confidence interval, 2.7-28.1), and non-inferiority was not demonstrated (one-sided P=0.520). The treatment group had a higher risk of recurrent atrial tachyarrhythmia than the control group (hazard ratio, 2.05; 95% confidence interval, 1.04-4.06), with longer procedure times and more frequent acute pulmonary vein reconnection after the initial lesion set. The treatment group had fewer acute complications than the control group (1.5% versus 9.2%; P=0.115), and esophageal thermal injury was observed only in the control group (3 cases). ConclusionsVoltage-guided stepwise pulmonary vein isolation failed to demonstrate non-inferiority to conventional circumferential antral pulmonary vein isolation for 12-month atrial tachyarrhythmia recurrence. ClinicalTrials.gov ID: NCT07354737

AimsEarly discharge after electrophysiological procedures has gained increasing attention. However, definition of patient- and procedure-related prerequisites for successful and safe discharge strategies after atrial tachycardia (AT) ablation remains unknown. We therefore evaluated patient characteristics, procedural features, and outcomes according to index length of stay (LOS) following AT ablation. Methods and resultsThe multicenter observational SATELLITE registry enrolled consecutive patients undergoing AT rhythm control. Patients were stratified by LOS ([&le;]1, 2 and >2 nights) after catheter ablation. Among 670 patients (67 [IQR 56-75] years, 54.9% male), LOS was [&le;]1 night in 13.9%, 2 nights in 41.9% and >2 nights in 44.2%. LOS was only modestly predictable from clinical characteristics including age, sex, atrial fibrillation and prior atrial ablation (AUC 0.73). Discrimination improved after inclusion of procedural variables and early post-procedural events (AUC 0.77; P=0.0300), consistent with an increase in left atrial procedures (26.5% vs. 76.0% vs. 80.8%; P<0.0001), acute minor complications (3.2% vs. 2.5% vs. 14.5%; P<0.0001) and early recurrences of atrial arrhythmia (2.2% vs. 6.8% vs. 21.3%; P<0.0001). During 2.8{+/-}3.0 years of follow-up, LOS did not predict long-term outcomes including subsequent cardiovascular hospitalization (HR 1.19, 95% CI 0.78-1.81; P=0.4175). ConclusionDespite multiple comorbidities, most patients undergoing AT ablation need up to 2 nights of hospitalization. However, prolonged hospital stays before successful and safe discharge are common and associated with acute minor complications and early recurrences of atrial arrhythmia rather than comorbidities. Accordingly, discharge timing largely reflects the immediate peri-procedural clinical course, therefore challenging purely logistics-driven planning. Key Learning PointsO_ST_ABSWhat is already knownC_ST_ABSO_LIEarly discharge after electrophysiological procedures has gained increasing attention. C_LIO_LIDefinition of patient- and procedure-related prerequisites for successful and safe discharge strategies after atrial tachycardia (AT) ablation remains unknown. C_LI What this study addsO_LIDespite multiple comorbidities, most patients undergoing AT ablation need up to 2 nights of hospitalization. C_LIO_LIProlonged hospital stays before successful and safe discharge are common and associated with acute minor complications and early recurrences of atrial arrhythmia rather than comorbidities. C_LIO_LIDischarge timing largely reflects the immediate peri-procedural clinical course, therefore challenging purely logistics-driven planning C_LI Structured Graphical AbstractO_LIDespite multiple comorbidities, most patients undergoing AT ablation need up to 2 nights of hospitalization. However, prolonged hospital stays before successful and safe discharge are common and associated with acute minor complications and early recurrences of atrial arrhythmia rather than comorbidities. Accordingly, discharge timing largely reflects the immediate peri-procedural clinical course, therefore challenging purely logistics-driven planning. C_LI O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=130 SRC="FIGDIR/small/26345799v1_ufig1.gif" ALT="Figure 1"> View larger version (31K): org.highwire.dtl.DTLVardef@200309org.highwire.dtl.DTLVardef@1a745fcorg.highwire.dtl.DTLVardef@e3cd45org.highwire.dtl.DTLVardef@1b98c3e_HPS_FORMAT_FIGEXP M_FIG C_FIG

BackgroundAnticoagulants are often less frequently prescribed in elderly patients with atrial fibrillation (AF) because of concerns regarding high bleeding risk, despite their increased susceptibility to embolic stroke and systemic embolization. This study evaluated the impact of the Clinical Frailty Scale (CFS) on clinical outcomes and decision-making for prescribing antithrombotic therapy following left atrial appendage closure (LAAC) in a large contemporary registry. MethodsThe OCEAN-LAAC registry included 1,409 patients who underwent LAAC. Outcomes and antithrombotic prescriptions after the procedure were compared between groups stratified by CFS into 1-3 and 4-8. ResultsPatients with CFS 4-8 were more likely to have a history of stroke and demonstrated lower serum albumin and hemoglobin levels, consistent with advanced frailty. In multivariate analysis, CFS 4-8 was independently associated with higher all-cause mortality at one year compared with CFS 1-3 (adjusted hazard ratio 1.89; 95% confidence interval 1.05-3.41). By one year, patients with CFS 4-8 more frequently discontinued antithrombotic therapy, without significant differences in ischemic stroke or device-related thrombotic events. Notably, major bleeding was more common in the CFS 4-8 group, reflecting their advanced clinical vulnerability. ConclusionGreater frailty, as assessed by CFS, was independently associated with increased all-cause mortality following LAAC. Although frailty influenced patterns of antithrombotic therapy in this real-world registry, thrombotic events remained comparable between CFS groups, supporting the feasibility of individualized, frailty-guided post-LAAC management. These findings underscore the importance of incorporating frailty assessment into multidisciplinary Brain-Heart team decision-making.

BackgroundEarly identification of arrhythmias in the intensive care unit (ICU) is important to prevent ICU morbidity and mortality. Timely arrhythmia detection relies on bedside providers telemetry interpretation. Machine learning (ML) models can function as clinical support tools to facilitate diagnoses. ML model development requires well-curated training data. The differential performance between labelers of different roles and experience is currently unknown. MethodsThis was a prospective observational study with frontline providers. 300 (200 original, 100 duplicate) 10-second telemetry tracings were labeled including sinus rhythm, 2nd/3rd degree atrioventricular (AV) block, junctional ectopic tachycardia (JET), ectopic atrial tachycardia (EAT), and reentrant supraventricular tachycardia (SVT). Interrater reliability was calculated against the ground truth label as the primary performance measure (intrarater reliability for consistency utilizing duplicate labels). Results11 participants completed the study: 1 Cardiology fellow, 4 pediatric ICU fellows, 2 pediatric cardiac ICU fellows, 3 pediatric cardiac ICU NPs, and 1 Pediatrics resident. Highest level of agreement was moderate ({kappa} 0.68, p <0.001) with the majority poor to moderate. There was no association of clinical subspecialty with labeling performance. Performance varied by rhythm type (median {kappa}): sinus (0.61), AV block (0.68) > Junctional (0.47), EAT (0.25), SVT (0.49). There was good intrarater reliability ({kappa} 0.71 [median], p<0.001). ConclusionsOverall, frontline provider performance was poor especially for complex arrhythmia classes. Cardiology training and experience was not associated with better performance. These findings highlight the need for thoughtful consideration in labeler training and validates the need for a clinical decision support tool in arrhythmia detection.

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.

BackgroundOut-of-hospital cardiac arrest (OHCA) remains a global health burden where neurological injury sustained is a key predictor of mortality but there are challenges in early risk stratification. This study aims to derive the Pre-MIRACLE2 score, which excludes pH as a component from the MIRACLE2 score, as a means of stratifying neurological risk in a pre-hospital setting. MethodsTo validate the Pre-MIRACLE2 score, we used (i) the EUCAR Registry retrospectively analysed from 1 May 2012 to 31 December 2021, and (ii) the GLOBAL-MIRACLE Registry, a prospective cohort analysed from 1 January 2022 to 31 May 2023. The primary outcome was poor neurological outcome (defined as Cerebral Performance Category 3-5) at hospital discharge. ResultsFrom 1 May 2012 until 31 May 2023, 2149 patients were resuscitated from OHCA with sustained return of spontaneous circulation. After excluding patients who remained non-comatose following return of spontaneous circulation and those with incomplete scores, 1402 patients from EUCAR and 747 from GLOBAL-MIRACLE were included in the final analysis. The primary endpoint occurred in 54.4% of the study cohort. The performance of the Pre-MIRACLE2 score for the primary endpoint was excellent, with an area under the receiver operating curve (AUROC) of 0.85 (95% CI 0.83, 0.87). From the prospective validation cohort (GLOBAL-MIRACLE), the AUROC was 0.85 (95% CI 0.82-0.88) with a calibration slope of 1.11 (95% CI 0.95-1.29). ConclusionThe Pre-MIRACLE2 score has the potential to be an effective and pragmatic risk stratification tool for prediction of poor neurological outcome in a pre-hospital environment or where the pH cannot be measured.

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

BackgroundIn 2018, the United Network for Organ Sharing (UNOS) revised the donor heart allocation policy, replacing the single urgency status for left ventricular assist device (LVAD)-related complications with three distinct categories. We evaluated the impact of this policy modification on transplant access and outcomes. MethodsThe UNOS Standard Transplant Analysis and Research File was queried to identify adult patients listed for heart transplantation with a LVAD-related complication in the United States between 2018 and 2023. The cumulative incidence of heart transplantation, mortality on device, and overall mortality following complication were assessed. ResultsDuring the study period, 792 patients experienced an LVAD complication that led to an initial listing or change in urgency status. Device infection was the most frequent complication (n=472, 59.6%), followed by device malfunction (n=80, 10.1%), aortic regurgitation (n=73, 9.2%), ventricular arrhythmias (n=46, 5.8%), thrombosis/hemolysis (n=43, 5.4%), bleeding (n=42, 5.3%), and right heart failure (n=36, 4.5%). At 1 year, transplantation incidence was 71.5% (95% CI, 67.9-74.8%), mortality on device was 3.8% (95% CI, 2.5-5.4%), and overall mortality was 12.3% (95% CI, 9.9-15.1%). Right heart failure was associated with increased 1-year mortality (34.1%, 95% CI, 18.2-50.8%; adjusted HR 2.0, 95% CI, 1.1-3.8). ConclusionsThe revised allocation system provides LVAD patients with complications timely access to transplantation, reflected in high transplant rates and low mortality. Right heart failure remains a distinct subgroup, with one-third of patients not surviving to one year, suggesting this complication may warrant consideration for higher urgency status.

BackgroundPreoperative cardiovascular (CV) risk stratification is essential in non-cardiac surgery, but conventional testing is frequently overused, increasing costs without improving outcomes. Artificial intelligence (AI)-enabled electrocardiography (ECG) may enhance perioperative risk assessment by identifying patients at very low risk for adverse events. ObjectiveThis study aimed to evaluate whether AI-ECG-based risk stratification could help maintain safety and decrease potentially avoidable preoperative CV testing, while reducing the associated costs, in patients undergoing non-cardiac surgery. MethodsWe retrospectively analyzed 41,218 patients (46,135 ECG-surgery pairs) undergoing elective non-cardiac surgery at Seoul National University Bundang Hospital (2020-2021). An AI-ECG algorithm generated eight probability scores for cardiac conditions, classifying patients as low- or high-risk. Based on the performance and results of preoperative cardiovascular testing (transthoracic echocardiography, coronary computed tomography angiography, single-photon emission computed tomography, or coronary angiography), patients were classified as no test, negative test, or positive test. The primary endpoint was a 30-day composite of all-cause mortality, unplanned percutaneous coronary intervention, or prolonged mechanical ventilation ([&ge;]3 days). ResultsAI-ECG classified 92.4% of patients as low-risk, with an event rate of 0.62% versus 6.04% in high-risk patients. Preoperative CV testing was performed in 11.8% of cases, with only 16.3% yielding positive findings. In AI-ECG low-risk patients, event rates were uniformly low (0.6-2.7%) regardless of testing, whereas in high-risk patients, rates were consistently high (5.3-6.2%), suggesting no additional prognostic value of conventional testing. An AI-ECG guided approach could reduce potentially avoidable preoperative testing by 35.7% while preserving safety. Integrating AI-ECG with established risk tools may better delineate patients who truly require preoperative CV testing while conserving medical resources. ConclusionAI-enabled ECG reliably identified surgical candidates at low risk for postoperative complications, for whom additional CV testing may be potentially avoidable. Integrating AI-ECG with conventional risk tools may optimize resource use and minimize redundant testing without compromising outcomes. Prospective studies are needed to confirm clinical and economic benefits. Trial RegistrationN/A

BackgroundCongenital Heart Disease (CHD) research must focus on outcomes that affect the whole-of-life course. To achieve this, datasets with long term follow up and patient-relevant outcomes are required. This paper reports on the linkage of The Australian and New Zealand Congenital Heart Disease Registry (ANZCHD Registry) (>80 000 unique individuals) with Australian National Administrative Health records and describes the final dataset. MethodsLinkage on two cohorts was conducted by accredited linkage agencies, after all appropriate Ethics and Governance approvals. Cohort 1 included people who were identified from the ANZCHD Registry and Cohort 2 included people with an inpatient admission with a CHD diagnosis who had not been identified in Cohort 1. Healthcare events linked from 2010 to 2024 included outpatient encounters and medications, hospital admissions and emergency department presentations. Linked data was cleaned and curated to minimize the impacts of errors from the probabilistic linkage process. ResultsThe final dataset included 94,383 subjects with structural CHD (58,523 from Cohort 1 and 35,860 from Cohort 2). There were over 35 million linked healthcare events recorded for this population, from 2010 to 2025. Cohort 1 was younger by an average of 14 years (95% CI: 13.2 - 13.9, p<0.001) and had a higher proportion of severe CHD lesions (20%) compared to Cohort 2 (6%) ({chi}2 = 7433.1, p<0.001). ConclusionsThe linkage described here represent a significant enrichment of the large and comprehensive Australian National CHD Registry. This will provide important research infrastructure that will enable better quality research in CHD. Key MessagesO_LIWe sought to link the Australia and New Zealand Congenital Heart Disease Registry with comprehensive, national Australian administrative healthcare records. C_LIO_LIThe final dataset included a total of 95,383 individuals with over 35 million healthcare events from 2010 to 2025. C_LIO_LICongenital Heart Disease is a whole-of-life condition with a growing and ageing population and comprehensive datasets such as these need to be made available to improve healthcare for people with Congenital Heart Disease. C_LI

BackgroundThere are controversies about whether atrial fibrillation (AF) type, paroxysmal (PaAF) vs persistent (PeAF), affects stroke risk. The type of AF is still not included in most risk stratification tools. ObjectiveWe aim to assess differences in stroke outcomes between PaAF and PeAF patients in both low- and high-CHA2DS2-VASc groups. MethodsWe conducted an epidemiological study of all patients admitted to Tulane Medical Center with the diagnosis of AF from January 2010 to March 2020. Data were extracted from the regional US electronic medical records database, Research Action for Health Network (REACHnet), for all patients aged 18 years or older with a diagnosis of AF. Patients were divided into four groups: a low CHA2DS2-VASc score was defined as CHA2DS2-VASc < 2 in women and CHA2DS2-VASc < 1 in men. PeAF was defined as a patient with at least one episode of AF lasting 7 days or more. PaAF was defined as a patient with AF with no episode lasting more than 7 days. The outcome of the study was an ischemic stroke event or a transient ischemic attack that occurred after the diagnosis of AF. Kaplan-Meier curves and the log-rank test were used to compare the study outcomes across all four groups. Multivariable Cox regression was performed to adjust for the use of anticoagulants. ResultsA total of 1,079 patients were included in the study. 576 patients had PaAF and high CHA2DS2-VASc, 215 had PaAF and low CHA2DS2-VASc, 214 had PeAF and high CHA2DS2-VASc, and 74 patients were PeAF, and low CHA2DS2-VASc. Patients were followed up over 5 years. Based on the Log-rank test, there were significant differences among the four groups (p < 0.001). After adjusting for anticoagulants, patients with high CHA2DS2-VASc appeared to have more strokes on follow-up than patients with low CHA2DS2-VASc, independent of AF type and anticoagulation prescription. For the Cox model, when the PaAF High CHA2DS2-VASc group was used as the reference, both low CHA2DS2-VASc groups, PaAF (0.032 [0.012-0.081], p < 0.001) and PeAF (0.032 [0.008-0.135], p < 0.001), had a lower risk of stroke. However, there was no difference in stroke when the reference group was compared to high CHA2DS2-VASc, PeAF (1.169 [0.866 - 1.576], p=0.308). ConclusionIn our database, the CHA2DS2-VASc score remained superior to the type of AF when predicting stroke outcome. Type of AF did not affect stroke outcome even after adjusting for CHA2DS2-VASc and for anticoagulation prescription.

BackgroundAtrial cardiomyopathy (AtCM) is both a cause and a consequence of atrial fibrillation and flutter (AF) and can lead to ischemic stroke. Imaging derived left atrial (LA) structure and function are used to diagnose AtCM. Considering the tight coupling of heart structure and rhythm generation, this information might also be derived from 12-lead electrocardiogram (ECG), which is low-cost and readily available. MethodsFirst, we finetuned a deep learning ECG foundational model (ECG-FM) pretrained on over 1 million ECG samples to predict LA imaging indices based on 26134 ECGs from the UK Biobank cohort. We then investigated if the ECG-predicted imaging features improved detection of patients with previous diagnosis of AF as well as prediction of incident AF beyond the CHARGE-AF Score on a test set from the UK Biobank. We externally validated our model on a Brazilian cohort of primary care ECGs (n = 64851) as well as a cohort of ischemic stroke patients from the University Hospital Zurich (n = 312) ResultsOur deep learning model successfully predicted LA imaging indices from 12 lead ECG with Pearson correlation of predictions and ground truths ranging from 0.41 - 0.52 (p < 0.001). In the UK Biobank test set, the ECG-predicted imaging features significantly improved detection of participants with previous AF diagnosis and five-year risk prediction of incident AF beyond the CHARGE-AF score. ECG-predicted imaging markers showed superior test performance compared to established ECG markers of AtCM and an alternative deep learning approach trained to detect patients with previous diagnosis of AF directly. This also held on external validation sets. We further successfully validated our model on Holter-ECG with reduced number of leads. DiscussionWe established a novel deep learning approach for the diagnosis of AtCM from 12 lead ECG. Due to the wide availability of ECG, our approach has the potential to improve screening and diagnosis of AtCM. The code for the analysis is available under: https://github.com/jul-des/DL-AtCM.git Clinical PerspectiveWhat is new? O_LILeft atrial imaging indices derived from cardiac magnetic resonance imaging can be predicted from 12 lead sinus rhythm ECG using deep learning. C_LIO_LIThe predicted imaging indices allow improved diagnosis of patients with previous episodes of atrial fibrillation, as well as prediction of incident atrial fibrillation. C_LIO_LIFurther, they allow improved detection atrial fibrillation as cause of stroke in a cohort of ischemic stroke patients. C_LI What are the clinical implications? O_LIWe developed and validated a novel approach for diagnosing atrial cardiomyopathy from 12 lead ECG. C_LIO_LIIt has the potential to improve diagnosis of atrial cardiomyopathy, which is key for preventing atrial fibrillation and ischemic stroke. C_LI