European Heart Journal

Population-level data on preclinical heart failure (HF) remain limited because most epidemiological studies focus on symptomatic HF. We therefore developed an administrative-data algorithm to classify HF stages across the national population and describe temporal trends, stage transitions, and mortality across the HF continuum. Methods Using a claims-based staging framework adapted from the Universal Definition of HF, we classified HF stages from ICD-10 codes, prescription records, and medical procedures. We applied this algorithm to the Czech population, linking the National Registry of Reimbursed Health Services to National mortality records from 2015 to 2024. Results In 2024, 27.8% of the Czech population met criteria for Stage A HF and 8.2% for Stage B. Over 10 years, the prevalence of both preclinical stages increased beyond what could be explained by population aging alone, with age-standardized prevalence rising by 9.5% for Stage A and 19.2% for Stage B. Age-standardized 1-year mortality showed a steep stepwise gradient, from 0.69% in Stage A to 1.69% in Stage B, 3.06% in Stage C, and 7.27% in Stage D. Among 52,172 individuals with incident clinical HF in 2024, more than 95% had previously met administrative criteria for Stage A or Stage B. Conclusion Administrative surveillance of the HF continuum using routinely collected healthcare data provides a scalable administrative framework for population-level monitoring of HF burden. In Czechia, both preclinical and clinical HF burdens increased over time beyond population aging alone, underscoring the need for earlier preventive strategies targeting preclinical disease.

Background: Left ventricular (LV) remodeling is associated with impaired cardiac function and future cardiovascular disease (CVD). Current remodeling definitions use broad categorizations based on hypertrophy and mean wall thickness. Cardiac magnetic resonance (CMR) imaging provides detailed characterization of regional myocardial wall properties, which may enhance sex-specific cardiovascular disease (CVD) risk stratification. Objectives: We aimed to identify detailed LV remodeling patterns, their associations with CVD risk, and their clinical predictors. Methods: LV wall thickness data were obtained by CMR in three independent population-based cohorts (SHIP-TREND-0, n=931; SHIP-START-2, n=490; KORA-FF4, n=368). Sex-specific remodeling patterns were identified by k-means clustering and associated with established CVD risk scores and incident morbidity and all-cause mortality. Bootstrapped multinomial regression with LASSO regularization was used to select relevant clinical predictors of remodeling patterns. Results: The sample comprised 991 men (mean age 52.9 years, prevalent CVD 7.8%) and 798 women (52.5 years, 2%). Four remodeling clusters were found for men and women, respectively. For a subset of these clusters, significant associations with an increased CVD risk were found, e.g. in women, the high-risk cluster was associated with a 10.6 (95% confidence interval: 8.9, 12.3) percentage point increase in the 10-year Framingham Risk Score. Associations were independent of blood pressure and myocardial mass. Only in women, associations were also independent of average wall thickness and LV concentricity. Variable selection identified distinct clinical predictors of remodeling patterns. Conclusion: Particularly in women, regional LV wall thickness patterns detect unfavorable cardiac remodeling and might improve CVD risk stratification beyond existing strategies. Automated implementation during image acquisition and integration with shape-based models may facilitate clinical application.

Background and Aims: Epicardial adipose tissue (EAT) has emerged as an important cardiovascular biomarker that reflects both inflammatory and cardiometabolic risk. EAT volume and density vary significantly across populations, yet there is a lack of multicenter studies investigating the predictive value of population-specific EAT percentiles. Methods: In this multicenter study, we retrospectively analyzed low-dose computed tomography correction scans from 42,842 patients undergoing myocardial perfusion imaging. A derivation cohort of 15,082 patients was used to establish sex- and age-specific nomograms for EAT density and EAT volume indexed to body surface area. Percentile-based thresholds were tested for outcome prediction in a validation cohort of 27,760 patients. For clinical implementation, we developed an online EAT percentile calculator. Results: Percentile curves demonstrated increased BSA-indexed EAT volume and decreasing EAT density with age. Over a median follow-up of 3.6 years (IQR: 1.83 - 5.14), 4,956 patients experienced a nonfatal myocardial infarction or death. In multivariable Cox models, patients above the 95th sex- and age-specific percentile had significantly worse outcomes for BSA- indexed EAT volume [adjusted hazard ratio 1.30, 95% CI: 1.14 - 1.49, p < 0.001] and EAT density [adjusted hazard ratio 1.7, 95% CI: 1.51 - 1.92, p<0.001] when compared to patients below the 50th percentile (p<0.001). Conclusion: Age- and sex-specific EAT percentiles provide a clinically interpretable framework for contextualizing automated EAT measurements and identifying patients at increased cardiovascular risk. EAT density was a stronger prognostic marker and identified elevated risk even among patients with normal BMI, supporting its potential to provide information beyond conventional anthropometric assessment.

Background: Obesity is a major modifiable risk factor for heart failure (HF), but body mass index (BMI) does not distinguish biologically distinct fat depots. Whether imaging-derived adipose tissue depots capture specific cardiometabolic pathways underlying HF risk beyond conventional anthropometric measures remains uncertain. Methods: We performed two-sample Mendelian randomization (MR) and multivariable MR mediation analyses using published genome-wide association study summary statistics. General adiposity traits included BMI, waist-to-hip ratio (WHR), and WHR adjusted for BMI from GIANT and UK Biobank meta-analyses of up to 694,649 individuals. MRI-derived visceral adipose tissue (VAT), abdominal subcutaneous adipose tissue (ASAT), and gluteofemoral adipose tissue (GFAT) were derived from 38,965 UK Biobank participants. HF outcome data were obtained from the HERMES consortium, including 1,946,349 individuals and 153,174 HF cases. Cardiometabolic mediators included type 2 diabetes (T2D), systolic blood pressure (SBP), LDL cholesterol, HDL cholesterol, and triglycerides. Primary analyses used inverse-variance weighted MR, with sensitivity analyses and directionality testing. Mediation was estimated using joint multivariable MR conditioning on significant cardiometabolic mediators. Results: Among MRI-derived adipose depots, ASAT was the only trait significantly associated with HF risk (odds ratio [OR] 1.64 per 1-SD increase; 95% CI 1.40-1.93; FDR q<0.001). VAT showed a positive but imprecise association (OR 1.38; 95% CI 0.87-2.20), and GFAT was not associated with HF. Among general adiposity measures, BMI (OR 1.65; 95% CI 1.58-1.71) and WHR (OR 1.30; 95% CI 1.23-1.38) were robustly associated with HF, whereas WHR adjusted for BMI was not. ASAT was significantly associated with T2D, SBP, HDL cholesterol, and triglycerides, but not LDL cholesterol. In joint multivariable MR, 67.9% of ASAT's HF effect was mediated through T2D, SBP, HDL cholesterol, and triglycerides (95% CI 49.2-86.8%). In contrast, BMI demonstrated only 8.5% mediation (95% CI -7.4 to 24.4%), and WHR showed non-significant mediation of 36.7% (95% CI -8.3 to 81.7%). Conclusions: MRI-derived abdominal subcutaneous adipose tissue captures a biologically coherent cardiometabolic signal underlying HF risk that is diluted by conventional anthropometric measures. ASAT may represent an imaging biomarker of metabolic syndrome-mediated HF risk and could support more precise risk stratification and mechanistically targeted prevention in HF.

Purpose A planar Perugini grade 1 scan is equivocal for transthyretin amyloid cardiomyopathy (ATTR-CM), has no dedicated management pathway and is biologically heterogeneous, spanning early or low-burden ATTR, AL amyloidosis and non-amyloid blood-pool activity. Rather than investigating grade 1 as a step toward amyloid confirmation, we characterized the clinical phenotype it marks and tested whether that phenotype, rather than amyloid, drives its prognosis. Methods We studied 9,170 consecutive patients who underwent [99mTc]Tc-DPD bone scintigraphy between 2010 and 2020, graded on planar imaging by blinded expert consensus. Using an imaging-phenotype association (IPA) framework analogous to genome-wide association analysis, we related Perugini grade to 1,243 clinical, laboratory, echocardiographic, cardiac magnetic resonance and ICD-10 comorbidity variables from electronic health records, adjusting for age, sex and cancer history. The outcome was a composite of heart failure hospitalization or death. Results Grade 1 occurred in 175 (1.9%) and grade ?2 in 142 (1.6%) patients. Grade ?2 reproduced the canonical infiltrative ATTR phenotype, with greater septal thickness, higher extracellular volume, neuropathy and atrial fibrillation, validating the framework. Grade 1 instead marked a non-infiltrative cardiometabolic phenotype dominated by hypertension, chronic ischemic heart disease, high BMI, anemia, reduced eGFR and atrial enlargement, with low extracellular volume and septal thickness arguing against meaningful infiltration. Grade 1 carried worse outcomes than grade 0 (adjusted HR 1.30, 95% CI 1.07 to 1.59). Within grade 1, a cardiometabolic phenotype defined by hypertension, high BMI and low hematocrit, with or without chronic ischemic heart disease, identified patients whose risk matched or exceeded that of grade ?2 (median HR 2.46 versus HR 1.76), indicating that grade 1 prognosis is driven substantially by comorbid cardiometabolic disease rather than amyloid burden. Conclusions Perugini grade 1 is neither uniform early amyloid nor a benign artifact but a mixed-etiology, predominantly non-infiltrative cardiometabolic phenotype that carries clinically meaningful risk. A simple cardiometabolic phenotype stratifies this risk, supporting reinterpretation of grade 1 and a management focus on cardiorenal comorbidity alongside selective amyloid workup. The IPA framework provides a scalable approach to characterizing cardiovascular imaging biomarkers.

Background Targeted pharmacologic therapies for transthyretin amyloid cardiomyopathy (ATTR-CM) improve survival; however, their effects on cardiac structural parameters remain incompletely defined. Objectives To evaluate the pooled effects of disease-modifying therapies for ATTR-CM on echocardiographic structural parameters. Methods In accordance with PRISMA guidelines, we performed a systematic review and meta-analysis of randomized controlled trials and observational studies published through March 2025 assessing transthyretin stabilizers and RNA-silencing therapies in adults with cardiac amyloidosis. Outcomes included changes in global longitudinal strain (GLS), left ventricular ejection fraction (LVEF), interventricular septal (IVS) thickness, left ventricular mass, stroke volume, E/e? ratio, and LV end-diastolic volume. Pooled between-group mean differences were calculated using random-effects models. Sensitivity analyses were performed. Results Eighteen studies (11 randomized, 7 observational) encompassing 3,646 patients were included. Compared with control, drug therapy was associated with attenuation of GLS decline (mean difference [MD] -0.69%; 95% CI -1.10 to -0.29; P<0.001) and preservation of LVEF (MD 1.62%; 95% CI 0.73 to 2.51; P<0.001). Treatment was also associated with reduced worsening of E/e? ratio, and preservation of stroke volume. No significant between-group differences were observed for IVS thickness, LV mass and LV end-diastolic volume. Within-group analyses showed no change in echocardiographic parameters between baseline and follow-up in treated patients, in contrast to significant worsening in the control cohort. Conclusions Disease-modifying therapies for ATTR-CM are associated with stabilization and attenuated progression of cardiac remodeling rather than reversal of structural abnormalities.

Background: Cardiovascular disease (CVD), cardiometabolic and neurocognitive conditions share risk factors and frequently co-occur. We evaluated whether four established CVD risk prediction models (QRISK3, PCE, SCORE2, SCORE2-OP) can be repurposed to predict 10-year risk of these conditions and their co-occurrence with CVD. Methods: The models were recalibrated using 20% of the UK Biobank (UKB) and evaluated in the remaining 80%. We performed external validation using data from Clinical Practice Research Datalink (CPRD) Aurum, assessing model discrimination (c-statistics) and calibration (intercept and slope). We used permuted feature importance to determine the influence of each individual predictor in the models. Results: Depending on the model, the c-statistics for incident CVD ranged from 0.71 to 0.74 in the UKB test set (16,137 events). Discrimination was equal to or higher than CVD when evaluated against non-traditional CVD outcomes: 0.74 to 0.77 for heart failure (3,471 events), 0.72 to 0.73 for atrial fibrillation (9,213 events), 0.73 to 0.75 for peripheral arterial disease (1,927 events) and 0.80 to 0.82 for abdominal aortic aneurysm (595 events). For the multimorbidity endpoints, model discrimination ranged from 0.74 for the composite of CVD and T2DM (SCORE2-OP) to 0.83 for the composite of CVD and dementia or Parkinson's disease (QRISK3). When considering the onset of any cardiovascular, cardiometabolic, or neurocognitive outcome discrimination ranged from 0.71 to 0.72. The repurposed models slightly underestimated the predicted risk in the CPRD compared to the UKB: average difference in calibration intercept was at most -0.64. After age and sex, smoking status and systolic blood pressure contributed most to model predictions. Conclusions: Repurposed CVD models can be used to identify 10-year risk of many CVD-related conditions and their multimorbidity. These may be used to support risk-based approaches to prevention and screening. The repurposed models have been made available at: https://repurposed-cvd-risk-models.shinyapps.io/cvd_cmd_dementia_app/ Keywords: Risk prediction; cardiovascular disease; cardiometabolic disease; dementia; disease prevention.

Background: Atrial fibrillation detected after stroke (AFDAS) is frequently diagnosed after embolic stroke of undetermined source (ESUS) and has important implications for secondary stroke prevention. Although prediction scores have been proposed to identify patients at increased risk of AFDAS, prospective evidence supporting their implementation to guide rhythm monitoring in routine clinical practice is limited. Methods: In this prospective, population-based implementation cohort study, adults with ESUS were enrolled between January 2022 and December 2024 across all stroke centers in Styria, Austria. The Graz AF Risk Score was prospectively implemented as part of a risk-adapted diagnostic pathway for cardiac rhythm monitoring. Patients with a score [&ge;]4 were recommended for implantable loop recorder monitoring, whereas monitoring in those with scores <4 remained at the treating physician's discretion. The primary outcome was AFDAS detection; recurrent ischemic stroke and recurrent stroke etiology were secondary outcomes. Results: Among 784 patients (median age 73 years [IQR 64-80], 45.7% women), AFDAS was detected in 166 patients (21.2%) during a median follow-up of 26.3 months (IQR 20-34). AFDAS detection was substantially higher in patients with a Graz AF Risk Score [&ge;]4 than <4 (38.1% vs. 3.9%; p<0.001). After adjustment for age, sex and ILR monitoring, a score [&ge;]4 independently predicted AFDAS (HR 6.3, 95% CI 3.5-11.2; p<0.001) and recurrent ischemic stroke (HR 2.2, 95% CI 1.1-4.1; p=0.023). Only one recurrent stroke in patients with a score <4 was attributable to atrial fibrillation (AF) (1/18, 5.6%). Conclusions: Prospective implementation of the Graz AF Risk Score identified patients with ESUS at markedly different risks of AFDAS. A Graz AF Risk Score [&ge;]4 was also independently associated with recurrent ischemic stroke. These findings support a risk-adapted approach to cardiac rhythm monitoring after ESUS.

BackgroundHeart failure with preserved ejection fraction (HFpEF) is a heterogeneous syndrome associated with metabolic stress, hypertension, systemic inflammation, and microvascular dysfunction. Early cell-type-specific events and intercellular communication programs that accompany disease onset and progression remain poorly defined. MethodsWe performed a longitudinal study of HFpEF progression in high-fat diet (HFD)+L-NAME mice at control/baseline (0 weeks, 0w/Ctrl), early (1w), intermediate (4w), and established (8w) stages. Metabolic, hemodynamic, exercise, echocardiographic, and single-cardiomyocyte function were assessed. Cardiac non-cardiomyocytes (non-CMs) were profiled by single-cell RNA sequencing (scRNA-seq), with bulk RNA-seq for tissue-level comparison. Endothelial remodeling was assessed in an L-NAME-independent HFD plus mild transverse aortic constriction model (HFD+mTAC) and a published human HFpEF single-nucleus RNA-seq cohort. An endothelial-macrophage adhesion assay tested whether HFpEF-mimic stress promotes endothelial activation and macrophage adhesion. ResultsIn the HFD+L-NAME model, metabolic dysfunction, hypertension, reduced exercise tolerance, abnormal diastolic filling with preserved ejection fraction, and altered cardiomyocyte calcium handling were detected by 1w and persisted through 8w. Bulk RNA-seq showed progressive remodeling, with limited change between 8w and 12w, guiding scRNA-seq timepoint selection. scRNA-seq of 94,848 cardiac non-CMs identified nine major populations with stage-dependent remodeling. Endothelial cells (ECs) were recovered in high proportion and showed an early, pronounced transcriptional response, with inflammatory, adhesion, interferon-response, migratory, and vascular-remodeling programs emerging by 1w. Related EC activation signatures were observed in HFD+mTAC and human HFpEF data. Functionally, HFpEF-mimic stress increased adhesion and chemokine expression in human ECs and enhanced macrophage adhesion. Fibroblast matrix remodeling occurred at later stages, while macrophages progressively shifted toward inflammatory states. CellChat suggested stage-dependent communication remodeling from early endothelial-immune interactions toward later macrophage-fibroblast crosstalk. ConclusionTime-resolved scRNA-seq reveals coordinated, stage-dependent remodeling of the cardiac microvascular and interstitial microenvironment during HFpEF progression. Early endothelial activation emerges before later fibroblast matrix remodeling and inflammatory macrophage remodeling, identifying candidate cell states and signaling pathways for future mechanistic investigation. Clinical PerspectiveO_ST_ABSWhat Is New?C_ST_ABSO_LIThis study provides a time-resolved single-cell atlas of the cardiac non-cardiomyocyte compartment across baseline, early, intermediate, and established stages of HFpEF progression, rather than a single late-stage snapshot. C_LIO_LIEndothelial cells exhibit early inflammatory, adhesion, interferon-response, and vascular-remodeling programs within the first week of disease, preceding the later predominance of fibroblast matrix remodeling and inflammatory macrophage remodeling. C_LIO_LIThis endothelial activation signature is supported across two mechanistically distinct HFpEF mouse models and aligns with endothelial inflammatory and vascular-remodeling programs in human HFpEF myocardium, supporting its translational relevance. C_LI What Are the Clinical Implications?O_LIEarly endothelial activation may represent a targetable stage of HFpEF pathogenesis that arises before more established structural and fibrotic remodeling. C_LIO_LITherapeutic strategies aimed at limiting endothelial inflammatory activation or endothelial-immune interactions may help attenuate downstream vascular, immune, and stromal remodeling in HFpEF. C_LIO_LIThese findings provide a preclinical foundation for future longitudinal human studies testing whether early endothelial activation can serve as a biomarker, therapeutic target, or disease-staging feature in HFpEF. C_LI

Background: Adults aged [&ge;]75 years represent a rapidly growing population at risk of acute myocardial infarction (AMI), yet they remain markedly underrepresented in statin trials for primary prevention. The limited evidence base, together with multimorbidity, functional heterogeneity, and competing mortality risks, has contributed to uncertainty regarding the potential role of statins in very old adults. This study evaluated the association between baseline statin exposure and incident AMI among community-dwelling adults aged [&ge;]75 years without prior cardiovascular disease. Methods: We conducted a retrospective population-based cohort study using linked primary-care, hospital, laboratory, and pharmacy dispensing data from the Community of Madrid. Statin exposure was ascertained during a 24-month exposure-assessment period from 1 January 2018 to 31 December 2019 and classified at a landmark date of 1 January 2020, when outcome follow-up began. Participants were classified as exposed if they had received at least two statin dispensations during the exposure-assessment period and had no record of prior lipid-lowering therapy before 2018. Individuals with prior cardiovascular disease, type 1 diabetes, cancer, dementia, or advanced chronic kidney disease were excluded. Missing data were addressed using multiple imputation. The association between baseline statin exposure and incident AMI was estimated using multivariable Cox proportional hazards regression. Propensity-score matching and Fine-Gray competing-risk regression, with all-cause mortality as the competing event, were performed as sensitivity analyses. Results: Among 174,014 individuals included in the final cohort, 32,698 (18.8%) met the criteria for baseline statin exposure. The mean age was 82.5 years. During a median follow-up of 5 years, AMI occurred in 533 (1.63%) statin-exposed individuals and 2722 (1.93%) non-exposed individuals (p=0.0003). The observed absolute risk difference was 0.30 percentage points (95% CI, 0.14-0.45), corresponding to an estimated observational number needed to treat of 338 over 5 years (95% CI, 222-708). In the fully adjusted Cox model, baseline statin exposure was associated with a lower risk of incident AMI (HR, 0.805; 95% CI, 0.731-0.887). In the full-cohort Fine-Gray model accounting for competing mortality, baseline statin exposure remained associated with a lower cumulative incidence of AMI (sHR, 0.823; 95% CI, 0.748-0.905). After propensity-score matching, the association remained in the competing-risk analysis (subdistribution HR, 0.852; 95% CI, 0.738-0.983). Conclusions: In this large population-based cohort of adults aged [&ge;]75 years without prior cardiovascular disease, baseline statin exposure was associated with a lower incidence of AMI across several analytical approaches. The observed absolute risk difference was modest, and the findings should be interpreted in light of the observational design, residual confounding, and the potential for selection related to survival to the landmark date. Further randomized evidence is needed to determine whether this association reflects a causal effect of statin therapy in very old adults. Keywords: Statins; primary prevention; acute myocardial infarction; aged [&ge;]75 years; landmark analysis; competing risks; propensity-score matching; real-world data.

Background: Adults with congenital heart disease (ACHD) represent a rapidly expanding population with evolving mortality patterns. Despite improved survival, excess mortality persists. Objective: To evaluate the incidence, causes, and predictors of mortality in a contemporary ACHD cohort. Methods: We performed a retrospective cohort study of adults (?18 years) first evaluated at Mayo Clinic from 2002?2023. Baseline clinical, imaging, and electrocardiographic data were analyzed. Vital status was determined using institutional records and the Accurint national mortality database. Kaplan-Meier analysis and Cox proportional hazard models were used to evaluate mortality and identify independent predictors of mortality Results: A total of 7,678 ACHD patients were included, with median age of 36.8 years and median follow-up of 11.4 years. During 78,768 patient-years of follow-up, 1,116 patients died (median age at death 57.2 years), corresponding to an annual mortality rate of 1.4%. The cumulative rate of all-cause mortality at 5, 10, 15, and 20 years was 6.9%, 12.0%, 19.0%, and 26.2%, respectively. When stratified by CHD complexity, the annual death rate in patients with severe CHD (2.4%/year) was twice that of patients with moderate or mild CHD (both 1.2%/year). Older age and ACHD subtypes, specifically, cyanotic heart disease (HR 3.9, 95% CI 2.9?5.3) and Fontan physiology (HR 3.2, 95% CI 2.3?4.4), were strongly associated with increased mortality. Additional independent predictors included male sex, ventricular dysfunction, advanced NYHA class, prior heart failure hospitalization, hypertension, smoking, coronary artery disease, renal dysfunction, and abnormal hemoglobin levels. Cardiovascular causes accounted for 57.7% of deaths with known etiology, predominantly heart failure (48.9%) and sudden cardiac death (21.9%), while non-cardiovascular causes were driven mainly by infection and malignancy. Conclusions: In this large contemporary ACHD cohort, mortality was driven by ventricular dysfunction, heart failure, and systemic end-organ involvement in addition to the underlying congenital anatomy. Both cardiovascular and non-cardiovascular causes contributed significantly to mortality. These findings underscore the need for comprehensive multidisciplinary ACHD care focused on early recognition of cardiac functional decline, management of acquired comorbidities, and end-organ dysfunction.

Background Structural heart disease (SHD) drives heart failure and cardiovascular mortality but remains underdiagnosed, and echocardiography is limited as a population-level screening tool. Objectives We evaluated whether a composite artificial intelligence-enabled electrocardiogram (AI-ECG), combining independently developed models for left ventricular systolic (LVSD) and diastolic dysfunction (LVDD), identifies prevalent and predicts incident SHD across diverse populations. Methods In this multinational cohort study, detection was assessed cross-sectionally in a Korean clinical cohort (Incheon Sejong Hospital) and a US dataset (Columbia University Irving Medical Center), and incident risk was assessed in the Korean cohort and the UK Biobank among individuals without baseline SHD or heart failure. Adults with paired ECG and echocardiography were analyzed for detection, with the composite defined as positive on either model. SHD comprised reduced left ventricular ejection fraction, moderate or severe valvular disease, left ventricular hypertrophy, or pulmonary hypertension. Detection was assessed by sensitivity and specificity, and incident risk by Cox models and the C statistic. Results Among 46,082 and 36,286 participants in the two detection cohorts, the composite detected SHD with sensitivity of 71.8% and 76.1% and specificity of 88.3% and 70.1%, with positivity across all phenotypes. Among at-risk individuals, composite positivity was associated with incident SHD (hazard ratios, 3.75 and 2.75), with C statistics of 0.69 to 0.78. Conclusions A composite AI-ECG identified prevalent and predicted incident SHD across multinational cohorts, capturing signals beyond its training targets and supporting its potential as a scalable cardiovascular screening tool; whether ECG-based risk stratification improves outcomes requires prospective evaluation.

Background: Right ventricular (RV) function predicts survival in pulmonary hypertension (PH) and other cardiovascular diseases, yet echocardiographic AI has largely focused on the left ventricle (LV). Objectives: To develop and evaluate PH-ECHO-AI, a unified deep learning model performing four-chamber segmentation, landmark localisation, biventricular ejection fraction (EF) estimation, deformation analysis, and PH prediction from a single apical four-chamber (A4C) clip. Methods: We developed the model using 8,416 clips from four public datasets and no institutional data: EchoNet-Dynamic, CAMUS, RVENet (apical four-chamber clips paired with 3D-echocardiographic right ventricular ejection fraction, RVEF), and MIMIC-IV-ECHO. Evaluation used held-out, training-excluded data with expert-reviewed reference standards and a per-cohort audit of patient-level separation: 1,416 clips for segmentation; 600 clips for function and deformation (350 referenced to 3D-echocardiographic RVEF, 250 to the EchoNet LVEF); and 1,076 MIMIC-IV patients for PH prediction, with five-fold cross-validation. Performance measures were Dice, correlation, mean absolute error (MAE), Bland-Altman agreement, and area under the receiver operating characteristic curve (AUC). Results: Four-chamber segmentation generalised robustly across all datasets (pooled Dice: LV 0.925, RV 0.836, LA 0.910, RA 0.904). Left ventricular ejection fraction (LVEF) was estimated with r=0.845 (95% CI 0.786 to 0.886) and MAE 4.67%. RVEF, regressed directly from the clip by a supervised head trained on 3D-echocardiographic labels with no geometric assumption, reached r=0.754 (95% CI 0.690 to 0.806) and MAE 4.98%, matching published single-view RVEF ceilings and exceeding geometric RV fractional area change (RVFAC; r=0.278). Deformation and excursion metrics, namely RV free-wall and LV A4C longitudinal strain and tricuspid and mitral annular plane systolic excursion (TAPSE, MAPSE), proved physiologically coherent. Segmentation generalised to the external MIMIC-IV cohort, and PH prediction was developed and evaluated entirely within it; RVEF evaluation was clip-disjoint and same-source, so cross-centre RVEF validation remains outstanding. Using echocardiographic geometry alone, confirmed PH was detected with an AUC of 0.697 and strong calibration (Brier 0.061). Conclusions: A single, reproducible model provides comprehensive right-heart-focused interpretation from one A4C view. It achieves RVEF accuracy competitive with dedicated RV models while simultaneously delivering segmentation, deformation, annular excursion (TAPSE and MAPSE), and PH prediction. Registration: This retrospective study used existing datasets. Code is openly released, and trained model weights are available to credentialed investigators, for independent evaluation.

Background: The 2026 American Heart Association/American College of Cardiology (AHA/ACC) guidelines replaced the 2019 European Society of Cardiology (ESC) four-tier pulmonary embolism (PE) risk scheme with five clinical categories (A-E) and subcategories. These categories were set by expert consensus and have not been validated against outcomes. How patients are reclassified relative to ESC, or how the two systems compare prognostically, is unknown. Methods: We utilized three cohorts of patients with confirmed PE using structured electronic health record data, laboratory biomarkers, and large-language-model abstraction of radiology reports: Duke University Health System (n=12,992, drawn from 95,760 consecutive inpatient CT pulmonary angiography studies, 2014-2025, with no referral or registry enrollment step between imaging and cohort entry), INSPECT (Stanford; n=3,870), and MIMIC-IV (Beth Israel Deaconess; n=361). Patients were assigned AHA/ACC categories B through E, subcategorized where data allowed, and mapped to 2019 ESC risk strata. The primary outcome was 30-day mortality; discrimination was assessed with Harrell C-index. Results: Among 17,223 patients with confirmed PE, pooled 30-day mortality rose monotonically across categories: 1.5% (B), 8.9% (C), 15.5% (D), and 31.9% (E), with the ordering preserved in all three cohorts despite differing baseline mortality. Subcategory-level discrimination was reliable only at the high-acuity extreme (D2-E2); across subcategories C1 through D1, mortality did not order monotonically (9.2%, 10.8%, 8.1%, 10.9%), and adding subcategories to category C did not improve discrimination at Duke (C-index 0.699 vs 0.699). Category C patients lacking both echocardiography and biomarker testing (12.7% of category C) had mortality (10.4%) equal to or exceeding classified peers. Relative to ESC, the frameworks were concordant at the extremes, but 5.7%of ESC intermediate-risk patients were reclassified to category D, with modestly higher but non-significant 30-day mortality than those remaining in category C (10.8% versus 8.9%). Conclusions: Across a three-health-system cohort, the 2026 AHA/ACC framework produced a reproducible mortality gradient at the category level, with added subcategory granularity refining risk chiefly at the highest-acuity tiers. Discrimination across the broad intermediate band was limited, and reclassification from ESC fell almost entirely within this range.

BACKGROUND: Chronic coronary syndromes (CCS) remain under-characterized in Latin America, where clinical profiles may differ from high-income countries. OBJECTIVE: We aim to characterize the clinical presentation, coronary anatomic profile, and pharmacologic treatment patterns of adults living with CCS using data from the Mexican Chronic Coronary Syndrome Registry (RESINCCRO). METHODS: RESINCCRO is an observational, multicenter, cross-sectional registry conducted across ~50 centers in five regions from Mexico. We included adults ([&ge;]18 years) enrolled between September 2024 and March 2025 who met 2019 ESC CCS criteria. Coronary imaging data was collected from medical records into a standardized electronic case report form. RESULTS: We enrolled 3,029 adults (men [72.5%]; mean age 67.2 {+/-} 10.7 years). Cardiometabolic comorbidities were frequent: overweight/obesity (76%), arterial hypertension (69.0%), type 2 diabetes (44.0%), and chronic kidney disease (24.2%). Persistent angina/equivalents occurred in (23.9%), of which most had Canadian Cardiovascular Society class I - II (91.2%). The mean LVEF was of 53.7 {+/-} 12.0. Cardiac rehabilitation participation was (6.2%). Median LDL-C was 70 mg/dL (IQR 51 - 95) and LDL <55 mg/dL was only 26.1%, despite high prescription of lipid-lowering therapies, including statins (93.2%), ezetimibe (24.6%), and PCSK9 inhibitors (2.4%). 60.3% had obstructive epicardial disease. CONCLUSIONS: Mexican adults with CCS exhibit high cardiometabolic burden, frequent symptoms, suboptimal LDL-C goal attainment, low rehabilitation uptake, and a substantial obstructive phenotype. These findings highlight opportunities to intensify secondary prevention, adopt mechanism-directed evaluation and therapy, and expand cardiac rehabilitation to improve CCS care in Mexico.

Background No systematic review has been conducted to pool the existing evidence and quantify cancer prevalence rates in cardiovascular diseases (CVDs). We aimed to estimate pooled cancer prevalence in coronary artery disease (CAD), heart failure (HF), atrial fibrillation (AF), hypertension, type 2 diabetes mellitus (DM), stroke, peripheral arterial disease (PAD), and valvular heart diseases (VHD). Methods PubMed, Web of Science, and Scopus were searched from 2010 to July 2024. The outcomes were proportions of patients with active, any, previous, blood, solid, and metastatic cancer. The prevalence rates were estimated via one-step generalized linear mixed models. Results Totally, we retrieved 676 studies with enrollment of roughly 180 million participants. The analysis for active cancer included 59 studies with a population of 4,759,695 patients. The pooled prevalence of active cancer was 4.22% (95% confidence interval (CI) 2.18-5.32), 4.43% (95% CI 2.78-6.38), 4.60% (95% CI 1.72-8.13), 4.61% (95% CI, 2.83-6.97), 4.90% (95% CI 3.84-6.37), and 5.55% (95% CI 3.97-7.01) in patients with type 2 DM, chronic HF, any stroke, CAD, VHD, and AF. For any cancer, prevalence rates ranged from 14.10% (95% CI 12.20-15.99) in AF to 7.04% (95% CI 6.05-8.03) in CAD. Conclusion Pooled prevalence rates demonstrate a measurable burden of cancer among patients with a wide range of CVDs, highlighting the need for multidisciplinary management in this population.

Background: Heart failure with preserved ejection fraction (HFpEF) is a heterogeneous syndrome, and patients with a small left ventricular (LV) volume phenotype exhibit greater preload dependency. Whether the association between persistent outpatient oral loop diuretic use and clinical outcomes differs by LV volume phenotype remains unclear. Methods: We conducted a retrospective single-center cohort study of consecutive patients with heart failure and an LV ejection fraction [&ge;]50% who underwent transthoracic echocardiography between 2009 and 2024. Patients were stratified by LV volume phenotype using sex-specific LV end-diastolic volume index cutoffs. Associations between outpatient oral loop diuretic use and HF hospitalization, worsening renal function, and all-cause death were evaluated using multivariable time-varying Cox proportional hazards models, including interaction testing by LV phenotype. Results: Among 12,748 eligible patients, 1,437 had a small LV phenotype and 11,311 had a normal LV phenotype. Patients with a small LV phenotype initiated outpatient oral loop diuretics earlier and more frequently than those with a normal LV phenotype, while maintenance doses were comparable between groups. Outpatient oral loop diuretic use was associated with HF hospitalization in both phenotypes, with a significantly stronger association in the small LV group (hazard ratio [HR], 2.52; 95% confidence interval [CI], 1.68-3.78) than in the normal LV group (HR, 2.10; 95% CI, 1.77-2.49; P for interaction = 0.001). No significant interaction by LV phenotype was observed for worsening renal function or all-cause death. Fine-Gray competing-risk analyses showed a consistent interaction pattern. Conclusions: Persistent outpatient oral loop diuretic use was more strongly associated with HF hospitalization in patients with a small LV phenotype than in those with a normal LV phenotype. LV volume phenotype may help identify patients who warrant closer monitoring and more individualized diuretic management.

Background: Stroke remains a leading cause of mortality and long-term disability worldwide, with high residual vascular risk among survivors despite optimal secondary prevention. The contribution of inherited polygenic risk to this residual vulnerability remains unclear. Methods: We analyzed 2,701 stroke survivors (mean age 59.8{+/-}7.1 years, 61.7% male) from the UK Biobank. Stroke- and coronary artery disease (CAD)-polygenic risk scores (metaGRS), comprising approximately 3.2 million and 1.7 million genetic variants, respectively, were derived from large-scale genome-wide association studies using penalized regression. The primary outcome was major adverse cardiovascular events (MACE), while secondary outcomes included recurrent stroke and vascular risk factor control. metaGRS associations with incident MACE and recurrent stroke were tested using Cox models, whereas associations with baseline risk-factor control were assessed using logistic regression. Mediation analyses quantified indirect effects of metaGRS to MACE via HbA1c, LDL cholesterol, and blood pressure. Results: Over 12 years, 731 MACE events (27.1%) and 351 recurrent stroke events (13.0%) occurred. CAD-metaGRS was independently associated with future MACE (age- and sex-adjusted HR per SD increment 1.15, 95%CI 1.07-1.24; p<0.001), whereas higher stroke- and CAD metaGRS were both associated with poorer glycemic control. A higher CAD-metaGRS was also associated with poorer lipid control. Mediation analyses identified glycemic regulation as a significant pathway linking polygenic risk to recurrent vascular events. Conclusions: Polygenic risk scores for cardiovascular disease are associated with recurrent vascular events and vascular risk factor control among stroke survivors, pointing to potentially actionable insights in secondary prevention that merit further investigation.

Background: Cardiovascular disease (CVD) is a leading global health concern. Traditional models often miss nonlinear dependencies among physiological and behavioral factors. We hypothesized that a Transformer-based deep learning model, which excels at capturing complex patterns in structured data trained on large-scale health check-up records, would improve long-term CVD risk prediction. Methods: We analyzed longitudinal health records (2010?2024) from the Hokuriku Health Service Association (n = 100,056 without baseline CVD; development cohort). Incident CVD was defined as the first self-reported physician diagnosis of heart disease or stroke during the 10-year follow-up and was modeled as right-censored survival data. An external evaluation cohort comprised 79,756 Kanazawa City participants with health records. A Transformer model was trained using anthropometric, laboratory, and self-reported lifestyle data. Benchmarks included Cox regression, XGBoost survival embeddings, multilayer perceptron, the Framingham Risk Score, and the Hisayama Risk Score. Performance was evaluated using time-dependent area under the receiver operating characteristic curve (ROC-AUC) with a primary focus on the 10-year ROC-AUC, precision?recall AUC (PR-AUC), and concordance index (C-index). Interpretability was assessed through SHapley Additive exPlanations (SHAP) and a Feature-level Attention Network (FAN), visualizing the top 12 SHAP-ranked features to highlight key interactions. Results: In the development cohort, 4,113 CVD events (4.1%) occurred. The Transformer model achieved the best internal performance: 10-year ROC-AUC 0.821 (95% confidence interval [CI], 0.816?0.826), PR-AUC 0.427 (CI, 0.419?0.435), and C-index 0.781 (CI, 0.775?0.787). Performance remained robust externally (21,179 CVD events, 26.6%): ROC-AUC, 0.762; PR-AUC, 0.500; and C-index, 0.744. Regarding interpretability, SHAP identified age, electrocardiogram abnormality, antihypertensive medication, and sex as the most critical predictors. Notably, FAN elucidated the prognostic value of self-reported lifestyle factors. For example, daily exercise and weight gain modulated the model?s assessment of age-related risk. Within the attention network, age served as a central hub, linking these behavioral habits with physiological features. Conclusion: The Transformer-based model outperformed conventional methods in predicting long-term CVD risk. Model interpretation demonstrated the predictive utility of self-reported lifestyle factors, such as weight gain and daily exercise. These findings may support personalized CVD prevention and population-level risk stratification using routinely collected health checkup data.

Aims: Arrhythmogenic cardiomyopathy (ACM) is a genetic disease defined by arrhythmias and myocardial fibrosis with impaired cardiac function and increased risk of sudden cardiac death. Pathogenic variants are mostly identified in desmosomal genes such as desmoglein-2 (DSG2). We identified a novel disease phenotype in patients homozygous for the DSG2 variant c.523+2T>C (splice site of exon 5/intron 5), characterized by cardioembolic events in addition to classical ACM features. Here, we evaluate this new thromboembolic phenotype by comparing the clinical data to specific murine disease models. Methods and Results: We describe three unrelated patients presenting with an embolic event and/or left ventricular thrombus. Clinical evaluation revealed a shared right ventricular ACM phenotype characterized by arrhythmias, impaired function, and fibrotic remodeling. In addition, patients exhibited localized fibrotic changes of the left ventricular apex with formation of an aneurysm and predisposition to thrombus formation. Genetic analysis identified the DSG2 variant c.523+2T>C as a founder variant from the "Bernese Oberland". To elucidate the variant's functional impact, a mouse model deficient for Dsg2 exon 5 (Dsg2{Delta}ex5) was established and compared to a model carrying the adhesion-deficient Dsg2-W2A variant. Echocardiography, ECG, and histology in Dsg2{Delta}ex5 mice revealed similar disease patterns to patients and a loss of DSG2 expression. Importantly, these animals exhibited left apical fibrosis with aneurysm formation and left ventricular thrombus formation. In contrast, the Dsg2-W2A model presented with a biventricular ACM-phenotype but without left ventricular thrombi. Conclusions: We identified a novel ACM phenotype in patients homozygous for the DSG2 founder variant c.523+2T>C characterized by left ventricular apical fibrosis. Dsg2{Delta}ex5 mice recapitulate the patients' phenotype suggesting a causative link between left ventricular aneurysm due to DSG2 deficiency and thrombus formation with subsequent embolism. This highlights a novel pathological feature of ACM and the need for variant and phenotype-specific therapy.