158 papers
This study demonstrates that ECG abnormalities are significantly associated with increased cardiovascular disease risk across all PREVENT risk categories, suggesting that incorporating ECG evaluation into risk assessment could help identify low-risk individuals who may benefit from more aggressive primary prevention strategies.
This study utilizes multivariable Mendelian randomization to establish a causal link between obstructive sleep apnea and myocardial infarction, revealing that the association is partially mediated by obesity and atrial fibrillation rather than solely by traditional hemodynamic factors.
This paper introduces and validates an easily implemented ordinal logistic regression method for covariate adjustment in win ratio analyses of hierarchical outcomes, demonstrating that adjusting for prognostic variables consistently improves statistical power without compromising efficiency.
This population-based cohort study demonstrates that the modified cardiometabolic index (MCMI) is an independent, superior predictor of cardiovascular-kidney-metabolic syndrome progression and mortality compared to traditional indices, with a specific threshold of >3.5 indicating elevated risk and diabetes mediating nearly half of its association with cardiovascular death.
In patients with mixed aortic valve disease, afterload-adjusted strain provides independent and incremental prognostic value for predicting mortality and heart failure hospitalization, outperforming conventional structural remodeling indices like the integrated geometry index.
This systematic review and meta-analysis indicates that low-dose colchicine reduces major adverse cardiovascular events in patients with established atherosclerotic cardiovascular disease, though substantial heterogeneity driven by timing of initiation and cumulative dose suggests that clinical benefits vary significantly across settings, with acute-phase treatment showing no benefit compared to sub-acute or chronic administration.
This study demonstrates that a deep learning model originally trained to detect left ventricular outflow tract obstruction in hypertrophic cardiomyopathy can independently predict post-procedural obstruction following transcatheter aortic valve replacement using pre-operative single-view echocardiograms, even in patients without pre-existing obstruction.
This study identified and replicated 21 plasma proteins associated with hypertrophic cardiomyopathy severity in MYBPC3 variant carriers, prioritizing five druggable targets that offer potential for diagnosis, monitoring, and therapeutic intervention independent of genotype.
The paper introduces CLIAMDK, a modular and low-cost smartphone-based platform that achieves plate reader-level sensitivity for chemiluminescent immunoassay development, as demonstrated by its successful detection of renin and aldosterone biomarkers in patient samples.
This study developed and validated a machine learning-based pretest probability model (K-CAD) using ridge-penalized logistic regression on extensive Korean datasets, demonstrating that it significantly outperforms conventional models (UDF and CAD2) in predicting coronary artery disease risk within the Korean population.
This large-scale, propensity-matched analysis of over 167,000 patients demonstrates that participation in outpatient cardiac rehabilitation is significantly associated with improved long-term survival in individuals with chronic total occlusions, even after excluding those with other standard indications for rehabilitation.
In a cross-sectional study of patients with type 2 diabetes, researchers found that specific retinal microvascular features quantified by AI are significantly associated with subclinical cardiovascular dysfunction measures derived from cardiac MRI, suggesting retinal imaging could serve as a non-invasive window for early detection of systemic vascular disease.
This paper outlines the rationale, design, and protocol for the PREG-HEART study, a patient-driven digital cohort initiative aimed at establishing a large-scale platform to investigate the epidemiology, natural history, and optimal management of cardiovascular disease in pregnancy through direct-to-patient recruitment, national health record linkage, and future clinical trials.
This study develops superior longitudinal machine learning models that outperform traditional clinical scores in predicting six major adverse clinical endpoints for atrial fibrillation patients within a Portuguese primary care cohort, while also identifying key risk determinants and introducing a prototype decision-support tool.
Challenging the classical paradigm that arterial baroreceptors maintain a fixed blood pressure set point, this study proposes that the coefficient of variation of instantaneous baroreceptor gain (IBS CV) is the primary invariant cardiovascular variable, remaining stable across physiological stressors while blood pressure and heart rate fluctuate significantly.
This study evaluates the performance of deep neural networks and heuristic-based algorithms on single-lead wearable ECG data from children, finding that optimized heuristic models achieve comparable accuracy to complex deep learning approaches, making them highly suitable for efficient real-time digital health monitoring.
This study demonstrates that loss-of-function variants in the PCSK9 gene can mitigate the deleterious effects of an LDLR mutation, thereby protecting against Familial Hypercholesterolemia and highlighting the critical role of gene-gene interactions in the disease's phenotypic variability.
This multi-cohort study demonstrates that the AI-driven TRisk model significantly outperforms established clinical scoring systems in predicting thromboembolic and bleeding events for atrial fibrillation patients across UK and US populations, offering a pathway to reduce unnecessary anticoagulant prescriptions and generate substantial healthcare cost savings without compromising patient safety.
This multicenter study demonstrates that catheter ablation is an effective and safe treatment for supraventricular tachyarrhythmias in Fontan patients, particularly those with lateral tunnel or extracardiac conduit anatomies, which showed significantly lower recurrence rates compared to those with atriopulmonary connections.
This study demonstrates that explainable advanced electrocardiography (A-ECG) heart age exhibits excellent within-session and good between-session reproducibility in healthy young adults, supporting its reliability for cardiovascular risk communication.