Detecting structural heart disease from electrocardiograms using AI.

Early detection of structural heart disease is critical to improving outcomes, but widespread screening remains limited by the cost and accessibility of imaging tools such as echocardiography. Recent advances in machine learning applied to heart rhythm recordings have shown promise in identifying disease, although previous work has been limited by development in narrow populations or targeting only select heart conditions. Here we introduce a deep learning model, EchoNext, trained on more than 1 million heart rhythm and imaging records across a large and diverse health system to detect many forms of structural heart disease.

Common-variant and rare-variant genetic architecture of heart failure across the allele-frequency spectrum.

Heart failure is a complex trait, influenced by environmental and genetic factors, affecting over 30 million individuals worldwide. Here we report common-variant and rare-variant association studies of all-cause heart failure and examine how different classes of genetic variation impact its heritability. We identify 176 common-variant risk loci at genome-wide significance in 2,358,556 individuals and cluster these signals into five broad modules based on pleiotropic associations with anthropomorphic traits/obesity, blood pressure/renal function, atherosclerosis/lipids, immune activity and arrhythmias.

Deep learning for echocardiographic assessment and risk stratification of aortic, mitral, and tricuspid regurgitation: the DELINEATE-regurgitation study.

Background And Aims: Classification and risk stratification in aortic (AR), mitral (MR), and tricuspid regurgitation (TR) remains a significant clinical challenge. This study aimed to develop an artificial intelligence (AI) system to assess valvular regurgitation and stratify MR-progression risk.

Methods: Using transthoracic echocardiograms (TTEs) at two sites (internal development/test, external test), the DELINEATE-Regurgitation system was developed to classify AR, MR, and TR severity using colour Doppler videos.

Sequencing in over 50,000 cases identifies coding and structural variation underlying atrial fibrillation risk.

Atrial fibrillation (AF) is a prevalent and morbid abnormality of the heart rhythm with a strong genetic component. Here, we meta-analyzed genome and exome sequencing data from 36 studies that included 52,416 AF cases and 277,762 controls. In burden tests of rare coding variation, we identified novel associations between AF and the genes MYBPC3, LMNA, PKP2, FAM189A2 and KDM5B.

Genome-wide association study meta-analysis provides insights into the etiology of heart failure and its subtypes.

Heart failure (HF) is a major contributor to global morbidity and mortality. While distinct clinical subtypes, defined by etiology and left ventricular ejection fraction, are well recognized, their genetic determinants remain inadequately understood. In this study, we report a genome-wide association study of HF and its subtypes in a sample of 1.

A Large-scale Multimodal Study for Predicting Mortality Risk Using Minimal and Low Parameter Models and Separable Risk Assessment.

The majority of biomedical studies use limited datasets that may not generalize over large heterogeneous datasets that have been collected over several decades. The current paper develops and validates several multimodal models that can predict 1-year mortality based on a massive clinical dataset. Our focus on predicting 1-year mortality can provide a sense of urgency to the patients.

Predicted Risk of Ventricular Arrhythmias in a Genome-First Population With Genetic Risk for Arrhythmogenic Right Ventricular Cardiomyopathy.

Background: Population genomic screening for desmosome variants associated with arrhythmogenic right ventricular cardiomyopathy (ARVC) may facilitate early disease detection and protective intervention. The validated ARVC risk calculator offers a novel means to risk stratify individuals with diagnosed ARVC, but predicted risk in the context of genomic screening identification has not been explored.

Methods: Individuals harboring a pathogenic/likely pathogenic variant in a desmosome gene (, , , or ) were identified through the Geisinger MyCode Genomic Screening and Counseling program.

The impact of common and rare genetic variants on bradyarrhythmia development.

To broaden our understanding of bradyarrhythmias and conduction disease, we performed common variant genome-wide association analyses in up to 1.3 million individuals and rare variant burden testing in 460,000 individuals for sinus node dysfunction (SND), distal conduction disease (DCD) and pacemaker (PM) implantation. We identified 13, 31 and 21 common variant loci for SND, DCD and PM, respectively.

Promise and Peril of a Genotype-First Approach to Mendelian Cardiovascular Disease.

Precision medicine, which among other aspects includes an individual's genomic data in diagnosis and management, has become the standard-of-care for Mendelian cardiovascular disease (CVD). However, early identification and management of asymptomatic patients with potentially lethal and manageable Mendelian CVD through screening, which is the promise of precision health, remains an unsolved challenge. The reduced costs of genomic sequencing have enabled the creation of biobanks containing in-depth genetic and health information, which have facilitated the understanding of genetic variation, penetrance, and expressivity, moving us closer to the genotype-first screening of asymptomatic individuals for Mendelian CVD.

Predicted Deleterious Variants in Cardiomyopathy Genes Prognosticate Mortality and Composite Outcomes in the UK Biobank.

Background: Inherited cardiomyopathies present with broad variation of phenotype. Data are limited regarding genetic screening strategies and outcomes associated with predicted deleterious variants in cardiomyopathy-associated genes in the general population.

Objectives: The authors aimed to determine the risk of mortality and composite cardiomyopathy-related outcomes associated with predicted deleterious variants in cardiomyopathy-associated genes in the UK Biobank.

An ECG-based machine learning model for predicting new-onset atrial fibrillation is superior to age and clinical features in identifying patients at high stroke risk.

Background: Several large trials have employed age or clinical features to select patients for atrial fibrillation (AF) screening to reduce strokes. We hypothesized that a machine learning (ML) model trained to predict AF risk from 12‑lead electrocardiogram (ECG) would be more efficient than criteria based on clinical variables in indicating a population for AF screening to potentially prevent AF-related stroke.

Methods: We retrospectively included all patients with clinical encounters in Geisinger without a prior history of AF.

Loss-of-Function Variants Are Associated With Arrhythmogenic Cardiomyopathy Phenotypes When Identified Through Exome Sequencing of a General Clinical Population.

Background: The gene has recently garnered attention as a likely cause of arrhythmogenic cardiomyopathy, which is considered an actionable genetic condition. However, the association with disease in an unselected clinical population is unknown. We hypothesized that individuals with loss-of-function variants in () would have increased odds for arrhythmogenic cardiomyopathy-associated phenotypes versus variant-negative controls in the Geisinger MyCode cohort.

Monogenic and Polygenic Contributions to QTc Prolongation in the Population.

Background: Rare sequence variation in genes underlying cardiac repolarization and common polygenic variation influence QT interval duration. However, current clinical genetic testing of individuals with unexplained QT prolongation is restricted to examination of monogenic rare variants. The recent emergence of large-scale biorepositories with sequence data enables examination of the joint contribution of rare and common variations to the QT interval in the population.

Generalizability and quality control of deep learning-based 2D echocardiography segmentation models in a large clinical dataset.

Use of machine learning (ML) for automated annotation of heart structures from echocardiographic videos is an active research area, but understanding of comparative, generalizable performance among models is lacking. This study aimed to (1) assess the generalizability of five state-of-the-art ML-based echocardiography segmentation models within a large Geisinger clinical dataset, and (2) test the hypothesis that a quality control (QC) method based on segmentation uncertainty can further improve segmentation results. Five models were applied to 47,431 echocardiography studies that were independent from any training samples.