Artificial intelligence-enabled analysis of handheld single-lead electrocardiograms to predict incident atrial fibrillation: an analysis of the VITAL-AF randomized trial
Year: 2025
Type: article
Abstract: Whether artificial intelligence (AI) analysis of single-lead ECG (1 L ECG) can predict incident AF is unknown. In the VITAL-AF trial (ClinicalTrials.gov NCT03515057, registered 2/24/2021) of primary care patients aged ≥65 years undergoing handheld 1 L ECG screening, we tested three AI approaches to incident AF prediction, and compared the best model to the CHARGE-AF risk score. In a test set of 4,221 individuals, a published AI model trained using single standard ECG leads ("1 L ECG-AI") provided similar 2-year AF discrimination to models trained with VITAL-AF data. In the full VITAL-AF sample of 15,694 individuals without prevalent AF (2-year incident AF 3.1%), 1 L ECG-AI with age/sex (1 L ECG-AI AS) had comparable discrimination (area under the receiver operating characteristic curve [AUROC] 0.695[0.637-0.742]; average precision [AP] 0.060[0.050-0.078]) to CHARGE-AF (AUROC 0.679[0.623-0.730]; AP 0.062[0.052-0.080], AUROC p = 0.46, AP p = 0.92). Net reclassification improvement was favorable versus age ≥65 years (0.27[0.22-0.32]). 1 L ECG-AI may increase efficiency and reach of AF screening.
Source: npj Digital Medicine
Institutions: Broad Institute, Harvard University, Mass General Brigham, Massachusetts Institute of Technology, University Medical Center Hamburg-Eppendorf, German Centre for Cardiovascular Research, Massachusetts General Hospital, Brigham and Women's Hospital, Beth Israel Deaconess Medical Center, University of Massachusetts Chan Medical School
Language: English
Topic: ECG Monitoring and Analysis
Subfield: Cardiology and Cardiovascular Medicine
Field: Medicine
Domain: Health Sciences
Open Access status: gold
Estimated APC paid: $4,090
Awards: G102488861, G1189786236, G1445764998, Precision Medicine in Ischemic Stroke and Atrial Fibrillation (PreMISe-AF), G3071449852, IDENTIFICATION OF COMMON GENETIC VARIANTS FOR ATRIAL FIBRILLATION AND PR INTERVAL, G3763011920, G3897532131, G4077987447, G4405986302, G4411418652, G475755283, G5003834007, Deep learning von Echokardiographie zur Risikoprädiktion von Vorhofflimmern, Advancing Atrial Fibrillation Precision Medicine, Machine Learning Artificial Intelligence Early Detection Stroke Atrial Fibrillation, Electrocardiogram-based deep learning and decision analysis to improve atrial fibrillation risk estimation, G8623768980, G8630864450, All of Us Research Program Data and Research Center, G8767518440, Deep Learning to Identify Digital Biomarkers of Cardiovascular Disease, G8993135685, G9000017272, G9000023455, G9000029256, G9000100009, G9000265122
OpenAlex ID: W4416691671