首页|Using Artificial Intelligence to Predict Heart Failure Risk from Single-lead Ele ctrocardiographic Signals: A Multinational Assessment
Using Artificial Intelligence to Predict Heart Failure Risk from Single-lead Ele ctrocardiographic Signals: A Multinational Assessment
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – According to news reporting based on a preprint abstract, our journalists obtained the following quote sourced from me drxiv.org: “Importance: Despite the availability of disease-modifying therapies, scalable s trategies for heart failure (HF) risk stratification remain elusive. Portable de vices capable of recording single-lead electrocardiograms (ECGs) can enable larg e-scale community-based risk assessment. Objective: To evaluate an artificial in telligence (AI) algorithm to predict HF risk from noisy single-lead ECGs. Design : Multicohort study.Setting: Retrospective cohort of individuals with outpatien t ECGs in the integrated Yale New Haven Health System (YNHHS) and prospective po pulation-based cohorts of UK Biobank (UKB) and Brazilian Longitudinal Study of A dult Health (ELSA-Brasil). Participants: Individuals without HF at baseline. Exp osures: AI-ECG-defined risk of left ventricular systolic dysfunction (LVSD). Mai n Outcomes and Measures: Among individuals with ECGs, we isolated lead I ECGs an d deployed a noise-adapted AI-ECG model trained to identify LVSD. We evaluated t he association of the model probability with new-onset HF, defined as the first HF hospitalization. We compared the discrimination of AI-ECG against the pooled cohort equations to prevent HF (PCP-HF) score for new-onset HF using Harrel9s C- statistic, integrated discrimination improvement (IDI), and net reclassification improvement (NRI). Results: There were 194,340 YNHHS patients (age 56 years [IQR, 41-69], 112,082 women [58% ]), 42,741 UKB participants (65 years [59- 71], 21,795 women [52%] ), and 13,454 ELSA-Brasil participants (56 years [41-69] , 7,348 women [55%]) with bas eline ECGs.
Artificial IntelligenceEmerging Techno logiesMachine LearningRisk and Prevention
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