Artificial Intelligence Electrocardiogram and Left Ventricular Systolic Dysfunction in Kenya

Importance: Early detection of risk of heart failure with reduced ejection fraction remains challenging in resource-limited settings due to limited access to echocardiography. Artificial intelligence electrocardiogram (AI-ECG) algorithms have demonstrated promise for identifying left ventricular systolic dysfunction (LVSD), but their feasibility in resource-constrained settings remains unknown. Objective: To determine the frequency of patients in Kenya with a high probability of LVSD by AI-ECG and assess AI-ECG algorithm performance against the gold standard of echocardiography. Design, Setting, and Participants: This was a cross-sectional study with enrollment from June to December 2024. Participants underwent baseline assessment and 12-lead ECG, and a subset completed echocardiography within 7 days. The echocardiography subset included participants from 3 prespecified risk strata: those with prior cardiovascular disease, those at high cardiovascular risk (Framingham Risk Score [FRS] ≥10%), and those at low risk (FRS