Artificial Intelligence Electrocardiogram and Left Ventricular Systolic Dysfunction in Kenya.

Abstract

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 <10%). The study took place at 8 outpatient health care facilities across Kenya. A total of 1444 patients 18 years and older seeking routine care were enrolled and completed paired echocardiogram. Exclusion criteria included inability to provide informed consent.

Exposure: Risk of LVSD was identified using a validated convolutional neural network AI-ECG algorithm (AiTiALVSD).

Main outcomes and measures: Key outcomes were the diagnostic performance (sensitivity, specificity, and positive and negative predictive values) of the AI-ECG algorithm for detecting LVSD (LVEF <40%) when confirmed on echocardiography.

Results: Among 1444 participants (mean [SD] age, 59.0 [16.7] years; 907 [62.8%] female; 1118 [77.4%] at high risk), LVSD was identified in 204 (14.1%). The AI-ECG algorithm had a sensitivity of 95.6% (95% CI, 91.8-97.7), specificity of 79.4% (95% CI, 77.0-81.5), positive predictive value of 43.2% (95% CI, 38.7-47.9), negative predictive value of 99.1% (95% CI, 98.3-99.5), and area under the receiver operating characteristic curve (AUC) of 0.96 (95% CI, 0.95-0.97). Performance remained consistent across cardiovascular risk strata (AUC, 0.96-0.98).

Conclusions and relevance: In this study, the AI-ECG algorithm demonstrated the potential clinical utility for screening of LVSD risk with high sensitivity and negative predictive value and may be particularly scalable in a resource-limited setting.