Diagnostic Performance of Artificial Intelligence-Driven Electrocardiogram Algorithms in Detecting Heart Failure with Preserved Ejection Fraction: A Systematic Review and Meta-Analysis

Abstract

Background:

Heart failure with preserved ejection fraction (HFpEF) is a highly prevalent syndrome associated with substantial morbidity and mortality. Accurate diagnosis is often challenging and costly. Recently, artificial intelligence-based electrocardiogram (AI-ECG) algorithms have emerged to make the diagnosis of HFpEF less expensive and more accessible. However, there is a lack of data regarding their accuracy.

Objective:

We aimed to conduct a systematic review and meta-analysis to evaluate the diagnostic performance of AI-ECG algorithms in detecting HFpEF.

Methods:

We searched PubMed, Embase, and Cochrane databases for studies evaluating AI-ECG algorithms in diagnosing HFpEF. We extracted true-positive, true-negative, false-positive, and false-negative events to estimate pooled sensitivity, specificity with 95% confidence intervals (CI), and area under the curve. Statistical analysis was performed using R under a random-effects model. Heterogeneity was assessed with I² statistics.

Results:

We included three studies comprising 171,073 patients (66.86% with HFpEF), in which an AI-ECG was evaluated alongside echocardiography, with a left ventricular ejection fraction of ≥ 50%. The patients’ mean age was 65.86 ± 4.5 years, and 48.12% were male. AI-ECG algorithms demonstrated a sensitivity of 0.89 (95% CI: 0.64 to 0.97), specificity of 0.80 (95% CI: 0.65 to 0.89), and an area under the receiver operating characteristic curve of 0.89 (95% CI: 0.85 to 0.92).

Conclusion:

AI-ECG algorithms demonstrate moderate to high sensitivity, specificity, and accuracy in diagnosing HFpEF, supporting their potential for routine screening and early detection.