Each 1-point increase in AI-ECG score decrease predicted a 3.9% higher chance of EF recovery in HFrEF, with baseline AI-ECG also predicting outcomes (aHR 0.991).
Do serial changes in AI-ECG LVSD probability scores predict recovery to heart failure with improved ejection fraction in patients with HFrEF?
Serial decreases in AI-ECG LVSD probability scores strongly predict ejection fraction recovery in patients with HFrEF, offering a non-invasive tool to track LV function improvement.
Absolute Event Rate: 0% vs 0%
Abstract Background Artificial intelligence–enabled electrocardiography (AI-ECG) accurately detects left ventricular systolic dysfunction (LVSD). Prior studies suggest higher AI-ECG LVSD scores predict adverse outcomes in heart failure with reduced ejection fraction (HFrEF). Whether serial changes in these scores predict recovery to heart failure with improved ejection fraction (HFiEF) is unclear. If validated, AI-ECG could offer a non-invasive alternative to frequent transthoracic echocardiography (ECHO) for monitoring ejection fraction (EF). Purpose We investigated whether sequential 12-lead AI-ECG LVSD scores are associated with EF improvement in HFrEF, potentially enabling clinicians to detect HFiEF without relying solely on serial ECHO. Methods This single-center, retrospective cohort study included all adults (≥19 years) with at least one ECHO-confirmed LVEF ≤40% (2017–2025). Exclusion criteria included left ventricular assist device, heart transplantation, ECMO, or IABP. Each ECG was analyzed by an AI-ECG model, yielding an LVSD probability (0.0–100.0). ECHOs within 14 days of an ECG formed ECG-ECHO pairs. Baseline HFrEF was defined by the first LVEF ≤40%. HFiEF required a ≥10% absolute increase in LVEF from baseline after ≥90 days. We defined the "Delta score" as (baseline AI-ECG probability − follow-up AI-ECG probability). Associations were assessed using Cox proportional hazards regression, adjusted for age, sex, obesity, hypertension, diabetes, and ischemic heart disease (IHD). Kaplan-Meier analysis was conducted to compare the probability of achieving HFiEF among the three distinct Delta score groups (1st, 2nd, and 3rd tertile), with statistical significance assessed using the log-rank test. Results Among 832 patients (mean age 64.0±14.0 years; 66.6% male), 426 (51.2%) achieved HFiEF. They were younger (62.1±13.7 vs. 66.1±14.0 years, p0.001) and had lower IHD prevalence (38.3% vs. 58.1%, p0.001). Baseline LVEF was lower in the HFiEF group (29.15% vs. 32.17%, p0.001), with a significant rise at follow-up (49.89% vs. 33.26%, p0.001). Baseline AI-ECG scores were similar (57.56 vs. 53.63, p=0.069) but dropped substantially in the HFiEF group at follow-up (19.52 vs. 48.45, p0.001). Each 1-point higher baseline AI-ECG score predicted a 0.9% lower chance of HFiEF (aHR 0.991, p0.001), while each 1-point increase in Delta score predicted a 3.9% higher HFiEF likelihood (aHR 1.039, p0.001), with a significant interaction (p=0.004). Kaplan-Meier analysis demonstrated significant differences among the three distinct Delta score groups in predicting recovery to HFiEF (p 0.0001). Conclusion Baseline AI-ECG LVSD scores and their serial decreases both predict EF recovery in HFrEF. Incorporating AI-ECG into routine care could offer a simple, non-invasive strategy to track LV function improvement—complementing or reducing the need for repeated ECHO.
Lee et al. (Sat,) reported a other. Each 1-point increase in AI-ECG score decrease predicted a 3.9% higher chance of EF recovery in HFrEF, with baseline AI-ECG also predicting outcomes (aHR 0.991).