Serial AI-Enabled Electrocardiogram Trajectories During Heart Failure Treatment: A Proof-of-Concept Case Series

Artificial intelligence-enabled electrocardiogram (AI-ECG) detects left ventricular systolic and diastolic dysfunction at single time points, but longitudinal behavior during heart failure treatment remains unexplored. We examined whether paired serial AI-ECG scores track echocardiographic and biochemical trajectories.

In this retrospective 4-patient case series, previously validated deep learning AI-ECG models generated left ventricular systolic dysfunction and left ventricular diastolic dysfunction scores from serial 12-lead ECGs, paired with ejection fraction, E/e', and N-terminal pro-B-type natriuretic peptide across treatment courses spanning revascularization, valve replacement with resynchronization therapy, percutaneous coronary intervention, and rhythm control.

Serial score changes were broadly directionally concordant with echocardiographic and biochemical changes. Case-level and within-case discordances occurred, including persistent left ventricular systolic dysfunction elevation after resynchronization and bidirectional movement coinciding with rhythm transitions-consistent with rhythm- and pacing-related effects on ECG morphology.

These hypothesis-generating observations support the feasibility of longitudinal paired AI-ECG analysis and highlight the need for rhythm- and confounder-aware design in future studies.