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

BACKGROUND: 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. METHODS: 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. RESULTS: 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. CONCLUSIONS: 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.