An AI-enabled ECG tool (ECG-Vision) predicted TTE-defined left ventricular systolic dysfunction with 100% sensitivity, 100% negative predictive value, and an AUC of 0.94 (95% CI 0.90-0.98).
Cohort (n=147)
No
Does an AI-enabled ECG algorithm accurately predict left ventricular systolic dysfunction in patients with obstructive hypertrophic cardiomyopathy on mavacamten?
An AI-enabled ECG tool demonstrated high sensitivity and negative predictive value for detecting left ventricular systolic dysfunction in HCM patients on mavacamten, suggesting potential utility as a triage tool to reduce the need for frequent echocardiograms.
Effect estimate: AUC 0.94 (95% CI 0.90-0.98)
BACKGROUND: Frequent transthoracic echocardiograms (TTEs) are required to monitor for left ventricular systolic dysfunction (LVSD) in patients with obstructive hypertrophic cardiomyopathy receiving myosin inhibitors. This requirement may be cumbersome and limit access in underserved and rural areas. The objective of this study was to evaluate the performance of an artificial intelligence (AI)-enabled ECG tool in predicting LVSD in hypertrophic cardiomyopathy patients on mavacamten. METHODS: At Morristown Medical Center/Atlantic Health, 147 patients initiated on mavacamten between June 2022 and June 2025 underwent ECGs and TTEs at baseline and clinically available follow-up visits. A validated AI-enabled ECG algorithm predicted the probability of LVSD. Sensitivity, specificity, negative predictive value, positive predictive value, and area under the curve were calculated for left ventricular ejection fraction <50%, with CIs accounting for repeated paired ECG-TTE observations within patients. RESULTS: In 147 patients, mean age was 65±14 years; 44% were male. Among 453 paired ECG-TTE observations, 8 LVSD event observations occurred. At an AI probability threshold of 20%, sensitivity was 100% (95% CI, 68%-100%), specificity was 75% (95% CI, 69%-82%), positive predictive value was 7% (95% CI, 3%-11%), and negative predictive value was 100% (95% CI, 99%-100%). The area under the curve was 0.94 (95% CI, 0.90-0.98). Patient-level classifications between AI-enabled ECG and echo-confirmed LVSD were concordant in 127 of 147 patients (86%), inconclusive in 17 of 147 (12%), and discordant in 3 of 147 (2%). CONCLUSIONS: In this single-center exploratory cohort of patients with obstructive hypertrophic cardiomyopathy receiving mavacamten, ECG-Vision left ventricular demonstrated high observed sensitivity and negative predictive value for TTE-defined LVSD, although estimates were imprecise because LVSD events were infrequent. These findings support prospective multicenter validation of AI-enabled ECG as a potential adjunctive triage tool.
A Mon, study conducted a cohort in Obstructive hypertrophic cardiomyopathy (n=147). AI-enabled ECG tool (ECG-Vision) vs. Transthoracic echocardiogram (TTE) was evaluated on Left ventricular systolic dysfunction (left ventricular ejection fraction <50%) (AUC 0.94, 95% CI 0.90-0.98). An AI-enabled ECG tool (ECG-Vision) predicted TTE-defined left ventricular systolic dysfunction with 100% sensitivity, 100% negative predictive value, and an AUC of 0.94 (95% CI 0.90-0.98).