A noise-adapted AI-enabled single-lead ECG model accurately predicted moderate left ventricular systolic dysfunction (LVEF ≤40%) in pediatric and congenital heart disease patients, maintaining an AUROC of 0.91 under simulated real-world noise.
Observational (n=113,494)
Yes
Can a noise-adapted single-lead ECG AI model accurately predict left ventricular systolic dysfunction in pediatric and congenital heart disease patients?
An AI-enabled single-lead ECG model can successfully identify left ventricular systolic dysfunction in pediatric and congenital heart disease patients, highlighting the potential for wearable-based global risk stratification.
Effect estimate: AUROC 0.91
Absolute Event Rate: 0.91% vs 0.44%
p-value: p=<0.0001
Portable, scalable, and accessible artificial intelligence (AI)-enabled smartwatch technology shows promise as a cardiovascular risk stratification strategy in the general adult population. The challenges of lifelong care in congenital heart disease (CHD)—inclusive of regional and socioeconomic disparities worldwide—underscore the need for similar solutions tailored to this population. Herein, we present the first noise-adapted single-lead ECG model for predicting left ventricular systolic dysfunction (left ventricular ejection fraction LVEF ≤ 40%) in pediatric and CHD patients. The internal cohort was comprised of 70,226 patients. External test groups included Children’s Hospital of Philadelphia (CHOP; 42,984 patients) and Toronto General Hospital (TGH; 284 repaired tetralogy of Fallot patients). Our model had strong performance across a broad range of CHD lesions, races, ages, and healthcare systems. Our findings support the potential of AI-enabled wearables to expand global access to CHD care. Prospective studies utilizing wearable ECG devices in pediatric and CHD patients are warranted.
Mayourian et al. (Sat,) conducted a observational in Left ventricular systolic dysfunction in pediatric and congenital heart disease (n=113,494). Noise-adapted AI-enabled single-lead ECG model vs. Baseline AI-ECG model (without noise adaptation) was evaluated on Prediction of left ventricular ejection fraction ≤40% under simulated real-world noise (SNR 0.5) (AUROC 0.91, p=<0.0001). A noise-adapted AI-enabled single-lead ECG model accurately predicted moderate left ventricular systolic dysfunction (LVEF ≤40%) in pediatric and congenital heart disease patients, maintaining an AUROC of 0.91 under simulated real-world noise.