Key result
Lead I ECG-AI plus demographics predicts fatal coronary heart disease with ~0.87 AUC.
Why the study?
Fatal coronary heart disease affects approximately 650,000 people annually in the US, but the application of ECG-AI models to predict fatal coronary heart disease is understudied.
Does an ECG-AI model using single-lead ECGs and demographics predict fatal coronary heart disease in adult patients?
Cohort (n=52,437)
Yes
Does an ECG-AI model using single-lead ECGs and demographics predict fatal coronary heart disease in adult patients?
A single-lead ECG-AI model combined with basic demographics can accurately predict the risk of fatal coronary heart disease, potentially enabling population-level screening via smartwatches.
May support wearable ECG screening for fatal CHD; extends ECG-AI models but leaves open prospective outcome benefits.
Background: Fatal coronary heart disease (FCHD) affects ~650,000 people yearly in the US. Electrocardiographic artificial intelligence (ECG-AI) models can predict adverse coronary events, yet their application to FCHD is understudied. Objectives: The study aimed to develop ECG-AI models predicting FCHD risk from ECGs. Methods (Retrospective): Data from 10 s 12-lead ECGs and demographic/clinical data from University of Tennessee Health Science Center (UTHSC) were used for model development. Of this dataset, 80% was used for training and 20% as holdout. Data from Atrium Health Wake Forest Baptist (AHWFB) were used for external validation. We developed two separate convolutional neural network models using 12-lead and Lead I ECGs as inputs, and time-dependent Cox proportional hazard models using demographic/clinical data with ECG-AI outputs. Correlation of the predictions from the 12- and 1-lead ECG-AI models was assessed. Results: The UTHSC cohort included data from 50,132 patients with a mean age (SD) of 62.50 (14.80) years, of whom 53.4% were males and 48.5% African American. The AHWFB cohort included data from 2305 patients with a mean age (SD) of 63.04 (16.89) years, of whom 51.0% were males and 18.8% African American. The 12-lead and Lead I ECG-AI models resulted in validation AUCs of 0.84 and 0.85, respectively. The best overall model was the Cox model using simple demographics with Lead I ECG-AI output (D1-ECG-AI-Cox), with the following results: AUC = 0.87 (0.85–0.89), accuracy = 83%, sensitivity = 69%, specificity = 89%, negative predicted value (NPV) = 92% and positive predicted value (PPV) = 55% on the AHWFB validation cohort. For this, the 2-year FCHD risk prediction accuracy was AUC = 0.91 (0.90–0.92). The 12-lead versus Lead I ECG FCHD risk prediction showed strong correlation (R = 0.74). Conclusions: The 2-year FCHD risk can be predicted with high accuracy from single-lead ECGs, further improving when combined with demographic information.
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Butler et al. (2024) conducted a cohort in Fatal coronary heart disease (FCHD) (n=52,437). Lead I ECG-AI model with demographics vs. Demographic and clinical data alone was evaluated on Prediction of fatal coronary heart disease (AUC) (AUC 0.87, 95% CI 0.85-0.89). A time-dependent Cox model using simple demographics and Lead I ECG-AI output predicted fatal coronary heart disease with an AUC of 0.87 (95% CI 0.85-0.89) in the external validation cohort.
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