Electrocardiogram machine learning for detection of cardiovascular disease in African Americans: The Jackson Heart Study

Aims: Almost half of African American (AA) men and women have cardiovascular disease (CVD). Detection of prevalent CVD in community settings would facilitate secondary prevention of CVD. We sought to develop a tool for automated CVD detection. Methods and results: Participants from the Jackson Heart Study (JHS) with analysable electrocardiograms (ECGs) (n = 3679; age, 62 ± 12 years; 36% men) were included. Vectorcardiographic (VCG) metrics QRS, T, and spatial ventricular gradient vectors' magnitude and direction, and traditional ECG metrics were measured on 12-lead ECG. Random forests, convolutional neural network (CNN), lasso, adaptive lasso, plugin lasso, elastic net, ridge, and logistic regression models were developed in 80% and validated in 20% samples. We compared models with demographic, clinical, and VCG input (43 predictors) and those after the addition of ECG metrics (695 predictors). Prevalent CVD was diagnosed in 411 out of 3679 participants (11.2%). Machine learning models detected CVD with the area under the receiver operator curve (ROC AUC) 0.69-0.74. There was no difference in CVD detection accuracy between models with VCG and VCG + ECG input. Models with VCG input were better calibrated than models with ECG input. Plugin-based lasso model consisting of only two predictors (age and peak QRS-T angle) detected CVD with AUC 0.687 [95% confidence interval (CI) 0.625-0.749], which was similar (P = 0.394) to the CNN (0.660; 95% CI 0.597-0.722) and better (P < 0.0001) than random forests (0.512; 95% CI 0.493-0.530). Conclusions: Simple model (age and QRS-T angle) can be used for prevalent CVD detection in limited-resources community settings, which opens an avenue for secondary prevention of CVD in underserved communities.