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

Abstract

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, 6212 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) 0.690.74. 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) 0.6250.749], which was similar (P=0.394) to the CNN (0.660; 95% CI 0.5970.722) and better (P<0.0001) than random forests (0.512; 95% CI 0.4930.530).

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.

Keywords: Cardiovascular disease; ECG; Machine learning; QRS-T angle.

Figure 1

Study flowchart.

Figure 2

Representative vectorcardiogram. (A) Colour-coded (from red to purple) propagation of cardiac activation (red-yellow QRS loop) and repolarization (green-blue T loop). Peak QRS (red), T (green), and spatial ventricular gradient (blue) vectors. (B) Vector magnitude signal of a normal sinus median beat. Gray area indicates QT integral (scalar spatial ventricular gradient measure). (C) Corresponding orthogonal X, Y, Z ECG signal.

Figure 3

(A) Importance scores of predictor variables in a random forests model with vectorcardiographic input. (B) Comparison of the marginal effect size in a convolutional neural network with vectorcardiographic input.

Figure 4

Calibration of vectorcardiographic models. The calibration belt with 80% and 95% confidence intervals on the external sample shows the observed and predicted cardiovascular disease proportions in (A) lasso, (B) adaptive lasso, (C) plugin lasso, (D) elastic net, (E) ridge, (F) logistic regression models with vectorcardiographic input (43 predictors).

Figure 5

Calibration of electrocardiogram and vectorcardiographic models. The calibration plot shows the observed and predicted cardiovascular disease proportions in the (A) convolutional neural network model with vectorcardiographic input (43 variables) and (D) electrocardiogram input (153 variables). The size of the circles is proportional to the amount of data. The calibration belt with 80% and 95% confidence intervals on the external sample shows the observed and predicted cardiovascular disease proportions in (B) lasso, (C) adaptive lasso, (E) plugin lasso, (F) elastic net models with electrocardiogram and vectorcardiographic input (695 predictors).

Figure 6

Importance scores of the most important predictor variables in a random forest model with both vectorcardiographic and electrocardiogram input (695 predictors).