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Published on: December 11, 2019
Electrocardiogram machine learning for detection of cardiovascular disease in African Americans: the Jackson Heart
James D Pollard1, Kazi T Haq2, Katherine J Lutz2
1University of Mississippi Medical Center, 2500 N State St, Jackson, MS 39216, USA.
Insights
A simple model using age and QRS-T angle can detect cardiovascular disease (CVD) in community settings. This tool aids in secondary CVD prevention for underserved populations.
Area of Science:
- Cardiology
- Biomedical Engineering
- Machine Learning in Healthcare
Background:
- Cardiovascular disease (CVD) affects nearly half of African American adults.
- Early detection of prevalent CVD is crucial for effective secondary prevention strategies.
- Community-based screening tools are needed for underserved populations.
Purpose of the Study:
- To develop an automated tool for detecting prevalent cardiovascular disease (CVD).
- To evaluate machine learning models using electrocardiogram (ECG) and vectorcardiogram (VCG) data.
- To identify simple predictors for CVD detection in community settings.
Main Methods:
- Utilized data from 3679 participants in the Jackson Heart Study (JHS).
- Extracted VCG metrics (QRS, T vectors) and traditional ECG metrics.
- Developed and validated various machine learning models including CNN, lasso, and random forests.
Main Results:
- Machine learning models achieved an Area Under the Receiver Operator Curve (ROC AUC) of 0.69-0.74 for CVD detection.
- Models using VCG input showed comparable accuracy to VCG + ECG models and better calibration.
- A simple plugin-based lasso model with only age and peak QRS-T angle demonstrated strong performance (AUC 0.687).
Conclusions:
- A straightforward model incorporating age and QRS-T angle can effectively detect prevalent CVD.
- This simple model is suitable for resource-limited community settings.
- The findings offer a pathway for improving secondary CVD prevention in underserved communities.
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.
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