Deep learning on resting electrocardiogram to identify impaired heart rate recovery

Abstract

Background and objective: Postexercise heart rate recovery (HRR) is an important indicator of cardiac autonomic function and abnormal HRR is associated with adverse outcomes. We hypothesized that deep learning on resting electrocardiogram (ECG) tracings may identify individuals with impaired HRR.

Methods: We trained a deep learning model (convolutional neural network) to infer HRR based on resting ECG waveforms (HRR_pred) among UK Biobank participants who had undergone exercise testing. We examined the association of HRR_pred with incident cardiovascular disease using Cox models, and investigated the genetic architecture of HRR_pred in genome-wide association analysis.

Results: Among 56,793 individuals (mean age 57 years, 51% women), the HRR_pred model was moderately correlated with actual HRR (r = 0.48, 95% confidence interval [CI] 0.47-0.48). Over a median follow-up of 10 years, we observed 2060 incident diabetes mellitus (DM) events, 862 heart failure events, and 2065 deaths. Higher HRR_pred was associated with lower risk of DM (hazard ratio [HR] 0.79 per 1 standard deviation change, 95% CI 0.76-0.83), heart failure (HR 0.89, 95% CI 0.83-0.95), and death (HR 0.83, 95% CI 0.79-0.86). After accounting for resting heart rate, the association of HRR_pred with incident DM and all-cause mortality were similar. Genetic determinants of HRR_pred included known heart rate, cardiac conduction system, cardiomyopathy, and metabolic trait loci.

Conclusion: Deep learning-derived estimates of HRR using resting ECG independently associated with future clinical outcomes, including new-onset DM and all-cause mortality. Inferring postexercise heart rate response from a resting ECG may have potential clinical implications and impact on preventive strategies warrants future study.

Keywords: Diabetes mellitus; Electrocardiogram; Heart failure; Machine learning; Risk factor.

Figure 1

Representations of deep learning electrocardiogram (ECG) model behavior, part 1. Saliency maps of the ECG convolutional neural network model with areas on the ECG waveform of greatest influence on heart rate recovery predictions shown in darker gray and black. Saliency was averaged over 200 individuals and grouped based on resting heart rate (within 5 beats/min of the 25th, 50th, and 75th percentile of resting heart rate). bpm = beats per minute; Rest HR = resting heart rate.

Figure 2

Representations of deep learning electrocardiogram (ECG) model behavior, part 2. Median ECG waveforms for a random sample of 100 individuals, each with high (green) vs low (red) predicted heart rate recovery (HRR_pred) (90th and 10th percentile), grouped based on resting heart rate (HR). A: Within 5 beats per minute (bpm) of the 25th percentile of resting HR. B: Within 5 bpm of the 50th percentile of resting HR. C: Within 5 bpm of the 75th percentile of resting HR).

Figure 3

Overall cumulative incidence of cardiovascular events by predicted heart rate recovery (HRR_pred) tertile. Panels show plots for future risk of A: diabetes mellitus, B: heart failure, C: cardiovascular disease, and D: all-cause mortality across HRR_pred tertiles, with tertile 1 representing most impaired HRR_pred. Predicted HRR ranges in tertile 1: 8.9–26.2 beats per minute (bpm); tertile 2: 26.2–30.3 bpm; tertile 3: 30.3–44.2 bpm. Numbers of individuals at risk in each tertile are shown at the bottom of each panel.

Figure 4

Manhattan plot of genome-wide association study (GWAS) of predicted heart rate recovery. Chromosomes are represented across the x-axis, and -log₁₀(P value) on the y-axis. The dashed line indicates genome-wide significant P value threshold of 5 × 10^-8. Most significant genetic loci are annotated on the plot. Sample size for GWAS was n = 43,722.