November 14, 2024Open Access
Prognostic Significance and Associations of Neural Network–Derived Electrocardiographic Features
Key Result
Neural network-derived ECG phenogroup B was associated with a 20% increase in long-term mortality compared with phenogroup A (HR 1.20; 95% CI 1.17-1.23; P<0.0001).
Study Design
Type
Cohort (n=1,808,584)
Multicenter
Yes
Structured PICO
Do neural network-derived ECG features predict future cardiovascular disease and mortality in a diverse patient population?
P
Population
1,808,584 patients from 5 diverse cohorts from the United States, Brazil, and the United Kingdom (data collected between 2000 and 2023)
I
Intervention
Unsupervised machine learning clustering of 5120 neural network-derived ECG features into 3 phenogroups
C
Comparator
Comparison between identified phenogroups (e.g., phenogroup A vs phenogroup B)
O
Outcome
All-cause mortality and future cardiovascular diseases (atrial fibrillation, ventricular tachycardia, ischemic heart disease, and cardiomyopathy)hard clinical
Neural network-derived ECG features can identify patient phenogroups with significantly increased risk of all-cause mortality and future cardiovascular diseases, supported by biologically plausible genotypic associations.
Main Result
Effect estimate: HR 1.20 (95% CI 1.17-1.23)
Absolute Event Rate: 6.1% vs 2.2%
p-value: p=<0.0001
Abstract
BACKGROUND: Subtle, prognostically important ECG features may not be apparent to physicians. In the course of supervised machine learning, thousands of ECG features are identified. These are not limited to conventional ECG parameters and morphology. We aimed to investigate whether neural network–derived ECG features could be used to predict future cardiovascular disease and mortality and have phenotypic and genotypic associations. METHODS: We extracted 5120 neural network–derived ECG features from an artificial intelligence–enabled ECG model trained for 6 simple diagnoses and applied unsupervised machine learning to identify 3 phenogroups. Using the identified phenogroups, we externally validated our findings in 5 diverse cohorts from the United States, Brazil, and the United Kingdom. Data were collected between 2000 and 2023. RESULTS: In total, 1 808 584 patients were included in this study. In the derivation cohort, the 3 phenogroups had significantly different mortality profiles. After adjusting for known covariates, phenogroup B had a 20% increase in long-term mortality compared with phenogroup A (hazard ratio, 1.20 95% CI, 1.17–1.23; P <0.0001; phenogroup A mortality, 2.2%; phenogroup B mortality, 6.1%). In univariate analyses, we found phenogroup B had a significantly greater risk of mortality in all cohorts (log-rank P <0.01 in all 5 cohorts). Phenome-wide association study showed phenogroup B had a higher rate of future atrial fibrillation (odds ratio, 2.89; P <0.00001), ventricular tachycardia (odds ratio, 2.00; P <0.00001), ischemic heart disease (odds ratio, 1.44; P <0.00001), and cardiomyopathy (odds ratio, 2.04; P <0.00001). A single-trait genome-wide association study yielded 4 loci. SCN10A , SCN5A , and CAV1 have roles in cardiac conduction and arrhythmia. ARHGAP24 does not have a clear cardiac role and may be a novel target. CONCLUSIONS: Neural network–derived ECG features can be used to predict all-cause mortality and future cardiovascular diseases. We have identified biologically plausible and novel phenotypic and genotypic associations that describe mechanisms for the increased risk identified.