Why the study?
The authors evaluated whether age predicted by artificial intelligence from raw 12-lead ECGs (ECG-age) can serve as a measure of cardiovascular health and predict mortality.
Does deep neural network-estimated electrocardiographic age predict mortality in a large patient cohort?
Does deep neural network-estimated electrocardiographic age predict mortality in a large patient cohort?
Answers are grounded in this paper and cite their sources.
AI-estimated ECG-age gap from chronological age serves as a significant independent predictor of mortality, even in patients with apparently normal ECGs.
The electrocardiogram (ECG) is the most commonly used exam for the evaluation of cardiovascular diseases. Here we propose that the age predicted by artificial intelligence (AI) from the raw ECG (ECG-age) can be a measure of cardiovascular health. A deep neural network is trained to predict a patient's age from the 12-lead ECG in the CODE study cohort (n = 1,558,415 patients). On a 15% hold-out split, patients with ECG-age more than 8 years greater than the chronological age have a higher mortality rate (hazard ratio (HR) 1.79, p < 0.001), whereas those with ECG-age more than 8 years smaller, have a lower mortality rate (HR 0.78, p < 0.001). Similar results are obtained in the external cohorts ELSA-Brasil (n = 14,236) and SaMi-Trop (n = 1,631). Moreover, even for apparent normal ECGs, the predicted ECG-age gap from the chronological age remains a statistically significant risk predictor. These results show that the AI-enabled analysis of the ECG can add prognostic information.
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Lima et al. (2021) studied this question.
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