| Summary: | Introduction: Aging affects the electrocardiogram (ECG) with a higher incidence of abnormalities in older patients. ECG-age can be predicted by artificial intelligence (AI) and can be used as a measure of cardiovascular health. Hypothesis: ECG-age predicted by AI is a risk factor for overall mortality. Methods: The Clinical Outcomes in Digital Electrocardiography (CODE) study is a retrospective cohort with a mean follow-up of 3.67 years.The dataset consists of Brazilian patients, mainly from primary care centers. Two established cohorts, ELSA-Brasil, of Brazilian public servants, and SaMi-Trop, of Chagas disease patients, were used for external validation. 2,322,513 ECGs from 1,558,421 patients over 16 years old that underwent an ECG from 2010 to 2017 were included. A deep convolutional neural network was trained in order to predict the age of the patient based solely on ECG 12-lead tracings. The ECG database was split into 85-15% training and test datasets, respectively. Death was ascertained using probabilistic linkage with Brazil′s mortality information data. The Cox regression model, adjusted by age and sex, was used for statistical analysis. The model was validated in two cohorts: ELSA-Brasil (n=14,263) and SaMi-Trop (n=1,631). Results: he mean predicted ECG-age was 52.0 years (±18.7) with a mean absolute error of 8.38 (±7.0) years. Patients with ECG-age >8y older than chronological age had higher mortality rate (HR 1.79, 95%CI 1.69-1.90; p<0.001), whereas those ECG-age >8y younger than chronological age were associated with a lower mortality rate (HR 0.78, 95%CI 0.74-0.83; p<0.001). These results were similar in ELSA-Brasil and SaMi-Trop external validation cohorts (HR 1.75, 95%CI 1.35-2.27; p<0.001;HR 2.42, 95%CI 1.53-3.83; p<0.001 for >8y difference, retrospectively; HR 0.74, 95%CI 0.63-0.88; p<0.001;HR 0.89, 95%CI 0.52-1.54; p=0.68 for <8y difference, respectively). Conclusions: ECG-age, predicted by AI, can be useful as a tool for risk ...
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