Deep neural network-estimated electrocardiographic age as a mortality predictor

• Autor(a) principal: Lima, Emilly M.
• Data de Publicação: 2021
• Outros Autores: Ribeiro, Antônio H., Paixão, Gabriela Miana de Mattos, Ribeiro, Manoel Horta, Pinto Filho, Marcelo Martins, Gomes, Paulo R., Oliveira, Derick Matheus de, Sabino, Ester Cerdeira, Duncan, Bruce Bartholow, Giatti, Luana, Barreto, Sandhi Maria, Meira Junior, Wagner, Schön, Thomas B., Ribeiro, Antônio Luiz Pinho
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: Repositório Institucional da UFRGS
• Texto Completo: http://hdl.handle.net/10183/247311
• topic: Eletrocardiografia; Inteligência artificial

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.