Deep neural network-estimated electrocardiographic age as a mortality predictor

Emilly M. Lima, Antônio H. Ribeiro, Gabriela M. M. Paixão, Manoel Horta Ribeiro, Marcelo M. Pinto-Filho, Paulo R. Gomes, Derick M. Oliveira, Ester C. Sabino, Bruce B. Duncan, Luana Giatti, Sandhi M. Barreto, Wagner Meira, Thomas B. Schön () and Antonio Luiz P. Ribeiro ()
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Emilly M. Lima: Universidade Federal de Minas Gerais
Antônio H. Ribeiro: Universidade Federal de Minas Gerais
Gabriela M. M. Paixão: Universidade Federal de Minas Gerais
Manoel Horta Ribeiro: École Polytechnique Fédérale de Lausanne
Marcelo M. Pinto-Filho: Universidade Federal de Minas Gerais
Paulo R. Gomes: Universidade Federal de Minas Gerais
Derick M. Oliveira: Universidade Federal de Minas Gerais
Ester C. Sabino: Instituto de Medicina Tropical da Faculdade de Medicina da Universidade de São Paulo
Bruce B. Duncan: Universidade Federal do Rio Grande do Sul
Luana Giatti: Universidade Federal de Minas Gerais
Sandhi M. Barreto: Universidade Federal de Minas Gerais
Wagner Meira: Universidade Federal de Minas Gerais
Thomas B. Schön: Uppsala University
Antonio Luiz P. Ribeiro: Universidade Federal de Minas Gerais

Nature Communications, 2021, vol. 12, issue 1, 1-10

Abstract: Abstract 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

Date: 2021
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DOI: 10.1038/s41467-021-25351-7

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