Deep neural network-estimated electrocardiographic age as a mortality predictor

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dc.creatorEmilly M.lima
dc.creatorLuana Giatti
dc.creatorSandhi m. Barreto
dc.creatorWagner Meira jr
dc.creatorThomas b. Schön
dc.creatorAntonio Luiz Pinho Ribeiro
dc.creatorAntônio h. Ribeiro
dc.creatorGabriela m. m. Paixão
dc.creatorManoel Horta Ribeiro
dc.creatorMarcelo m. Pinto-filho
dc.creatorPaulo r. Gomes
dc.creatorDerick m. Oliveira
dc.creatorEster c. Sabino
dc.creatorBruce b. Duncan
dc.date.accessioned2023-10-02T23:03:18Z
dc.date.accessioned2025-09-08T23:26:36Z
dc.date.available2023-10-02T23:03:18Z
dc.date.issued2021
dc.format.mimetypepdf
dc.identifier.doi10.1038/s41467-021-25351-7
dc.identifier.issn20411723
dc.identifier.urihttps://hdl.handle.net/1843/59078
dc.languageeng
dc.publisherUniversidade Federal de Minas Gerais
dc.relation.ispartofnature communications
dc.rightsAcesso Aberto
dc.subjectElectrocardiography
dc.subjectCardiovascular Diseases
dc.subjectArtificial Intelligence
dc.subject.otherElectrocardiography
dc.subject.otherArtificial Intelligence
dc.subject.otherCardiovascular Diseases
dc.titleDeep neural network-estimated electrocardiographic age as a mortality predictor
dc.typeArtigo de periódico
local.citation.epage11
local.citation.issue1
local.citation.spage1
local.citation.volume12
local.description.resumoThe 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.
local.publisher.countryBrasil
local.publisher.departmentICX - DEPARTAMENTO DE CIÊNCIA DA COMPUTAÇÃO
local.publisher.departmentMED - DEPARTAMENTO DE CLÍNICA MÉDICA
local.publisher.departmentMED - DEPARTAMENTO DE MEDICINA PREVENTIVA SOCIAL
local.publisher.initialsUFMG
local.url.externahttps://www.nature.com/articles/s41467-021-25351-7

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