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Please use this identifier to cite or link to this item: http://hdl.handle.net/1843/59078
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dc.creatorEmilly M.limapt_BR
dc.creatorLuana Giattipt_BR
dc.creatorSandhi m. Barretopt_BR
dc.creatorWagner Meira jrpt_BR
dc.creatorThomas b. Schönpt_BR
dc.creatorAntonio Luiz Pinho Ribeiropt_BR
dc.creatorAntônio h. Ribeiropt_BR
dc.creatorGabriela m. m. Paixãopt_BR
dc.creatorManoel Horta Ribeiropt_BR
dc.creatorMarcelo m. Pinto-filhopt_BR
dc.creatorPaulo r. Gomespt_BR
dc.creatorDerick m. Oliveirapt_BR
dc.creatorEster c. Sabinopt_BR
dc.creatorBruce b. Duncanpt_BR
dc.date.accessioned2023-10-02T23:03:18Z-
dc.date.available2023-10-02T23:03:18Z-
dc.date.issued2021-
dc.citation.volume12pt_BR
dc.citation.issue1pt_BR
dc.citation.spage1pt_BR
dc.citation.epage11pt_BR
dc.identifier.doi10.1038/s41467-021-25351-7pt_BR
dc.identifier.issn20411723pt_BR
dc.identifier.urihttp://hdl.handle.net/1843/59078-
dc.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.pt_BR
dc.format.mimetypepdfpt_BR
dc.languageengpt_BR
dc.publisherUniversidade Federal de Minas Geraispt_BR
dc.publisher.countryBrasilpt_BR
dc.publisher.departmentICX - DEPARTAMENTO DE CIÊNCIA DA COMPUTAÇÃOpt_BR
dc.publisher.departmentMED - DEPARTAMENTO DE CLÍNICA MÉDICApt_BR
dc.publisher.departmentMED - DEPARTAMENTO DE MEDICINA PREVENTIVA SOCIALpt_BR
dc.publisher.initialsUFMGpt_BR
dc.relation.ispartofnature communications-
dc.rightsAcesso Abertopt_BR
dc.subjectElectrocardiographypt_BR
dc.subjectArtificial Intelligencept_BR
dc.subjectCardiovascular Diseasespt_BR
dc.subject.otherElectrocardiographypt_BR
dc.subject.otherCardiovascular Diseasespt_BR
dc.subject.otherArtificial Intelligencept_BR
dc.titleDeep neural network-estimated electrocardiographic age as a mortality predictorpt_BR
dc.typeArtigo de Periódicopt_BR
dc.url.externahttps://www.nature.com/articles/s41467-021-25351-7pt_BR
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