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dc.contributor.authorLima, Emilly M.pt_BR
dc.contributor.authorRibeiro, Antônio H.pt_BR
dc.contributor.authorPaixão, Gabriela Miana de Mattospt_BR
dc.contributor.authorRibeiro, Manoel Hortapt_BR
dc.contributor.authorPinto Filho, Marcelo Martinspt_BR
dc.contributor.authorGomes, Paulo R.pt_BR
dc.contributor.authorOliveira, Derick Matheus dept_BR
dc.contributor.authorSabino, Ester Cerdeirapt_BR
dc.contributor.authorDuncan, Bruce Bartholowpt_BR
dc.contributor.authorGiatti, Luanapt_BR
dc.contributor.authorBarreto, Sandhi Mariapt_BR
dc.contributor.authorMeira Junior, Wagnerpt_BR
dc.contributor.authorSchön, Thomas B.pt_BR
dc.contributor.authorRibeiro, Antônio Luiz Pinhopt_BR
dc.date.accessioned2022-08-19T04:43:01Zpt_BR
dc.date.issued2021pt_BR
dc.identifier.issn2041-1723pt_BR
dc.identifier.urihttp://hdl.handle.net/10183/247311pt_BR
dc.description.abstractThe 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.en
dc.format.mimetypeapplication/pdfpt_BR
dc.language.isoengpt_BR
dc.relation.ispartofNature communications. [London]. Vol. 12 (2021), 5117, [10 p.]pt_BR
dc.rightsOpen Accessen
dc.subjectEletrocardiografiapt_BR
dc.subjectInteligência artificialpt_BR
dc.titleDeep neural network-estimated electrocardiographic age as a mortality predictorpt_BR
dc.typeArtigo de periódicopt_BR
dc.identifier.nrb001146455pt_BR
dc.type.originEstrangeiropt_BR


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