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http://hdl.handle.net/1843/59078| Type: | Artigo de Periódico |
| Title: | Deep neural network-estimated electrocardiographic age as a mortality predictor |
| Authors: | Emilly M.lima Luana Giatti Sandhi m. Barreto Wagner Meira jr Thomas b. Schön Antonio Luiz Pinho Ribeiro 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 |
| 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 < 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. |
| Subject: | Electrocardiography Cardiovascular Diseases Artificial Intelligence |
| language: | eng |
| metadata.dc.publisher.country: | Brasil |
| Publisher: | Universidade Federal de Minas Gerais |
| Publisher Initials: | UFMG |
| metadata.dc.publisher.department: | ICX - DEPARTAMENTO DE CIÊNCIA DA COMPUTAÇÃO MED - DEPARTAMENTO DE CLÍNICA MÉDICA MED - DEPARTAMENTO DE MEDICINA PREVENTIVA SOCIAL |
| Rights: | Acesso Aberto |
| metadata.dc.identifier.doi: | 10.1038/s41467-021-25351-7 |
| URI: | http://hdl.handle.net/1843/59078 |
| Issue Date: | 2021 |
| metadata.dc.url.externa: | https://www.nature.com/articles/s41467-021-25351-7 |
| metadata.dc.relation.ispartof: | nature communications |
| Appears in Collections: | Artigo de Periódico |
Files in This Item:
| File | Description | Size | Format | |
|---|---|---|---|---|
| Deep neural network-estimated pdfa.pdf | 416.12 kB | Adobe PDF | View/Open |
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