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Deep neural network-estimated electrocardiographic age as a mortality predictor

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...

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Autores principales: Lima, Emilly M., Ribeiro, Antônio H., Paixão, Gabriela M. M., Ribeiro, Manoel Horta, Pinto-Filho, Marcelo M., Gomes, Paulo R., Oliveira, Derick M., Sabino, Ester C., Duncan, Bruce B., Giatti, Luana, Barreto, Sandhi M., Meira Jr, Wagner, Schön, Thomas B., Ribeiro, Antonio Luiz P.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8387361/
https://www.ncbi.nlm.nih.gov/pubmed/34433816
http://dx.doi.org/10.1038/s41467-021-25351-7
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author Lima, Emilly M.
Ribeiro, Antônio H.
Paixão, Gabriela M. M.
Ribeiro, Manoel Horta
Pinto-Filho, Marcelo M.
Gomes, Paulo R.
Oliveira, Derick M.
Sabino, Ester C.
Duncan, Bruce B.
Giatti, Luana
Barreto, Sandhi M.
Meira Jr, Wagner
Schön, Thomas B.
Ribeiro, Antonio Luiz P.
author_facet Lima, Emilly M.
Ribeiro, Antônio H.
Paixão, Gabriela M. M.
Ribeiro, Manoel Horta
Pinto-Filho, Marcelo M.
Gomes, Paulo R.
Oliveira, Derick M.
Sabino, Ester C.
Duncan, Bruce B.
Giatti, Luana
Barreto, Sandhi M.
Meira Jr, Wagner
Schön, Thomas B.
Ribeiro, Antonio Luiz P.
author_sort Lima, Emilly M.
collection PubMed
description 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.
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spelling pubmed-83873612021-09-22 Deep neural network-estimated electrocardiographic age as a mortality predictor Lima, Emilly M. Ribeiro, Antônio H. Paixão, Gabriela M. M. Ribeiro, Manoel Horta Pinto-Filho, Marcelo M. Gomes, Paulo R. Oliveira, Derick M. Sabino, Ester C. Duncan, Bruce B. Giatti, Luana Barreto, Sandhi M. Meira Jr, Wagner Schön, Thomas B. Ribeiro, Antonio Luiz P. Nat Commun Article 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. Nature Publishing Group UK 2021-08-25 /pmc/articles/PMC8387361/ /pubmed/34433816 http://dx.doi.org/10.1038/s41467-021-25351-7 Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Article
Lima, Emilly M.
Ribeiro, Antônio H.
Paixão, Gabriela M. M.
Ribeiro, Manoel Horta
Pinto-Filho, Marcelo M.
Gomes, Paulo R.
Oliveira, Derick M.
Sabino, Ester C.
Duncan, Bruce B.
Giatti, Luana
Barreto, Sandhi M.
Meira Jr, Wagner
Schön, Thomas B.
Ribeiro, Antonio Luiz P.
Deep neural network-estimated electrocardiographic age as a mortality predictor
title Deep neural network-estimated electrocardiographic age as a mortality predictor
title_full Deep neural network-estimated electrocardiographic age as a mortality predictor
title_fullStr Deep neural network-estimated electrocardiographic age as a mortality predictor
title_full_unstemmed Deep neural network-estimated electrocardiographic age as a mortality predictor
title_short Deep neural network-estimated electrocardiographic age as a mortality predictor
title_sort deep neural network-estimated electrocardiographic age as a mortality predictor
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8387361/
https://www.ncbi.nlm.nih.gov/pubmed/34433816
http://dx.doi.org/10.1038/s41467-021-25351-7
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