Deep neural network-estimated electrocardiographic age as a mortality predictor
© 2021. The Author(s)..
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.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2021 |
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| Erschienen: |
2021 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:12 |
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| Enthalten in: |
Nature communications - 12(2021), 1 vom: 25. Aug., Seite 5117 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Lima, Emilly M [VerfasserIn] |
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| Links: |
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| Themen: |
Journal Article |
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| Anmerkungen: |
Date Completed 24.09.2021 Date Revised 24.09.2021 published: Electronic Citation Status MEDLINE |
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| doi: |
10.1038/s41467-021-25351-7 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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NLM32976974X |
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| 500 | |a Citation Status MEDLINE | ||
| 520 | |a © 2021. The Author(s). | ||
| 520 | |a 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 | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a Research Support, N.I.H., Extramural | |
| 650 | 4 | |a Research Support, Non-U.S. Gov't | |
| 700 | 1 | |a Ribeiro, Antônio H |e verfasserin |4 aut | |
| 700 | 1 | |a Paixão, Gabriela M M |e verfasserin |4 aut | |
| 700 | 1 | |a Ribeiro, Manoel Horta |e verfasserin |4 aut | |
| 700 | 1 | |a Pinto-Filho, Marcelo M |e verfasserin |4 aut | |
| 700 | 1 | |a Gomes, Paulo R |e verfasserin |4 aut | |
| 700 | 1 | |a Oliveira, Derick M |e verfasserin |4 aut | |
| 700 | 1 | |a Sabino, Ester C |e verfasserin |4 aut | |
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| 700 | 1 | |a Schön, Thomas B |e verfasserin |4 aut | |
| 700 | 1 | |a Ribeiro, Antonio Luiz P |e verfasserin |4 aut | |
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