Deep neural network-estimated electrocardiographic age as a mortality predictor

• Published in: Nature Communications
• Main Authors: Lima, Emilly M., Horta Ribeiro, Antônio, Paixao, Gabriela M. M., Horta Ribeiro, Manoel, 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.
• Format: Article in Journal/Newspaper
• Language: English
• Published: Uppsala universitet, Avdelningen för systemteknik 2021
• Subjects: Cardiac and Cardiovascular Systems; Kardiologi; sami
• Online Access: http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-456162; https://doi.org/10.1038/s41467-021-25351-7

The electrocardiogram (ECG) is the most commonly used exam for the screening and evaluation of cardiovascular diseases. Here, the authors propose that the age predicted by artificial intelligence from the raw ECG tracing can be a measure of cardiovascular health and provide prognostic information. 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. These authors contributed equally: Emilly M. Lima, Antônio H. Ribeiro, Gabriela M. M. Paixão