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Deep neural network-estimated electrocardiographic age as a mortality predictor

Author

Listed:
  • Emilly M. Lima

    (Universidade Federal de Minas Gerais
    Universidade Federal de Minas Gerais)

  • Antônio H. Ribeiro

    (Universidade Federal de Minas Gerais
    Uppsala University)

  • Gabriela M. M. Paixão

    (Universidade Federal de Minas Gerais
    Universidade Federal de Minas Gerais)

  • Manoel Horta Ribeiro

    (École Polytechnique Fédérale de Lausanne)

  • Marcelo M. Pinto-Filho

    (Universidade Federal de Minas Gerais
    Universidade Federal de Minas Gerais)

  • Paulo R. Gomes

    (Universidade Federal de Minas Gerais
    Universidade Federal de Minas Gerais)

  • Derick M. Oliveira

    (Universidade Federal de Minas Gerais)

  • Ester C. Sabino

    (Instituto de Medicina Tropical da Faculdade de Medicina da Universidade de São Paulo)

  • Bruce B. Duncan

    (Universidade Federal do Rio Grande do Sul)

  • Luana Giatti

    (Universidade Federal de Minas Gerais)

  • Sandhi M. Barreto

    (Universidade Federal de Minas Gerais)

  • Wagner Meira Jr

    (Universidade Federal de Minas Gerais)

  • Thomas B. Schön

    (Uppsala University)

  • Antonio Luiz P. Ribeiro

    (Universidade Federal de Minas Gerais
    Universidade Federal de Minas Gerais)

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

Suggested Citation

  • Emilly M. Lima & 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 & Luana Giatti & S, 2021. "Deep neural network-estimated electrocardiographic age as a mortality predictor," Nature Communications, Nature, vol. 12(1), pages 1-10, December.
    • Handle: RePEc:nat:natcom:v:12:y:2021:i:1:d:10.1038_s41467-021-25351-7
    DOI: 10.1038/s41467-021-25351-7
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    Cited by:

    1. Jintai Chen & Shuai Huang & Ying Zhang & Qing Chang & Yixiao Zhang & Dantong Li & Jia Qiu & Lianting Hu & Xiaoting Peng & Yunmei Du & Yunfei Gao & Danny Z. Chen & Abdelouahab Bellou & Jian Wu & Huiyin, 2024. "Congenital heart disease detection by pediatric electrocardiogram based deep learning integrated with human concepts," Nature Communications, Nature, vol. 15(1), pages 1-13, December.
    2. Mit Shah & Marco H. A. Inácio & Chang Lu & Pierre-Raphaël Schiratti & Sean L. Zheng & Adam Clement & Antonio Marvao & Wenjia Bai & Andrew P. King & James S. Ware & Martin R. Wilkins & Johanna Mielke &, 2023. "Environmental and genetic predictors of human cardiovascular ageing," Nature Communications, Nature, vol. 14(1), pages 1-15, December.

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