Artificial intelligence-enabled electrocardiogram for mortality and cardiovascular risk estimation: a model development and validation study.

Journal: The Lancet. Digital health
Published Date:

Abstract

BACKGROUND: Artificial intelligence (AI)-enabled electrocardiography (ECG) can be used to predict risk of future disease and mortality but has not yet been adopted into clinical practice. Existing model predictions do not have actionability at an individual patient level, explainability, or biological plausibi. We sought to address these limitations of previous AI-ECG approaches by developing the AI-ECG risk estimator (AIRE) platform.

Authors

  • Arunashis Sau
    National Heart and Lung Institute, Hammersmith Campus, Imperial College London, 72 Du Cane Road, W12 0HS, London, UK.
  • Libor Pastika
    National Heart and Lung Institute, Hammersmith Campus, Imperial College London, 72 Du Cane Road, W12 0HS, London, UK.
  • Ewa Sieliwonczyk
    National Heart and Lung Institute (A.S., K.A.M., L.P., N.B., M.G., E. Sieliwonczyk, K.P., M.A., J.Y.C., H.W., X.S., K.H., S.Z., D.B.K., N.S.P., M.M., J.S.W., F.S.N.), Imperial College London, United Kingdom.
  • Konstantinos Patlatzoglou
    National Heart and Lung Institute (A.S., K.A.M., L.P., N.B., M.G., E. Sieliwonczyk, K.P., M.A., J.Y.C., H.W., X.S., K.H., S.Z., D.B.K., N.S.P., M.M., J.S.W., F.S.N.), Imperial College London, United Kingdom.
  • Antônio H Ribeiro
    Universidade Federal de Minas Gerais, Belo Horizonte, Brazil. antonio-ribeiro@ufmg.br.
  • Kathryn A McGurk
    National Heart and Lung Institute (A.S., K.A.M., L.P., N.B., M.G., E. Sieliwonczyk, K.P., M.A., J.Y.C., H.W., X.S., K.H., S.Z., D.B.K., N.S.P., M.M., J.S.W., F.S.N.), Imperial College London, United Kingdom.
  • Boroumand Zeidaabadi
    National Heart and Lung Institute, Imperial College London, London, UK.
  • Henry Zhang
    National Heart and Lung Institute, Imperial College London, London, UK.
  • Krzysztof Macierzanka
    National Heart and Lung Institute, Imperial College London, London, UK.
  • Danilo Mandic
    Department of Electrical and Electronic Engineering, Imperial College London, London, United Kingdom. Electronic address: d.mandic@imperial.ac.uk.
  • Ester Sabino
    Faculdade de Medicina da Universidade de São Paulo-FMUSP, São Paulo, Brazil.
  • Luana Giatti
    Faculdade de Medicina, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil.
  • Sandhi M Barreto
    Faculdade de Medicina, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil.
  • Lidyane do Valle Camelo
    Department of Preventive Medicine, School of Medicine, and Hospital das Clínicas/EBSERH, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil.
  • Ioanna Tzoulaki
    Systems Biology, Biomedical Research Foundation, Academy of Athens, Athens, Greece; Department of Biostatistics and Epidemiology, School of Public Health, Imperial College London, London, UK.
  • Declan P O'Regan
    MRC London Institute of Medical Sciences, Imperial College London, London, United Kingdom.
  • Nicholas S Peters
    National Heart and Lung Institute, Hammersmith Campus, Imperial College London, 72 Du Cane Road, W12 0HS, London, UK.
  • James S Ware
    National Heart & Lung Institute & MRC London Institute of Medical Sciences, Imperial College London, London W12 0HS, UK.
  • Antonio Luiz P Ribeiro
    Hospital das Clínicas and Faculdade de Medicina, Universidade Federal de Minas Gerais, Av. Prof. Alfredo Balena, 190 - sala 533/Universidade Federal de Minas Gerais (UFMG), Belo Horizonte - MG, Brazil. Electronic address: tom@hc.ufmg.br.
  • Daniel B Kramer
    Richard A. and Susan F. Smith Center for Outcomes Research, Division of Cardiology, Beth Israel Deaconess Medical Center, Boston, Massachusetts, United States of America.
  • Jonathan W Waks
    Harvard-Thorndike Electrophysiology Institute, Beth Israel Deaconess Medical Centre, Harvard Medical School, Boston, MA, USA.
  • Fu Siong Ng
    National Heart and Lung Institute, Hammersmith Campus, Imperial College London, 72 Du Cane Road, W12 0HS, London, UK.