Machine learning-based prediction of adverse events following an acute coronary syndrome (PRAISE): a modelling study of pooled datasets.

Journal: Lancet (London, England)
PMID:

Abstract

BACKGROUND: The accuracy of current prediction tools for ischaemic and bleeding events after an acute coronary syndrome (ACS) remains insufficient for individualised patient management strategies. We developed a machine learning-based risk stratification model to predict all-cause death, recurrent acute myocardial infarction, and major bleeding after ACS.

Authors

  • Fabrizio D'Ascenzo
    Division of Cardiology, Cardiovascular and Thoracic Department, Città della Salute e della Scienza, Turin, Italy; Cardiology, Department of Medical Sciences, University of Turin, Turin, Italy. Electronic address: fabrizio.dascenzo@gmail.com.
  • Ovidio De Filippo
    Division of Cardiology, Cardiovascular and Thoracic Department, Città della Salute e della Scienza, Turin, Italy; Cardiology, Department of Medical Sciences, University of Turin, Turin, Italy.
  • Guglielmo Gallone
    Division of Cardiology, Cardiovascular and Thoracic Department, Città della Salute e della Scienza, Turin, Italy; Cardiology, Department of Medical Sciences, University of Turin, Turin, Italy.
  • Gianluca Mittone
    Department of Computer Science, University of Turin, Turin, Italy.
  • Marco Agostino Deriu
    Polito BIO Med Lab, Department of Mechanical and Aerospace Engineering, Politecnico di Torino, Turin, Italy.
  • Mario Iannaccone
    Department of Cardiology, S G Bosco Hospital, Turin, Italy.
  • Albert Ariza-Solé
    Department of Cardiology, University Hospital de Bellvitge, Barcelona, Spain.
  • Christoph Liebetrau
    Kerckhoff Heart and Thorax Center, Frankfurt, Germany.
  • Sergio Manzano-Fernández
    Department of Cardiology, University Hospital Virgen Arrtixaca, Murcia, Spain.
  • Giorgio Quadri
    Interventional Cardiology Unit, Degli Infermi Hospital, Turin, Italy.
  • Tim Kinnaird
    Cardiology Department, University Hospital of Wales, Cardiff, UK.
  • Gianluca Campo
    Azienda Ospedaliera Universitaria di Ferrara, Ferrara, Italy.
  • Jose Paulo Simao Henriques
    University of Amsterdam, Academic Medical Center, Amsterdam, Netherlands.
  • James M Hughes
    Candiolo Cancer Institute, FPO - IRCCS, Turin, Italy.
  • Alberto Dominguez-Rodriguez
    Servicio de Cardiologìa, Hospital Universitario de Canarias, Santa Cruz de Tenerife, Spain.
  • Marco Aldinucci
    Department of Computer Science, University of Turin, Turin, Italy.
  • Umberto Morbiducci
    Polito BIO Med Lab, Department of Mechanical and Aerospace Engineering, Politecnico di Torino, Turin, Italy.
  • Giuseppe Patti
    Department of Thoracic, Heart and Vascular Diseases, Maggiore Della Carità Hospital, Corso Mazzini 18, 28100 Novara, Italy.
  • Sergio Raposeiras-Roubin
    Department of Cardiology, University Hospital Álvaro Cunqueiro, Vigo, Spain.
  • Emad Abu-Assi
    Department of Cardiology, University Hospital Álvaro Cunqueiro, Vigo, Spain.
  • Gaetano Maria De Ferrari
    Coronary Care Unit, Fondazione IRCCS Policlinico San Matteo, Pavia, Italy.