Machine learning-based risk prediction of intrahospital clinical outcomes in patients undergoing TAVI.

Journal: Clinical research in cardiology : official journal of the German Cardiac Society
PMID: 32583062

Abstract

BACKGROUND: Currently, patient selection in TAVI is based upon a multidisciplinary heart team assessment of patient comorbidities and surgical risk stratification. In an era of increasing need for precision medicine and quickly expanding TAVI indications, machine learning has shown promise in making accurate predictions of clinical outcomes. This study aims to predict different intrahospital clinical outcomes in patients undergoing TAVI using a machine learning-based approach. The main clinical outcomes include all-cause mortality, stroke, major vascular complications, paravalvular leakage, and new pacemaker implantations.

Authors

  • Bruna Gomes
    Department of Internal Medicine III, University of Heidelberg, Im Neuenheimer Feld 410, 69120, Heidelberg, Germany.
  • Maximilian Pilz
    Institute of Medical Biometry and Informatics, University of Heidelberg, Heidelberg, Germany.
  • Christoph Reich
    Department of Internal Medicine III, University of Heidelberg, Im Neuenheimer Feld 410, 69120, Heidelberg, Germany.
  • Florian Leuschner
    Department of Internal Medicine III, University of Heidelberg, Im Neuenheimer Feld 410, 69120, Heidelberg, Germany.
  • Mathias Konstandin
    Department of Internal Medicine III, University of Heidelberg, Im Neuenheimer Feld 410, 69120, Heidelberg, Germany.
  • Hugo A Katus
    University of Heidelberg, Department of Cardiology, Angiology and Pneumology, Im Neuenheimer Feld 410, Heidelberg, 69120, Germany.
  • Benjamin Meder
    Department of Internal Medicine III, University Hospital Heidelberg, Germany.

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