Predicting High-Risk Patients and High-Risk Outcomes in Heart Failure.

Journal: Heart failure clinics
Published Date:

Abstract

Identifying patients with heart failure at high risk for poor outcomes is important for patient care, resource allocation, and process improvement. Although numerous risk models exist to predict mortality, hospitalization, and patient-reported health status, they are infrequently used for several reasons, including modest performance, lack of evidence to support routine clinical use, and barriers to implementation. Artificial intelligence has the potential to enhance the performance of risk prediction models, but has its own limitations and remains unproved.

Authors

  • Ramsey M Wehbe
    Division of Cardiology, Department of Medicine, Northwestern University Feinberg School of Medicine, 676 North St. Clair Street, Suite 600, Chicago, IL 60611, USA. Electronic address: https://twitter.com/ramseywehbemd.
  • Sadiya S Khan
    Division of Cardiology, Department of Medicine, Northwestern University Feinberg School of Medicine, 676 North St. Clair Street, Suite 600, Chicago, IL 60611, USA; Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, 680 N Lake Shore Drive, Suite 1400, Chicago, IL 60611, USA. Electronic address: https://twitter.com/HeartDocSadiya.
  • Sanjiv J Shah
    Division of Cardiology, Department of Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL, USA.
  • Faraz S Ahmad
    Division of Cardiology, Department of Medicine, Northwestern University Feinberg School of Medicine, Chicago, Illinois, USA.