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Users' Guides to the Medical Literature
October 10, 2017

Discrimination and Calibration of Clinical Prediction Models: Users’ Guides to the Medical Literature

Author Affiliations
  • 1Heart Failure and Transplant Program, Toronto General Hospital, University Health Network, Toronto, Ontario, Canada
  • 2Department of Health Research Methods, Evidence and Impact, McMaster University, Hamilton, Ontario, Canada
  • 3Divisions of Clinical Epidemiology and General Internal Medicine, University Hospitals of Geneva, Geneva, Switzerland
  • 4Feinstein Institute for Medical Research, Northwell School of Medicine, Hofstra University, Hempstead, New York
JAMA. 2017;318(14):1377-1384. doi:10.1001/jama.2017.12126

Accurate information regarding prognosis is fundamental to optimal clinical care. The best approach to assess patient prognosis relies on prediction models that simultaneously consider a number of prognostic factors and provide an estimate of patients’ absolute risk of an event. Such prediction models should be characterized by adequately discriminating between patients who will have an event and those who will not and by adequate calibration ensuring accurate prediction of absolute risk. This Users’ Guide will help clinicians understand the available metrics for assessing discrimination, calibration, and the relative performance of different prediction models. This article complements existing Users’ Guides that address the development and validation of prediction models. Together, these guides will help clinicians to make optimal use of existing prediction models.