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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.
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Corresponding Author: Ana Carolina Alba, MD, PhD, Toronto General Hospital, 585 University Ave, 6EN-246, Toronto, ON M5G 2N2, Canada (email@example.com).
Accepted for Publication: August 15, 2017.
Author Contributions: Dr Alba had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.
Concept and design: Alba, Walsh, Hanna, Iorio, Devereaux, McGinn, Guyatt.
Acquisition, analysis, or interpretation of data: Alba, Agoritsas, Iorio.
Drafting of the manuscript: Alba, Agoritsas, McGinn.
Critical revision of the manuscript for important intellectual content: Alba, Walsh, Hanna, Iorio, Devereaux, Guyatt.
Obtained funding: Alba.
Administrative, technical, or material support: Alba, McGinn.
Conflict of Interest Disclosures: The authors have completed and submitted the ICMJE Form for Disclosure of Potential Conflicts of Interest. Dr Devereaux reported receiving grant funding from Abbott Diagnostics, Boehringer Ingelheim, Covidien, Octapharma, Roche Dignostics, and Stryker. No other disclosures were reported.
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