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March 5, 2020

Validation and Utility Testing of Clinical Prediction Models: Time to Change the Approach

Author Affiliations
  • 1Respiratory Evaluation Sciences Program, Collaboration for Outcomes Research and Evaluation, Faculty of Pharmaceutical Sciences, University of British Columbia, Vancouver, British Columbia, Canada
  • 2Meta-Research Innovation Center at Stanford (METRICS), Stanford Prevention Research Center, Department of Medicine, Stanford University School of Medicine, Stanford, California
JAMA. 2020;324(3):235-236. doi:10.1001/jama.2020.1230

The growth in the publication of clinical prediction models (CPMs) has been exponential, largely as a result of an ever-increasing availability of clinical data, inexpensive computational power, and an expanding tool kit for constructing predictive algorithms. Such an abundance of CPMs has led to an overcrowded, confusing landscape in which it is difficult to identify and select the best, most useful models.1 Few models are externally validated by the same researchers who developed them, and even fewer by independent investigators. Only 592 (43.3%) of 1366 cardiovascular CPMs in the Tufts PACE Clinical Predictive Model Registry reported at least 1 validation.2 The proportions of models in the Tufts registry that reported at least 2, 3, and 10 validations were 20.1%, 12.8%, and 2.9%, respectively.2 A few select CPMs, such as the Framingham Risk Score and EuroSCORE, have had numerous validations.

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