How Well Do Clinical Prediction Models (CPMs) Validate? A Large-Scale Evaluation of Cardiovascular Clinical Prediction Models [Methods Study], United States, 2016-2021 (ICPSR 39624)
Clinical prediction models, or CPMs, are statistical models that can predict a patient's risk for a specific event, such as a health problem, adverse effect, or even death. To create a CPM, researchers use a single data set, such as data from a clinical trial. To find out whether the CPM accurately predicts risks for patients who weren't part of the original data, researchers can test the CPM with other data sets. This testing can help researchers know if the CPM is accurate for patients from different backgrounds and whether it can be used to make health decisions. But few CPMs have been tested with other data sets.
In this study, the research team used other data sets to look at how well CPMs for heart disease predict patients' risks. They also looked at how to improve CPMs.
To access the software and methods, please visit the Tufts Race CPM Registry.