The effects of misclassification in routine healthcare databases on the accuracy of prognostic prediction models: a case study of the CHA2DS2-VASc score in atrial fibrillation.
Sander van DoornT B BrakenhoffK G M MoonsF H RuttenA W HoesR H H GroenwoldG J GeersingPublished in: Diagnostic and prognostic research (2017)
In a case study validating the CHA2DS2-VASc prediction model, we found substantial predictor misclassification in routine healthcare data with only limited effect on overall model performance. Our study should be repeated for other often applied prediction models to further evaluate the usefulness of routinely available healthcare data for validating prognostic models in the presence of predictor misclassification.