Background: Research on prognostic prediction models frequently uses data from routine healthcare. However, potential misclassification of predictors when using such data may strongly affect the studied associations. There is no doubt that such misclassification could lead to the derivation of suboptimal prediction models. The extent to which misclassification affects the validation of existing prediction models is currently unclear. We aimed to quantify the amount of misclassification in routine care data and its effect on the validation of the existing risk prediction model. As an illustrative example, we validated the CHA2DS2-VASc prediction rule for predicting mortality in patients with atrial fibrillation (AF).