Applicability of clinical prediction models in acute myocardial infarction: A comparison of traditional and empirical Bayes adjustment methods

Abstract

Several clinical prediction models have been developed to predict outcome after acute myocardial infarction. Updating to local circumstances may be required to make such models better applicable. We aimed to compare traditional and empirical Bayes (EB) methods to perform such updating. We focused on 16 geographical regions within the GUSTO-I trial, which included 40,830 patients with acute myocardial infarction; of whom, 2851 (7.0%) had died by 30 days. Differences in mortality between regions were studied with traditional adjustment for case mix in logistic regression models and with EB methods. These methods updated predictions for new patients while accounting for the uncertainty in the traditionally estimated mortality differences. The case mix in the regions differed with respect to important predictive characteristics such as age, presence of shock, and anterior infarct location (all P < .001). These differences did not explain regional differences in 30-day mortality, which varied between 80% and 120% with traditional analyses (P < .01). The EB estimates for regional differences were much smaller (between 93% and 107%). Statistically significant differences in case mix and 30-day mortality were noted between geographical regions. The practical implications of this heterogeneity were, however, limited when model predictions were updated with EB methods.