Mortality prediction in intensive care units including premorbid functional status improved performance and internal validity

Objective: Prognostic models are key for benchmarking intensive care units (ICUs). They require up-to-date predictors and should report transportability properties for reliable predictions. We developed and validated an in-hospital mortality risk prediction model to facilitate benchmarking, quality assurance, and health economics evaluation. Study Design and Setting: We retrieved data from the database of an international (Finland, Estonia, Switzerland) multicenter ICU cohort study from 2015 to 2017. We used a hierarchical logistic regression model that included age, a modified Simplified Acute Physiology Score-II, admission type, premorbid functional status, and diagnosis as grouping variable. We used pooled and meta-analytic cross-validation approaches to assess temporal and geographical transportability. Results: We included 61,224 patients treated in the ICU (hospital mortality 10.6%). The developed prediction model had an area under the receiver operating characteristic curve 0.886, 95% confidence interval (CI) 0.882-0.890; a calibration slope 1.01, 95% CI (0.99-1.03); a mean calibration -0.004, 95% CI (-0.035 to 0.027). Although the model showed very good internal validity and geographic discrimination transportability, we found substantial heterogeneity of performance measures between ICUs (I-squared: 53.4-84.7%). Conclusion: A novel framework evaluating the performance of our prediction model provided key information to judge the validity of our model and its adaptation for future use. (c) 2021 The Authors. Published by Elsevier Inc. This is an open access article under the CC BY license ( http:// creativecommons.org/ licenses/ by/ 4.0/ )