Prediction Models for Prolonged Intensive Care Unit Stay After Cardiac Surgery

Abstract

Background— Several models have been developed to predict prolonged stay in the intensive care unit (ICU) after cardiac surgery. However, no extensive quantitative validation of these models has yet been conducted. This study sought to identify and validate existing prediction models for prolonged ICU length of stay after cardiac surgery. Methods and Results— After a systematic review of the literature, the identified models were applied on a large registry database comprising 11 395 cardiac surgical interventions. The probabilities of prolonged ICU length of stay based on the models were compared with the actual outcome to assess the discrimination and calibration performance of the models. Literature review identified 20 models, of which 14 could be included. Of the 6 models for the general cardiac surgery population, the Parsonnet model showed the best discrimination (area under the receiver operating characteristic curve=0.75 [95% confidence interval, 0.73 to 0.76]), followed by the European system for cardiac operative risk evaluation (EuroSCORE) (0.71 [0.70 to 0.72]) and a model by Huijskes and colleagues (0.71 [0.70 to 0.73]). Most of the models showed good calibration. Conclusions— In this validation of prediction models for prolonged ICU length of stay, 2 widely implemented models (Parsonnet, EuroSCORE), although originally designed for prediction of mortality, were superior in identifying patients with prolonged ICU length of stay.