Predicting prolonged intensive care unit stays in older cardiac surgery patients: a validation study

Abstract

In cardiac surgery prediction models identifying patients at risk of prolonged stay at the Intensive Care Unit (ICU) are used to optimize treatment and use of ICU resources. A recent systematic validation study of 14 of these models identified three models with a good predictive performance across patients of all ages. It is however unclear how these models perform in older patients, who nowadays form a considerable part of this patient population. The current study specifically validates the performance of these three models in older cardiac surgery patients and quantifies how their performance changes with increasing age of patients. The Parsonnet model, the EuroSCORE, and a model by Huijskes and colleagues were validated using prospectively collected data of 11,395 cardiac surgery patients. Performance of the models was described by discrimination (area under the ROC curve, AUC) and calibration. For the Parsonnet model, the EuroSCORE and the Huijskes model discrimination clearly decreased with increasing age (AUCs of 0.76, 0.71 and 0.72 for ages 70-75 and 0.72, 0.70 and 0.72, respectively, for ages 75-80 and 0.68, 0.64 and 0.69, respectively, above 80 years). The models showed poor calibration in patients aged >70 (p values for fit of the models <0.006). To optimize treatment and ICU resources, risk prediction for prolonged ICU stay after cardiac surgery using the existing models should be done with great care for older patients.