Prediction of long-term mortality in ICU patients: model validation and assessing the effect of using in-hospital versus long-term mortality on benchmarking

PURPOSE\nTo analyze the influence of using mortality 1, 3, and 6 months after intensive care unit (ICU) admission instead of in-hospital mortality on the quality indicator standardized mortality ratio (SMR).\nMETHODS\nA cohort study of 77,616 patients admitted to 44 Dutch mixed ICUs between 1 January 2008 and 1 July 2011. Four Acute Physiology and Chronic Health Evaluation (APACHE) IV models were customized to predict in-hospital mortality and mortality 1, 3, and 6 months after ICU admission. Models' performance, the SMR and associated SMR rank position of the ICUs were assessed by bootstrapping.\nRESULTS\nThe customized APACHE IV models can be used for prediction of in-hospital mortality as well as for mortality 1, 3, and 6 months after ICU admission. When SMR based on mortality 1, 3 or 6 months after ICU admission was used instead of in-hospital SMR, 23, 36, and 30% of the ICUs, respectively, received a significantly different SMR. The percentages of patients discharged from ICU to another medical facility outside the hospital or to home had a significant influence on the difference in SMR rank position if mortality 1 month after ICU admission was used instead of in-hospital mortality.\nCONCLUSIONS\nThe SMR and SMR rank position of ICUs were significantly influenced by the chosen endpoint of follow-up. Case-mix-adjusted in-hospital mortality is still influenced by discharge policies, therefore SMR based on mortality at a fixed time point after ICU admission should preferably be used as a quality indicator for benchmarking purposes