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

Intensive Care Med. 2013 Nov;39(11):1925-31. doi: 10.1007/s00134-013-3042-5. Epub 2013 Aug 7.


Purpose: To 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).

Methods: A 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.

Results: The 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.

Conclusions: The 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.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Benchmarking
  • Coronary Artery Bypass / mortality*
  • Female
  • Glasgow Coma Scale
  • Hospital Mortality*
  • Humans
  • Intensive Care Units*
  • Length of Stay / statistics & numerical data
  • Male
  • Middle Aged
  • Netherlands / epidemiology
  • Predictive Value of Tests
  • Quality Indicators, Health Care
  • Registries