Methods to assess performance of models estimating risk of death in intensive care patients: a review

Anaesth Intensive Care. 2006 Apr;34(2):164-75. doi: 10.1177/0310057X0603400205.


Models that estimate the probability of death of intensive care unit patients can be used to stratify patients according to the severity of their condition and to control for casemix and severity of illness. These models have been used for risk adjustment in quality monitoring, administration, management and research and as an aid to clinical decision making. Models such as the Mortality Prediction Model family, SAPS II, APACHE II, APACHE III and the organ system failure models provide estimates of the probability of in-hospital death of ICU patients. This review examines methods to assess the performance of these models. The key attributes of a model are discrimination (the accuracy of the ranking in order of probability of death) and calibration (the extent to which the model's prediction of probability of death reflects the true risk of death). These attributes should be assessed in existing models that predict the probability of patient mortality, and in any subsequent model that is developed for the purposes of estimating these probabilities. The literature contains a range of approaches for assessment which are reviewed and a survey of the methodologies used in studies of intensive care mortality models is presented. The systematic approach used by Standards for Reporting Diagnostic Accuracy provides a framework to incorporate these theoretical considerations of model assessment and recommendations are made for evaluation and presentation of the performance of models that estimate the probability of death of intensive care patients.

Publication types

  • Review

MeSH terms

  • Critical Illness / classification
  • Hospital Mortality*
  • Humans
  • Intensive Care Units / statistics & numerical data*
  • Models, Statistical*
  • Outcome Assessment, Health Care / methods*
  • Outcome Assessment, Health Care / statistics & numerical data*
  • Proportional Hazards Models
  • Risk Assessment / methods
  • Risk Assessment / statistics & numerical data
  • Severity of Illness Index