Prognostic models based on administrative data alone inadequately predict the survival outcomes for critically ill patients at 180 days post-hospital discharge

There is interest in evaluating the quality of critical care by auditing patient outcomes after hospital discharge. Risk adjustment using acuity of illness scores, such as Acute Physiology and Chronic Health Evaluation (APACHE III) scores, derived from clinical databases is commonly performed for in-hospital mortality outcome measures. However, these clinical databases do not routinely track patient outcomes after hospital discharge. Linkage of clinical databases to administrative data sets that maintain records on patient survival after discharge can allow for the measurement of survival outcomes of critical care patients after hospital discharge while using validated risk adjustment methods. Objective: The aim of this study was to compare the ability of 4 methods of risk adjustment to predict survival of critically ill patients at 180 days after hospital discharge: one using only variables from an administrative data set, one using only variables from a clinical database, a model using a full range of administrative and clinical variables, and a model using administrative variables plus APACHE III scores. Design: This was a population-based cohort study. Patients: The study sample consisted of adult (> 15 years of age) residents of Victoria, Australia, admitted to a public hospital intensive care unit between 1 January 2001 and 31 December 2006 (n = 47,312 linked cases). Logistic regression analyses were used to develop the models. Results: The administrative-only model was the poorest predictor of mortality at 180 days after hospital discharge (C = 0.73). The clinical model had substantially better predictive capabilities (C = 0.82), whereas the full-linked model achieved similar performance (C = 0.83). Adding APACHE III scores to the administrative model also had reasonable predictive capabilities (C = 0.83). Conclusions: The addition of APACHE III scores to administrative data substantially improved model performance to the level of the clinical model. Although linking data systems requires some investment, having the ability to evaluate case ascertainment and accurately risk adjust outcomes of intensive care patients after discharge will add valuable insights into clinical audit and decision-making processes. (C) 2012 Elsevier Inc. All rights reserved.