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This Article Full Text (PDF) All Versions of this Article: chest.07-3061v1 133/6/1319    most recent Submit a response Alert me when this article is cited Alert me when eLetters are posted Alert me if a correction is posted Services Email this article to a friend Similar articles in this journal Similar articles in PubMed Alert me to new issues of the journal Add to My Personal Article Archive Download to citation manager Google Scholar Articles by Kuzniewicz, M. W Articles by Dudley, R. A. PubMed PubMed Citation Articles by Kuzniewicz, M. W Articles by Dudley, R. A.

Variation in ICU risk-adjusted mortality: impact of methods of assessment and potential confounders

University of California, San Francisco; Institute for Health Policy Studies (MWK, EEV, RKL, MLD, NT, DJR, TC, PLK, and RAD), Division of Neonatology (MWK), Division of Pulmonary and Critical Care Medicine (RAD), Department of Anesthesia (RKL), Division of General Internal Medicine (EEV), Division of Hospital Medicine (EEV)

kuzniewiczm{at}peds.ucsf.edu

Abstract

Background: Federal and state agencies are considering ICU performance assessment and public reporting; however, an accurate method for measuring performance must be selected. In this study, we determine whether substantial variation in ICU mortality performance still exists in modern ICUs and compare the predictive accuracy, reliability, and data burden of existing ICU risk-adjustment models.

Methods: A retrospective chart review of 11,300 ICU patients from 35 California hospitals from 2001-2004 was performed. We calculated Standardized Mortality Ratios (SMRs) for each hospital using the Mortality Probability Model III (MPM₀ III), the Simplified Acute Physiology Score II (SAPS II), and the Acute Physiology and Chronic Health Evaluation IV (APACHE® IV) risk-adjustment models. We compared discrimination, calibration, data reliability, and abstraction time for the models.

Results: Regardless of model used, there was large variation in SMRs among ICUs. Discrimination and calibration were adequate for all risk-adjustment models. APACHE® IV had the best discrimination, AUC=0.892 compared to MPM₀ III, 0.809, and SAPS II, 0.873 (p>0.001). The models differed substantially in data abstraction times, MPM₀III 11.1 minutes (95% CI 8.7-13.4), SAPS II 19.6 minutes (95% CI 17.0-22.2), and APACHE® IV 37.3 minutes (95% CI 28.0-46.6).

Conclusions: We found substantial variation in ICU risk-adjusted mortality rates that persisted regardless of the risk-adjustment model. With unlimited resources APACHE® IV offers the best predictive accuracy. If constrained by cost and manual data collection, MPM₀ III offers a viable alternative without substantial loss in accuracy.

Key Words: Intensive care unit • Outcome Assessment (Health Care), Quality of Health Care • Risk Adjustment • Severity of Illness Index