RESUMO
OBJECTIVE: To evaluate the feasibility of an automated intensive care unit (ICU) risk adjustment tool (acronym: SISVistA) developed by selecting a subset of predictor variables from the Acute Physiology and Chronic Health Evaluation (APACHE) III available in the existing computerized database of the Department of Veterans Affairs (VA) healthcare system and modifying the APACHE diagnostic and comorbidity approach. DESIGN: Retrospective cohort study. SETTING: Six ICUs in three Ohio Veterans Affairs hospitals. PATIENT SELECTION: The first ICU admission of all patients from February 1996 through July 1997. OUTCOME MEASURE: Mortality at hospital discharge. METHODS: The predictor variables, including age, comorbidity, diagnosis, admission source (direct or transfer), and laboratory results (from the +/- 24-hr period surrounding admission), were extracted from computerized VA databases, and APACHE III weights were applied using customized software. The weights of all laboratory variables were added and treated as a single variable in the model. A logistic regression model was fitted to predict the outcome and the model was validated using a boot-strapping technique (1,000 repetitions). MAIN RESULTS: The analysis included all 4,651 eligible cases (442 deaths). The cohort was predominantly male (97.5%) and elderly (63.6 +/- 12.0 yrs). In multivariate analysis, significant predictors of hospital mortality included age (odds ratio [OR], 1.06; 95% confidence interval [CI], 1.04-1.09), comorbidity (OR, 1.11; 95% CI, 1.08-1.15), total laboratory score (OR, 1.07; 95% CI, 1.06-1.08), direct ICU admission (OR, 0.39; 95% CI, 0.31-0.49), and several broad ICU diagnostic categories. The SISVistA model had excellent discrimination and calibration (C statistic = 0.86, goodness-of-fit statistics; p > .20). The area under the receiver operating characteristic curve of the validated model was 0.86. CONCLUSIONS: Using common data elements often found in hospital computer systems, SISVistA predicts hospital mortality among patients in Ohio VA ICUs. This preliminary study supports the development of an automated ICU risk prediction system on a more diverse population.
