Dynamic assessment of severity of illness in pediatric intensive care

Abstract

Severity of illness in 293 pediatric ICU patients was assessed by a daily estimate of ICU survival. The probability of nonsurvival was obtained by logistic regression analysis, using physiologic stability index (PSI) values from previous days as time-dependent covariates. Only PSI values from the previous 2 days gave statistically significant predictions of short-term (less than 24 h) outcome. When the prediction model derived from these data was tested prospectively on a separate set of 345 pediatric patients, there was excellent agreement between observed and predicted short-term mortality. Receiver operating characteristic curves for the 345 patients were statistically equivalent to those originally derived for the 293 patients, and this prediction model had significantly (p less than .025) more accuracy than prediction based on admission PSI. These results indicate that this model for daily risk assessment is statistically reliable and objective, as verified against eventual outcome. In the 345 patients, ICU mortality was predicted with 89% sensitivity and 91% specificity. This prediction model may be used to stratify patient groups for clinical studies, or identify very low-risk patients for potential early ICU discharge.