Statistical models to predict the need for postoperative intensive care and hospitalization in pediatric surgical patients.

Objective: To develop statistical models for predicting postoperative hospital and ICU stay in pediatric surgical patients based on preoperative clinical characteristics and operative factors related to the degree of surgical stress. We hypothesized that preoperative and operative factors will predict the need for ICU admission and may be used to forecast the length of ICU stay or postoperative hospital stay.

Design: Prospective data collection from 1,763 patients.

Setting: Tertiary care children's hospital.

Patients And Participants: All pediatric surgical patients, including those undergoing day surgery. Patients undergoing dental or ophthalmologic surgical procedures were excluded.

Interventions: None.

Measurements And Results: A logistic regression model predicting ICU admission was developed from all patients. Poissonregression models were developed from 1,161 randomly selected patients and validated from the remaining 602 patients. The logistic regression model for ICU admission was highlypredictive (area under the receiver operating characteristics (ROC) curve = 0.981). In the data set used for development of Poisson regression models, significant correlations occurred between the observed and predicted ICU stay (Pearson r = 0.468, p < 0.0001, n = 131) and between the observed and predicted hospital stay for patients undergoing general (r = 0.695, p < 0.0001), orthopedic (r = 0.717, p < 0.0001), cardiothoracic (r = 0.746, p < 0.0001), urologic (r = 0.458, p < 0.0001), otorhinolaryngologic (r = 0.962, p < 0.0001), neurosurgical (r = 0.7084, p < 0.0001) and plastic surgical (r = 0.854, p < 0.0001) procedures. In the validation data set, correlations between predicted and observed hospital stay were significant for general (p < 0.0001), orthopedic (p < 0.0001), cardiothoracic (p = 0.0321) and urologic surgery (p = 0.0383). The Poisson models for length of ICU stay, otorhinolaryngology, neurosurgery or plastic surgery could not be validated because of small numbers of patients.

Conclusions: Preoperative and operative factors may be used to develop statistical models predicting the need for ICU admission in pediatric surgical patients, and hospital stay following general surgical, orthopedic, cardiothoracic and urologic procedures. These statistical models need to be refined and validatedfurther, perhaps using data collection from multiple institutions.