Study objective: The development and validation of a pediatric emergency department severity of illness assessment method, using hospital admission as the primary outcome.
Methods: A random sample of 25% of ED charts from 4 consecutive months in a university-affiliated pediatric hospital was reviewed, after exclusion of children with minor injuries and children triaged to the nonurgent clinic. Sampled data included components of the medical history, physical findings, physiologic variables, diagnoses, and ED therapies. Univariate and multivariate logistic regression analyses, with bootstrapping validation, were performed to develop a bias-corrected model estimating the probability of hospital admission.
Results: Of the 2,683 ED patients whose records were reviewed, 643 (24%) were admitted to the hospital. The final model, which yielded a Pediatric Risk of Admission (PRISA) score, included the following: 3 components of the medical history, 3 chronic disease factors, 9 physiologic variables, 2 therapies, and 4 interaction terms. Overall, the number of hospital admissions was well predicted in both the 80% development and 20% validation samples. In the former, 514 admissions were predicted and 514 were observed; in the latter, 126.9 admissions were predicted and 129 were observed. The Hosmer-Lemeshow goodness-of-fit test demonstrated good agreement between observed and expected admissions in consecutive deciles of admission probability; total chi2 was 10.49 (P=.233) for the development sample and 11.85 (P=.222) for the validation sample. The areas under the receiver operating characteristic curves (+/-SE) were .86+/-.011 and .825+/-.024, respectively. As the risk of hospital admission increased, the proportions of patients using unique hospital-based resources and using ICU resources increased, and the proportion of patients dying increased.
Conclusion: The probability of admission to the hospital can reliably be estimated from data available during the pediatric ED stay. Applications for this method include studies of quality and efficiency of care and measurements of severity of illness.