Article

Revised Pediatric Emergency Assessment Tool (RePEAT): a severity index for pediatric emergency care.

The Children's Hospital of Philadelphia, Filadelfia, Pennsylvania, United States
Academic Emergency Medicine (Impact Factor: 2.2). 05/2007; 14(4):316-23. DOI: 10.1197/j.aem.2006.11.015
Source: PubMed

ABSTRACT To develop and validate a multivariable model, using information available at the time of patient triage, to predict the level of care provided to pediatric emergency patients for use as a severity of illness measure.
This was a retrospective cohort study of 5,521 children 18 years of age or younger treated at four emergency departments (EDs) over a 12-month period. Data were obtained from abstraction of patient records. Logistic regression was used to develop (75% of sample) and validate (25% of sample) models to predict any nonroutine diagnostic or therapeutic intervention in the ED and admission to the hospital. Data on ED length of stay and hospital costs were also obtained.
Eight predictor variables were included in the final models: presenting complaint, age, triage acuity category, arrival by emergency medical services, current use of prescription medications, and three triage vital signs (heart rate, respiratory rate, and temperature). The resulting models had adequate goodness of fit in both derivation and validation samples. The area under the receiver operating characteristic curve was 0.73 for the ED intervention model and 0.85 for the admission model. The Revised Pediatric Emergency Assessment Tool (RePEAT) score was then calculated as the sum of the predicted probability of receiving intervention and twice the predicted probability of admission. The RePEAT score had a significant univariate association with ED costs (r = 0.44) and with ED length of stay (r = 0.27) and contributed significantly to the fit of multivariable models comparing these outcomes across sites.
The RePEAT score accurately predicts level of care provided for pediatric emergency patients and may provide a useful means of risk adjustment when benchmarking outcomes

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