Pediatric emergency assessment tool (PEAT): a risk-adjustment measure for pediatric emergency patients

M H Gorelick, C Lee, K Cronan, S Kost, K Palmer
Academic Emergency Medicine 2001, 8 (2): 156-62

OBJECTIVE: To develop a multivariable model predicting the level of care required by pediatric patients for use as a risk-adjustment tool in the evaluation of emergency medical services for children.

METHODS: A random 10% sample of records of all visits over a 12-month period to a suburban, university-affiliated pediatric emergency department (PED) was selected and abstracted. The outcome variable, level of care received, was categorized in three levels: routine care only (R); diagnostic or therapeutic procedures performed in the ED but patient not admitted (EDT); and admission to hospital (ADM). Predictor variables included information routinely elicited and recorded at the time of triage. Using multinomial logistic regression, a predictive model was derived from a subset of 70% of the selected visits, and was validated in the remaining 30%.

RESULTS: The total sample included 2,287 visits. The overall rate of each outcome was R-37%, EDT-53%, and ADM-10%. The final regression model included the following predictors significantly associated with the outcome: age, past medical history, temperature, abnormal respiratory rate or pulse oximetry in triage, chief complaint, and triage level (model likelihood ratio chi-square, 14 df = 332, p < 0.00001, R(2) = 0.14). The number of outcomes was well predicted by the model in both subsamples. Analysis of variance showed a significant association between Pediatric Emergency Assessment Tool (PEAT) score (weighted sum of the predicted probabilities of EDT and ADM) and both ED charges and time spent in the ED (p < 0.001).

CONCLUSIONS: A model based on easily and routinely measured variables can accurately predict the level of care rendered in the PED. The predicted probabilities from such a model correlate well with other outcomes of care and may be useful in adjusting for differences in risk when evaluating quality of care.

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