Presented at the Society for Academic Emergency Medicine (SAEM) annual meeting, Washington DC, May 2008.
Multicenter Validation of the Philadelphia EMS Admission Rule (PEAR) to Predict Hospital Admission in Adult Patients Using Out-of-hospital Data
Article first published online: 11 MAY 2009
© 2009 by the Society for Academic Emergency Medicine
Academic Emergency Medicine
Volume 16, Issue 6, pages 519–525, June 2009
How to Cite
Meisel, Z. F., Mathew, R., Wydro, G. C., Crawford Mechem, C., Pollack, C. V., Katzer, R., Prabhu, A., Ozumba, A. and Pines, J. M. (2009), Multicenter Validation of the Philadelphia EMS Admission Rule (PEAR) to Predict Hospital Admission in Adult Patients Using Out-of-hospital Data. Academic Emergency Medicine, 16: 519–525. doi: 10.1111/j.1553-2712.2009.00422.x
Dr. Meisel was supported by the Robert Wood Johnson Foundation Clinical Scholars Program.
- Issue published online: 1 JUN 2009
- Article first published online: 11 MAY 2009
- Received December 23, 2008; revision received February 13, 2009; accepted February 17, 2009.
- emergency medical services;
- prehospital care;
- prediction rule;
- resource utilization;
- ambulance diversion
Objectives: The objective was to validate a previously derived prediction rule for hospital admission using routinely collected out-of-hospital information.
Methods: The authors performed a multicenter retrospective cohort study of 1,500 randomly selected, adult patients transported to six separate emergency departments (EDs; three community and three academic hospitals in three separate health systems) by a city-run emergency medical services (EMS) system over a 1-year period. Patients younger than 18 years or who bypassed the ED to be evaluated by trauma, obstetric, or psychiatric teams were excluded. The score consisted of six weighted elements that generated a total score (0–14): age ≥ 60 years (3 points); chest pain (3); shortness of breath (3); dizzy, weakness, or syncope (2); history of cancer (2); and history of diabetes (1). Receiver operator characteristic (ROC) curves for the decision rule and admission rates were calculated among individual hospitals and for the entire cohort.
Results: A total of 1,102 patients met inclusion criteria. The admission rate for the entire cohort was 40%, and individual hospital admission rates ranged from 28% to 57%. Overall, 34% had a score of ≥4, and 29% had a score of ≥5. Area under the ROC curve (AUC) for the combined cohort was 0.83 for all admissions and 0.72 for intensive care unit (ICU) admissions; AUCs at individual hospitals ranged from 0.72 to 0.85. The admission rate for a score of ≥4 was 77%; for a score of ≥5 the admission rate was 80%.
Conclusions: The ability of this EMS rule to predict the likelihood of hospital admission appears valid in this multicenter cohort. Further studies are needed to measure the impact and feasibility of using this rule to guide decision-making.