Multicenter validation of the Philadelphia EMS admission rule (PEAR) to predict hospital admission in adult patients using out-of-hospital data.
OBJECTIVE
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 >or= 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 >or=4, and 29% had a score of >or=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 >or=4 was 77%; for a score of >or=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.
Meisel
Zachary F
ZF
The Robert Wood Johnson Foundation Clinical Scholars Program, Leonard Davis Institute of Health Economics, and the Department of Emergency Medicine, University of Pennsylvania School of Medicine, Philadelphia, PA, USA. zachary.meisel@uphs.upenn.edu
Mathew
Rex
R
Wydro
Gerald C
GC
Crawford Mechem
C
C
Pollack
Charles V
CV
Katzer
Robert
R
Prabhu
Anjeli
A
Ozumba
Adora
A
Pines
Jesse M
JM
D000046:Academic Medical Centers; D015331:Cohort Studies; D016208:Databases, Factual; D004632:Emergency Medical Services / Q000706:statistics & numerical data*; D004636:Emergency Service, Hospital / Q000706:statistics & numerical data; D005260:Female; D006764:Hospitals, Community; D006801:Humans; D007362:Intensive Care Units; D016015:Logistic Models; D008297:Male; D008875:Middle Aged; D010343:Patient Admission / Q000706:statistics & numerical data*; D010351:Patient Discharge / Q000706:statistics & numerical data*; D015143:Philadelphia; D012372:ROC Curve; D015203:Reproducibility of Results; D012189:Retrospective Studies; D014187:Transportation of Patients