title: Derivation and internal validation of a rule to predict hospital admission in prehospital patients.
abstract: OBJECTIVE
  To derive and internally validate a simple prediction rule, using routinely collected prehospital patient data, that discriminates between hospital admission and emergency department (ED) discharge for adult patients who arrive by ambulance.
  
  
  METHODS
  We performed a retrospective cohort study of consecutive adult nontrauma patients transported to two separate EDs over two months by a city-run emergency medical services (EMS) system. We tested whether specific prehospital variables could predict hospital admission using chi-square tests, logistic regression, and receiver-operating characteristic curves. We created a rule to predict the probabilities of hospital admission for individual patients.
  
  
  RESULTS
  Of 401 patients, the mean age was 47 years; 60% were black and 32% were white; 51% were female; and 33% were admitted to an inpatient service after evaluation in the ED. Independent predictors of admission were dyspnea (adjusted odds ratio [OR] 6.8; awarded 3 points), chest pain (OR 5.2; 3 points), and dizziness, weakness, or syncope (OR 3.5; 2 points). Also predictive were age>or=60 years (OR 5.5; 3 points) and the prehospital identification of a history of diabetes (OR 1.9; 1 point) or cancer (OR 3.9; 2 points). Patients who had a score of 5 or higher had a greater than 69% chance of being admitted to an inpatient unit.
  
  
  CONCLUSIONS
  Routinely collected EMS patient information can help predict hospital admission for certain ED patients.
authors.affiliation: Department of Emergency Medicine, University of Pennsylvania School of Medicine, Philadelphia, PA 19104, USA. zachary.meisel@uphs.upenn.edu
authors.forename: Zachary F
authors.initials: ZF
authors.lastname: Meisel
authors.forename: Charles V
authors.initials: CV
authors.lastname: Pollack
authors.forename: C Crawford
authors.initials: CC
authors.lastname: Mechem
authors.forename: Jesse M
authors.initials: JM
authors.lastname: Pines
mesh_terms: D000208:Acute Disease / Q000145:classification*; D000328:Adult; D000552:Ambulances / Q000706:statistics & numerical data; D015331:Cohort Studies; D005260:Female; D005544:Forecasting; D006801:Humans; D016015:Logistic Models; D008297:Male; D008875:Middle Aged; D015999:Multivariate Analysis; D010343:Patient Admission; D012372:ROC Curve; D012189:Retrospective Studies; D012680:Sensitivity and Specificity; D014218:Triage / Q000379:methods*