title: [Prediction model for in-hospital admission in patients arriving in the emergency department].
abstract: BACKGROUND
  To develop a prediction model for in-hospital admission to provide an almost "real time" determination of hospital beds needed, so as to predict the resources required as early as possible.
  
  
  METHODS
  A prospective observational study in the emergency department of a university hospital. We included all consecutive patients between 8.00-22.00 hours during one month. We analyzed 7 variables taken when the patient arrived at the emergency department: age, sex, level of triage, initial disposition, first diagnosis, diagnostic test and medication, and we performed a logistic regression.
  
  
  RESULTS
  We included 2,476 visits of which 114 (4.6%) were admitted. A significant direct correlation was seen between: age >65 years old (odds ratio[OR]=2.1, confidence interval [CI] 95%,1.3-3.2; p=0.001); male sex (OR=1.6, IC 95%,1.1-2.4; p=0.020); dyspnea (OR=5.2, IC 95%, 2.8-9.7; p<0.0001), abdominal pain (OR=4.7, IC 95%, 2.7-8.3; p<0.0001); acute care initial disposition (OR=8.9, IC 95%, 5.4-14.9; p<0.0001), diagnostic test (OR=1.1, IC 95%,0.9-1.3; p=0.064) and treatment prescription (OR=2.6, IC95%,1.6-4.2; p=<0.0001). The model had a sensitivity of 76% and a specificity of 82% (area under curve 0.85 [IC 95% 0.81-0.88; p<0.001]).
  
  
  CONCLUSIONS
  The in-hospital admission prediction model is a good and useful tool for predicting the in-hospital beds needed when patients arrive at the emergency department.
authors.affiliation: Servicio Admisión y Documentación Clínica, Hospital Clínico San Carlos, Madrid, Spain.
authors.forename: C M
authors.initials: CM
authors.lastname: Elvira Martínez
authors.forename: C
authors.initials: C
authors.lastname: Fernández
authors.forename: J
authors.initials: J
authors.lastname: González Del Castillo
authors.forename: J J
authors.initials: JJ
authors.lastname: González Armengol
authors.forename: P
authors.initials: P
authors.lastname: Villarroel
authors.forename: F J
authors.initials: FJ
authors.lastname: Martín Sánchez
mesh_terms: D000293:Adolescent; D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D004636:Emergency Service, Hospital; D005260:Female; D005544:Forecasting; D006801:Humans; D008297:Male; D008875:Middle Aged; D015233:Models, Statistical; D010343:Patient Admission / Q000706:statistics & numerical data*; D011446:Prospective Studies; D055815:Young Adult