title: [Scores to predict the number of hospitalization days in a general ward at a tertiary hospital].
abstract: Although most usual admissions to hospital are in rooms in general wards, there are not scores to predict the number of hospitalization days in this area. The patients are located based on diagnosis at admission.
  
  
  OBJECTIVE
  to make scores who predict the number of hospitalization days in the general wards.
  
  
  METHODS
  We studied all the patients who were admitted to the Italiano Hospital from march of 2004 to may of 2005 in Cordoba city (Argentina). The inclusion criteria were: more than 18 years old patients who were admitted for more than 24 hours due to clinical or surgical conditions in general wards. We evaluated 53 variables including background, toxics, physiologic and demographic data, social reports, nutritional condition, out patients previous consultations at the admission day. Died patients were not included in this score analysis.
  
  
  RESULTS
  The number of patients included was 1003. Short hospitalization was considered when the number of the days of the hospitalization was less than 4 days and long hospitalization was more than 5 day in a general ward. We made a score with 11 main variables according to physician clinical perception. The statistical analysis was not significant in each variable studied. When we analyzed the score with 11 of them as a whole, it showed statistical significance. We divided in categories and pointing according to statistic settlements. Minimum pointing: 11; maximum: 33. Showed R2: 0,77 ( p: 0,06) between pointing and the hospitalization days.
  
  
  CONCLUSIONS
  The lower scores were related with a high chance of discharge before 5 days. This score may be a simple and feasible tool for the hospital administration and for the prediction of available beds in a general wards.
authors.affiliation: Hospital Italiano de Córdoba, Paz, Argentina. Gerardobelletti@hotmail.com
authors.forename: Gerardo A
authors.initials: GA
authors.lastname: Belletti
authors.forename: Julio
authors.initials: J
authors.lastname: Enders
authors.forename: Gustavo
authors.initials: G
authors.lastname: Serra
authors.forename: Marcelo A
authors.initials: MA
authors.lastname: Yorio
mesh_terms: D000293:Adolescent; D000328:Adult; D000368:Aged; D001118:Argentina; D004636:Emergency Service, Hospital / Q000458:organization & administration; D005260:Female; D006769:Hospitals, General / Q000706:statistics & numerical data; D006801:Humans; D007902:Length of Stay / Q000706:statistics & numerical data* / Q000639:trends; D008297:Male; D008875:Middle Aged; D015233:Models, Statistical; D010343:Patient Admission / Q000706:statistics & numerical data; D010362:Patients' Rooms / Q000458:organization & administration*; D011446:Prospective Studies; D012720:Severity of Illness Index; D055815:Young Adult