title: Using admission characteristics to predict return to the community from a post-acute geriatric evaluation and management unit.
abstract: OBJECTIVE
  To compare the Cumulative Illness Rating Scale (CIRS) and the Nursing Severity Index (NSI) as independent predictors of discharge outcome from a post-acute GEM unit and to define a multivariate model for predicting the same outcome.
  
  
  METHODS
  Retrospective chart review for the entire sample. The sample was split into two cohorts, a derivation cohort (n = 298) and a validation cohort (n = 154).
  
  
  METHODS
  A 20-bed, post-acute GEM unit in a nonproprietary skilled nursing facility.
  
  
  METHODS
  All 452 patients admitted to the GEM from the unit's inception in December 1994 until January 1998.
  
  
  METHODS
  Demographics, CIRS, NSI, functional status, and social support variables were measured using data available on admission to the GEM unit. The discharge outcome was dichotomized as return to the community or not.
  
  
  RESULTS
  A total of 99.7% of the individuals in the derivation cohort were living in the community before the index hospitalization; 75.8% of patients in the derivation cohort returned to the community. The NSI, individual "severe" items from the CIRS, age, and social support were in the final logistic regression model fitted to the derivation cohort. A total of 87.7% of the observed discharge outcomes were predicted when the model was applied to the validation cohort and the calculated probability of return to the community.
  
  
  CONCLUSIONS
  Variables for severity of illness, function, social support, and age combined into a logistic regression equation that predicted more than 80% of the dichotomized discharge outcome in the derivation cohort. The proportion of discharge outcomes that were predicted with the validation cohort remained high at 87.7%. The NSI and CIRS were each important to a model that is anticipated to refine the selection of geriatric patients for post-acute services.
authors.affiliation: School of Medicine and Biomedical Sciences, State University of New York at Buffalo, USA.
authors.forename: B J
authors.initials: BJ
authors.lastname: Naughton
authors.forename: S
authors.initials: S
authors.lastname: Saltzman
authors.forename: R
authors.initials: R
authors.lastname: Priore
authors.forename: K
authors.initials: K
authors.lastname: Reedy
authors.forename: J M
authors.initials: JM
authors.lastname: Mylotte
mesh_terms: D000367:Age Factors; D000368:Aged; D015331:Cohort Studies; D003625:Data Collection; D005260:Female; D015577:Geriatric Assessment; D006801:Humans; D016015:Logistic Models; D008297:Male; D015999:Multivariate Analysis; D009722:Nurse Practitioners; D010351:Patient Discharge; D011237:Predictive Value of Tests; D012189:Retrospective Studies; D012720:Severity of Illness Index; D012866:Skilled Nursing Facilities; D012944:Social Support