title: Predicting discharge destination of stroke patients using a mathematical model based on six items from the Functional Independence Measure.
abstract: OBJECTIVE
  A mathematical model using selected items from the Functional Independence Measure (FIM) was developed to predict disposition of stroke patients from an acute care hospital.
  
  
  METHODS
  Case series of 279 acute stroke patients admitted to the hospital from 4/91 through 12/93. Data collection remains ongoing; a second series of patients will be analyzed to validate results and determine if the same 6 FIM items are significant.
  
  
  METHODS
  Tertiary care center, general acute care community hospital.
  
  
  METHODS
  Diagnosis of acute stroke; 298 patients whose attending physician referred the patient to the rehabilitation team. An 18-item FIM was administered within 3 days of admission and 24 hours of discharge.
  
  
  METHODS
  FIM scores were analyzed to determine if the initial score could be used to predict disposition. Discriminant analysis identified 6 items as being statistically significant in predicting discharge to home, rehabilitation facility, or nursing home. Patient's actual discharge location was compared with the location predicted by the model developed using the FIM.
  
  
  RESULTS
  Bathing, bowel, toileting, social interaction, dressing lower body, and eating were the selected initial FIM items that predicted disposition with 70% accuracy in our patient series.
  
  
  CONCLUSIONS
  The FIM is effective in the acute care setting to help determine appropriate discharge status, with certain variables being more predictive than others.
authors.affiliation: Physical Medicine and Rehabilitation Department, St. Luke's Hospital, Bethlehem, PA 18015, USA.
authors.forename: R W
authors.initials: RW
authors.lastname: Mauthe
authors.forename: D C
authors.initials: DC
authors.lastname: Haaf
authors.forename: P
authors.initials: P
authors.lastname: Hayn
authors.forename: J M
authors.initials: JM
authors.lastname: Krall
mesh_terms: D000203:Activities of Daily Living / Q000145:classification; D000368:Aged; D000704:Analysis of Variance; D002561:Cerebrovascular Disorders / Q000534:rehabilitation*; D003625:Data Collection; D004185:Disability Evaluation; D016002:Discriminant Analysis; D005260:Female; D018574:Home Care Agencies / Q000706:statistics & numerical data; D006801:Humans; D016014:Linear Models; D008297:Male; D008962:Models, Theoretical; D009735:Nursing Homes / Q000706:statistics & numerical data; D010351:Patient Discharge / Q000706:statistics & numerical data*; D011237:Predictive Value of Tests; D016896:Treatment Outcome