title: Measure of functional independence dominates discharge outcome prediction after inpatient rehabilitation for stroke.
abstract: OBJECTIVE
  Identifying clinical data acquired at inpatient rehabilitation admission for stroke that accurately predict key outcomes at discharge could inform the development of customized plans of care to achieve favorable outcomes. The purpose of this analysis was to use a large comprehensive national data set to consider a wide range of clinical elements known at admission to identify those that predict key outcomes at rehabilitation discharge.
  
  
  METHODS
  Sample data were obtained from the Uniform Data System for Medical Rehabilitation data set with the diagnosis of stroke for the years 2005 through 2007. This data set includes demographic, administrative, and medical variables collected at admission and discharge and uses the FIM (functional independence measure) instrument to assess functional independence. Primary outcomes of interest were functional independence measure gain, length of stay, and discharge to home.
  
  
  RESULTS
  The sample included 148,367 people (75% white; mean age, 70.6±13.1 years; 97% with ischemic stroke) admitted to inpatient rehabilitation a mean of 8.2±12 days after symptom onset. The total functional independence measure score, the functional independence measure motor subscore, and the case-mix group were equally the strongest predictors for any of the primary outcomes. The most clinically relevant 3-variable model used the functional independence measure motor subscore, age, and walking distance at admission (r(2)=0.107). No important additional effect for any other variable was detected when added to this model.
  
  
  CONCLUSIONS
  This analysis shows that a measure of functional independence in motor performance and age at rehabilitation hospital admission for stroke are predominant predictors of outcome at discharge in a uniquely large US national data set.
authors.affiliation: From the Department of Physical Medicine and Rehabilitation (A.W.B., B.A.S.) and Department of Biomedical Statistics and Informatics, Mayo Clinic, Rochester, MN (T.M.T.); Uniform Data System for Medical Rehabilitation, Buffalo, NY (P.M.N., C.V.G.); Department of Health Care Studies, Daemen College, Amherst, NY (P.M.N.); and the Department of Neurology, University at Buffalo, Buffalo, NY (C.V.G.). brown.allen@mayo.edu.
authors.forename: Allen W
authors.initials: AW
authors.lastname: Brown
authors.affiliation: From the Department of Physical Medicine and Rehabilitation (A.W.B., B.A.S.) and Department of Biomedical Statistics and Informatics, Mayo Clinic, Rochester, MN (T.M.T.); Uniform Data System for Medical Rehabilitation, Buffalo, NY (P.M.N., C.V.G.); Department of Health Care Studies, Daemen College, Amherst, NY (P.M.N.); and the Department of Neurology, University at Buffalo, Buffalo, NY (C.V.G.).
authors.forename: Terry M
authors.initials: TM
authors.lastname: Therneau
authors.affiliation: From the Department of Physical Medicine and Rehabilitation (A.W.B., B.A.S.) and Department of Biomedical Statistics and Informatics, Mayo Clinic, Rochester, MN (T.M.T.); Uniform Data System for Medical Rehabilitation, Buffalo, NY (P.M.N., C.V.G.); Department of Health Care Studies, Daemen College, Amherst, NY (P.M.N.); and the Department of Neurology, University at Buffalo, Buffalo, NY (C.V.G.).
authors.forename: Billie A
authors.initials: BA
authors.lastname: Schultz
authors.affiliation: From the Department of Physical Medicine and Rehabilitation (A.W.B., B.A.S.) and Department of Biomedical Statistics and Informatics, Mayo Clinic, Rochester, MN (T.M.T.); Uniform Data System for Medical Rehabilitation, Buffalo, NY (P.M.N., C.V.G.); Department of Health Care Studies, Daemen College, Amherst, NY (P.M.N.); and the Department of Neurology, University at Buffalo, Buffalo, NY (C.V.G.).
authors.forename: Paulette M
authors.initials: PM
authors.lastname: Niewczyk
authors.affiliation: From the Department of Physical Medicine and Rehabilitation (A.W.B., B.A.S.) and Department of Biomedical Statistics and Informatics, Mayo Clinic, Rochester, MN (T.M.T.); Uniform Data System for Medical Rehabilitation, Buffalo, NY (P.M.N., C.V.G.); Department of Health Care Studies, Daemen College, Amherst, NY (P.M.N.); and the Department of Neurology, University at Buffalo, Buffalo, NY (C.V.G.).
authors.forename: Carl V
authors.initials: CV
authors.lastname: Granger
mesh_terms: D000367:Age Factors; D000368:Aged; D000369:Aged, 80 and over; D004185:Disability Evaluation; D005260:Female; D006760:Hospitalization; D006801:Humans; D008297:Male; D008875:Middle Aged; D017063:Outcome Assessment, Health Care; D010351:Patient Discharge; D020521:Stroke / Q000453:epidemiology; D000071939:Stroke Rehabilitation; D014481:United States / Q000453:epidemiology
keywords: nursing homes; rehabilitation; residence characteristics; treatment outcome