title: Discharge destination following lower limb fracture: development of a prediction model to assist with decision making.
abstract: BACKGROUND
  Accurate prediction of the likelihood of discharge to inpatient rehabilitation following lower limb fracture made on admission to hospital may assist patient discharge planning and decrease the burden on the hospital system caused by delays in decision making.
  
  
  OBJECTIVE
  To develop a prognostic model for discharge to inpatient rehabilitation.
  
  
  METHODS
  Isolated lower extremity fracture cases (excluding fractured neck of femur), captured by the Victorian Orthopaedic Trauma Outcomes Registry (VOTOR), were extracted for analysis. A training data set was created for model development and validation data set for evaluation. A multivariable logistic regression model was developed based on patient and injury characteristics. Models were assessed using measures of discrimination (C-statistic) and calibration (Hosmer-Lemeshow (H-L) statistic).
  
  
  RESULTS
  A total of 1429 patients met the inclusion criteria and were randomly split into training and test data sets. Increasing age, more proximal fracture type, compensation or private fund source for the admission, metropolitan location of residence, not working prior to injury and having a self-reported pre-injury disability were included in the final prediction model. The C-statistic for the model was 0.92 (95% confidence interval (CI) 0.88, 0.95) with an H-L statistic of χ(2)=11.62, p=0.17. For the test data set, the C-statistic was 0.86 (95% CI 0.83, 0.90) with an H-L statistic of χ(2)=37.98, p<0.001.
  
  
  CONCLUSIONS
  A model to predict discharge to inpatient rehabilitation following lower limb fracture was developed with excellent discrimination although the calibration was reduced in the test data set. This model requires prospective testing but could form an integral part of decision making in regards to discharge disposition to facilitate timely and accurate referral to rehabilitation and optimise resource allocation.
authors.affiliation: Department of Epidemiology and Preventive Medicine, Monash University, Melbourne, Australia. l.kimmel@alfred.org.au
authors.forename: Lara A
authors.initials: LA
authors.lastname: Kimmel
authors.forename: Anne E
authors.initials: AE
authors.lastname: Holland
authors.forename: Elton R
authors.initials: ER
authors.lastname: Edwards
authors.forename: Peter A
authors.initials: PA
authors.lastname: Cameron
authors.forename: Richard
authors.initials: R
authors.lastname: De Steiger
authors.forename: Richard S
authors.initials: RS
authors.lastname: Page
authors.forename: Belinda
authors.initials: B
authors.lastname: Gabbe
mesh_terms: D000328:Adult; D000368:Aged; D001315:Australia; D003657:Decision Making; D005260:Female; D050723:Fractures, Bone / Q000191:economics / Q000534:rehabilitation / Q000601:surgery*; D006760:Hospitalization / Q000191:economics; D006801:Humans; D015601:Injury Severity Score; D035002:Lower Extremity / Q000293:injuries / Q000601:surgery*; D008297:Male; D008875:Middle Aged; D010351:Patient Discharge / Q000191:economics; D011379:Prognosis; D011446:Prospective Studies; D012017:Referral and Consultation; D040841:Resource Allocation / Q000191:economics