title: Validation of a Predictive Model to Identify Patients at High Risk for Hospital Readmission.
abstract: BACKGROUND
  Hospital readmission is an adverse patient outcome that is serious, common, and costly. For hospitals, identifying patients at risk for hospital readmission is a priority to reduce costs and improve care.
  
  
  OBJECTIVE
  The purposes were to validate a predictive algorithm to identify patients at a high risk for preventable hospital readmission within 30 days after discharge and determine if additional risk factors enhance readmission predictability.
  
  
  METHODS
  A retrospective study was conducted on a randomized sample of 598 patients discharged from a Southeast community hospital. Data were collected from the organization's database and manually abstracted from the electronic medical record using a structured tool. Two separate logistic regression models were fit for the probability of readmission within 30 days after discharge. The first model used the LACE index as the predictor variable, and the second model used the LACE index with additional risk factors. The two models were compared to determine if additional risk factors increased the model's predictive ability.
  
  
  RESULTS
  The results indicate both models have reasonable prognostic capability. The LACE index with additional risk factors did little to improve prognostication, while adding to the model's complexity.
  
  
  CONCLUSIONS
  Findings support the use of the LACE index as a practical tool to identify patients at risk for readmission.
authors.forename: LeeAnna
authors.initials: L
authors.lastname: Spiva
authors.forename: Marti
authors.initials: M
authors.lastname: Hand
authors.forename: Lewis
authors.initials: L
authors.lastname: VanBrackle
authors.forename: Frank
authors.initials: F
authors.lastname: McVay
mesh_terms: D000328:Adult; D000368:Aged; D000465:Algorithms; D005260:Female; D005544:Forecasting / Q000379:methods*; D006764:Hospitals, Community / Q000706:statistics & numerical data*; D006801:Humans; D016015:Logistic Models; D008297:Male; D008875:Middle Aged; D010359:Patient Readmission / Q000706:statistics & numerical data* / Q000639:trends*; D012189:Retrospective Studies; D018570:Risk Assessment / Q000379:methods*; D012307:Risk Factors; D015144:Southeastern United States