title: Unplanned readmissions after hospital discharge among patients identified as being at high risk for readmission using a validated predictive algorithm.
abstract: BACKGROUND
  Unplanned hospital readmissions are common, expensive and often preventable. Strategies designed to reduce readmissions should target patients at high risk. The purpose of this study was to describe medical patients identified using a recently published and validated algorithm (the LACE index) as being at high risk for readmission and to examine their actual hospital readmission rates.
  
  
  METHODS
  We used population-based administrative data to identify adult medical patients discharged alive from 6 hospitals in Toronto, Canada, during 2007. A LACE index score of 10 or higher was used to identify patients at high risk for readmission. We described patient and hospitalization characteristics among both the high-risk and low-risk groups as well as the 30-day readmission rates.
  
  
  RESULTS
  Of 26 045 patients, 12.6% were readmitted to hospital within 30 days and 20.9% were readmitted within 90 days of discharge. High-risk patients (LACE ≥ 10) accounted for 34.0% of the sample but 51.7% of the patients who were readmitted within 30 days. High-risk patients were readmitted with twice the frequency as other patients, had longer lengths of stay and were more likely to die during the readmission.
  
  
  CONCLUSIONS
  Using a LACE index score of 10, we identified patients with a high rate of readmission who may benefit from improved post-discharge care. Our findings suggest that the LACE index is a potentially useful tool for decision-makers interested in identifying appropriate patients for post-discharge interventions.
authors.affiliation: Women’s College Research Institute, University of Toronto, Canada. andrea.gruneir@wchospital.ca
authors.forename: Andrea
authors.initials: A
authors.lastname: Gruneir
authors.forename: Irfan A
authors.initials: IA
authors.lastname: Dhalla
authors.forename: Carl
authors.initials: C
authors.lastname: van Walraven
authors.forename: Hadas D
authors.initials: HD
authors.lastname: Fischer
authors.forename: Ximena
authors.initials: X
authors.lastname: Camacho
authors.forename: Paula A
authors.initials: PA
authors.lastname: Rochon
authors.forename: Geoffrey M
authors.initials: GM
authors.lastname: Anderson
mesh_terms: D000368:Aged; D000369:Aged, 80 and over; D000465:Algorithms; D015897:Comorbidity; D003266:Continuity of Patient Care / Q000458:organization & administration*; D004636:Emergency Service, Hospital / Q000706:statistics & numerical data; D017050:Episode of Care; D005260:Female; D017052:Hospital Mortality; D006801:Humans; D007902:Length of Stay / Q000706:statistics & numerical data*; D008297:Male; D008499:Medical Records / Q000706:statistics & numerical data; D008875:Middle Aged; D009864:Ontario / Q000453:epidemiology; D010351:Patient Discharge / Q000592:standards*; D010359:Patient Readmission / Q000592:standards / Q000706:statistics & numerical data; D018579:Patient Selection; D015203:Reproducibility of Results; D012307:Risk Factors; D013997:Time Factors