title: The HOSPITAL Score Predicts Potentially Preventable 30-Day Readmissions in Conditions Targeted by the Hospital Readmissions Reduction Program.
abstract: OBJECTIVE
  New tools to accurately identify potentially preventable 30-day readmissions are needed. The HOSPITAL score has been internationally validated for medical inpatients, but its performance in select conditions targeted by the Hospital Readmission Reduction Program (HRRP) is unknown.
  
  
  METHODS
  Retrospective cohort study.
  
  
  METHODS
  Six geographically diverse medical centers.
  All consecutive adult medical patients discharged alive in 2011 with 1 of the 4 medical conditions targeted by the HRRP (acute myocardial infarction, chronic obstructive pulmonary disease, pneumonia, and heart failure) were included. Potentially preventable 30-day readmissions were identified using the SQLape algorithm. The HOSPITAL score was calculated for all patients.
  
  
  METHODS
  A multivariable logistic regression model accounting for hospital effects was used to evaluate the accuracy (Brier score), discrimination (c-statistic), and calibration (Pearson goodness-of-fit) of the HOSPITAL score for each 4 medical conditions.
  
  
  RESULTS
  Among the 9181 patients included, the overall 30-day potentially preventable readmission rate was 13.6%. Across all 4 diagnoses, the HOSPITAL score had very good accuracy (Brier score of 0.11), good discrimination (c-statistic of 0.68), and excellent calibration (Hosmer-Lemeshow goodness-of-fit test, P=0.77). Within each diagnosis, performance was similar. In sensitivity analyses, performance was similar for all readmissions (not just potentially preventable) and when restricted to patients age 65 and above.
  
  
  CONCLUSIONS
  The HOSPITAL score identifies a high-risk cohort for potentially preventable readmissions in a variety of practice settings, including conditions targeted by the HRRP. It may be a valuable tool when included in interventions to reduce readmissions within or across these conditions.
authors.affiliation: *Hospital Medicine and Research Sections, Eastern Colorado Health Care System, DE †Department of Medicine, Division of General Internal Medicine, University of Colorado School of Medicine, Aurora, CO ‡BWH Hospitalist Service, Division of General Medicine, Brigham and Women's Hospital §Harvard Medical School, Boston, MA ∥Center for Health Services Research, University of Kentucky College of Medicine, Lexington, KY ¶Value Institute, Christiana Care Health System, Wilmington, DE #Section of Hospital Medicine, Division of General Internal Medicine and Public Health, Vanderbilt University **VA Tennessee Valley, Geriatric Research, Education and Clinical Center (GRECC) ††Center for Clinical Quality and Implementation Research, Vanderbilt University, Nashville, TN ‡‡Division of General Internal Medicine, Massachusetts General Hospital, Boston, MA §§Department of Medicine, Harborview Medical Center, University of Washington, Seattle, WA ∥∥Division of Hospital Medicine, University of California, San Francisco, CA ¶¶Division of General Internal Medicine, Bern University Hospital, Bern, Switzerland.
authors.forename: Robert E
authors.initials: RE
authors.lastname: Burke
authors.forename: Jeffrey L
authors.initials: JL
authors.lastname: Schnipper
authors.forename: Mark V
authors.initials: MV
authors.lastname: Williams
authors.forename: Edmondo J
authors.initials: EJ
authors.lastname: Robinson
authors.forename: Eduard E
authors.initials: EE
authors.lastname: Vasilevskis
authors.forename: Sunil
authors.initials: S
authors.lastname: Kripalani
authors.forename: Joshua P
authors.initials: JP
authors.lastname: Metlay
authors.forename: Grant S
authors.initials: GS
authors.lastname: Fletcher
authors.forename: Andrew D
authors.initials: AD
authors.lastname: Auerbach
authors.forename: Jacques D
authors.initials: JD
authors.lastname: Donzé
mesh_terms: D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D005260:Female; D006333:Heart Failure / Q000453:epidemiology; D006739:Hospital Administration / Q000706:statistics & numerical data*; D006801:Humans; D016015:Logistic Models; D008297:Male; D008875:Middle Aged; D008962:Models, Theoretical; D009203:Myocardial Infarction / Q000453:epidemiology; D010359:Patient Readmission / Q000706:statistics & numerical data*; D011014:Pneumonia / Q000453:epidemiology; D029424:Pulmonary Disease, Chronic Obstructive / Q000453:epidemiology; D012189:Retrospective Studies; D018570:Risk Assessment; D012307:Risk Factors; D013997:Time Factors