title: Functional Status Outperforms Comorbidities as a Predictor of 30-Day Acute Care Readmissions in the Inpatient Rehabilitation Population.
abstract: Functional status is associated with patient outcomes, but is rarely included in hospital readmission risk models. The objective of this study was to determine whether functional status is a better predictor of 30-day acute care readmission than traditionally investigated variables including demographics and comorbidities.
  
  
  
  Retrospective database analysis between 2002 and 2011.
  
  
  
  1158 US inpatient rehabilitation facilities.
  
  
  
  4,199,002 inpatient rehabilitation facility admissions comprising patients from 16 impairment groups within the Uniform Data System for Medical Rehabilitation database.
  
  
  
  Logistic regression models predicting 30-day readmission were developed based on age, gender, comorbidities (Elixhauser comorbidity index, Deyo-Charlson comorbidity index, and Medicare comorbidity tier system), and functional status [Functional Independence Measure (FIM)]. We hypothesized that (1) function-based models would outperform demographic- and comorbidity-based models and (2) the addition of demographic and comorbidity data would not significantly enhance function-based models. For each impairment group, Function Only Models were compared against Demographic-Comorbidity Models and Function Plus Models (Function-Demographic-Comorbidity Models). The primary outcome was 30-day readmission, and the primary measure of model performance was the c-statistic.
  
  
  
  All-cause 30-day readmission rate from inpatient rehabilitation facilities to acute care hospitals was 9.87%. C-statistics for the Function Only Models were 0.64 to 0.70. For all 16 impairment groups, the Function Only Model demonstrated better c-statistics than the Demographic-Comorbidity Models (c-statistic difference: 0.03-0.12). The best-performing Function Plus Models exhibited negligible improvements in model performance compared to Function Only Models, with c-statistic improvements of only 0.01 to 0.05.
  
  
  
  Readmissions are currently used as a marker of hospital performance, with recent financial penalties to hospitals for excessive readmissions. Function-based readmission models outperform models based only on demographics and comorbidities. Readmission risk models would benefit from the inclusion of functional status as a primary predictor.
authors.affiliation: Department of Physical Medicine and Rehabilitation, Spaulding Rehabilitation Hospital, Harvard Medical School, Boston, MA; Department of Physical Medicine and Rehabilitation, Massachusetts General Hospital, Harvard Medical School, Boston, MA.
authors.forename: Shirley L
authors.initials: SL
authors.lastname: Shih
authors.affiliation: Department of Physical Medicine and Rehabilitation, Spaulding Rehabilitation Hospital, Harvard Medical School, Boston, MA; Department of Physical Medicine and Rehabilitation, Massachusetts General Hospital, Harvard Medical School, Boston, MA.
authors.forename: Ross
authors.initials: R
authors.lastname: Zafonte
authors.affiliation: Division of General Internal Medicine and Primary Care, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA; Harvard School of Public Health, Boston, MA.
authors.forename: David W
authors.initials: DW
authors.lastname: Bates
authors.affiliation: New England Rehabilitation Hospital of Portland, Portland, ME.
authors.forename: Paul
authors.initials: P
authors.lastname: Gerrard
authors.affiliation: Department of Physical Medicine and Rehabilitation, Spaulding Rehabilitation Hospital, Harvard Medical School, Boston, MA.
authors.forename: Richard
authors.initials: R
authors.lastname: Goldstein
authors.affiliation: Uniform Data System for Medical Rehabilitation, Amherst, NY.
authors.forename: Jacqueline
authors.initials: J
authors.lastname: Mix
authors.affiliation: Uniform Data System for Medical Rehabilitation, Amherst, NY; Health Care Studies Department, Daemen College, Amherst, NY.
authors.forename: Paulette
authors.initials: P
authors.lastname: Niewczyk
authors.affiliation: Division of Hospital Medicine, University of California, San Francisco, CA.
authors.forename: S Ryan
authors.initials: SR
authors.lastname: Greysen
authors.affiliation: Department of Health Policy and Management, Boston University School of Public Health, Boston, MA.
authors.forename: Lewis
authors.initials: L
authors.lastname: Kazis
authors.affiliation: Sumner Redstone Burn Center, Surgical Services, Massachusetts General Hospital, Boston, MA; Shriners Hospital for Children-Boston, Boston, MA.
authors.forename: Colleen M
authors.initials: CM
authors.lastname: Ryan
authors.affiliation: Department of Physical Medicine and Rehabilitation, Spaulding Rehabilitation Hospital, Harvard Medical School, Boston, MA; Department of Physical Medicine and Rehabilitation, Massachusetts General Hospital, Harvard Medical School, Boston, MA. Electronic address: jcschneider@partners.org.
authors.forename: Jeffrey C
authors.initials: JC
authors.lastname: Schneider
mesh_terms: D000368:Aged; D000369:Aged, 80 and over; D015897:Comorbidity; D003422:Critical Care; D016208:Databases, Factual; D005260:Female; D005544:Forecasting; D016330:Frail Elderly; D006801:Humans; D016015:Logistic Models; D008297:Male; D008875:Middle Aged; D010359:Patient Readmission / Q000639:trends*; D012189:Retrospective Studies
keywords: Readmission; functional status; patient outcomes; rehabilitation