title: Functional Status Predicts Acute Care Readmissions from Inpatient Rehabilitation in the Stroke Population.
abstract: OBJECTIVE
  Acute care readmission risk is an increasingly recognized problem that has garnered significant attention, yet the reasons for acute care readmission in the inpatient rehabilitation population are complex and likely multifactorial. Information on both medical comorbidities and functional status is routinely collected for stroke patients participating in inpatient rehabilitation. We sought to determine whether functional status is a more robust predictor of acute care readmissions in the inpatient rehabilitation stroke population compared with medical comorbidities using a large, administrative data set.
  
  
  METHODS
  A retrospective analysis of data from the Uniform Data System for Medical Rehabilitation from the years 2002 to 2011 was performed examining stroke patients admitted to inpatient rehabilitation facilities. A Basic Model for predicting acute care readmission risk based on age and functional status was compared with models incorporating functional status and medical comorbidities (Basic-Plus) or models including age and medical comorbidities alone (Age-Comorbidity). C-statistics were compared to evaluate model performance.
  
  
  RESULTS
  There were a total of 803,124 patients: 88,187 (11%) patients were transferred back to an acute hospital: 22,247 (2.8%) within 3 days, 43,481 (5.4%) within 7 days, and 85,431 (10.6%) within 30 days. The C-statistics for the Basic Model were 0.701, 0.672, and 0.682 at days 3, 7, and 30 respectively. As compared to the Basic Model, the best-performing Basic-Plus model was the Basic+Elixhauser model with C-statistics differences of +0.011, +0.011, and + 0.012, and the best-performing Age-Comorbidity model was the Age+Elixhauser model with C-statistic differences of -0.124, -0.098, and -0.098 at days 3, 7, and 30 respectively.
  
  
  CONCLUSIONS
  Readmission models for the inpatient rehabilitation stroke population based on functional status and age showed better predictive ability than models based on medical comorbidities.
authors.affiliation: Department of Physical Medicine and Rehabilitation, Spaulding Rehabilitation Hospital, Harvard Medical School, Boston, Massachusetts, United States of America.|Department of Physical Medicine and Rehabilitation, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, United States of America.
authors.forename: Chloe
authors.initials: C
authors.lastname: Slocum
authors.affiliation: Department of Physical Medicine and Rehabilitation, Spaulding Rehabilitation Hospital, Harvard Medical School, Boston, Massachusetts, United States of America.|Department of Physical Medicine and Rehabilitation, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, United States of America.
authors.forename: Paul
authors.initials: P
authors.lastname: Gerrard
authors.affiliation: Department of Physical Medicine and Rehabilitation, Spaulding Rehabilitation Hospital, Harvard Medical School, Boston, Massachusetts, United States of America.|Department of Physical Medicine and Rehabilitation, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, United States of America.
authors.forename: Randie
authors.initials: R
authors.lastname: Black-Schaffer
authors.affiliation: Department of Physical Medicine and Rehabilitation, Spaulding Rehabilitation Hospital, Harvard Medical School, Boston, Massachusetts, United States of America.
authors.forename: Richard
authors.initials: R
authors.lastname: Goldstein
authors.affiliation: Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, United States of America.
authors.forename: Aneesh
authors.initials: A
authors.lastname: Singhal
authors.affiliation: Uniform Data System for Medical Rehabilitation, Amherst, New York, United States of America.
authors.forename: Margaret A
authors.initials: MA
authors.lastname: DiVita
authors.affiliation: Sumner Redstone Burn Center, Surgical Services, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, United States of America.|Shriners Hospitals for Children®-Boston, Boston, Massachusetts, United States of America.
authors.forename: Colleen M
authors.initials: CM
authors.lastname: Ryan
authors.affiliation: Uniform Data System for Medical Rehabilitation, Amherst, New York, United States of America.
authors.forename: Jacqueline
authors.initials: J
authors.lastname: Mix
authors.affiliation: Neurorehabilitation and Traumatic Brain Injury, National Intrepid Center of Excellence: Intrepid Spirit One, Fort Belvoir Community Hospital, Fort Belvoir, Virginia, United States of America.
authors.forename: Maulik
authors.initials: M
authors.lastname: Purohit
authors.affiliation: Uniform Data System for Medical Rehabilitation, Amherst, New York, United States of America.|Daemen College, Health Care Studies Dept., Amherst, New York, United States of America.
authors.forename: Paulette
authors.initials: P
authors.lastname: Niewczyk
authors.affiliation: Department of Health Policy and Management, Boston University School of Public Health, Boston, Massachusetts, United States of America.
authors.forename: Lewis
authors.initials: L
authors.lastname: Kazis
authors.affiliation: Department of Physical Medicine and Rehabilitation, Spaulding Rehabilitation Hospital, Harvard Medical School, Boston, Massachusetts, United States of America.|Department of Physical Medicine and Rehabilitation, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, United States of America.
authors.forename: Ross
authors.initials: R
authors.lastname: Zafonte
authors.affiliation: Department of Physical Medicine and Rehabilitation, Spaulding Rehabilitation Hospital, Harvard Medical School, Boston, Massachusetts, United States of America.|Department of Physical Medicine and Rehabilitation, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, United States of America.
authors.forename: Jeffrey C
authors.initials: JC
authors.lastname: Schneider
mesh_terms: D000367:Age Factors; D000368:Aged; D002138:Calibration; D015897:Comorbidity; D003430:Cross-Sectional Studies; D005260:Female; D006304:Health Status; D006760:Hospitalization; D006761:Hospitals; D006801:Humans; D007297:Inpatients; D008297:Male; D006278:Medicare; D008875:Middle Aged; D008962:Models, Theoretical; D010351:Patient Discharge; D010359:Patient Readmission; D012044:Regression Analysis; D012047:Rehabilitation Centers; D012189:Retrospective Studies; D000071939:Stroke Rehabilitation; D014481:United States