Shirley L Shih1, Ross Zafonte1, David W Bates2, Paul Gerrard3, Richard Goldstein4, Jacqueline Mix5, Paulette Niewczyk6, S Ryan Greysen7, Lewis Kazis8, Colleen M Ryan9, Jeffrey C Schneider10. 1. 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. 2. 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. 3. New England Rehabilitation Hospital of Portland, Portland, ME. 4. Department of Physical Medicine and Rehabilitation, Spaulding Rehabilitation Hospital, Harvard Medical School, Boston, MA. 5. Uniform Data System for Medical Rehabilitation, Amherst, NY. 6. Uniform Data System for Medical Rehabilitation, Amherst, NY; Health Care Studies Department, Daemen College, Amherst, NY. 7. Division of Hospital Medicine, University of California, San Francisco, CA. 8. Department of Health Policy and Management, Boston University School of Public Health, Boston, MA. 9. Sumner Redstone Burn Center, Surgical Services, Massachusetts General Hospital, Boston, MA; Shriners Hospital for Children-Boston, Boston, MA. 10. 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.
Abstract
OBJECTIVES: 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. DESIGN: Retrospective database analysis between 2002 and 2011. SETTING: 1158 US inpatient rehabilitation facilities. PARTICIPANTS: 4,199,002 inpatient rehabilitation facility admissions comprising patients from 16 impairment groups within the Uniform Data System for Medical Rehabilitation database. MEASUREMENTS: 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. RESULTS: 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. CONCLUSION: 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.
OBJECTIVES: 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. DESIGN: Retrospective database analysis between 2002 and 2011. SETTING: 1158 US inpatient rehabilitation facilities. PARTICIPANTS: 4,199,002 inpatient rehabilitation facility admissions comprising patients from 16 impairment groups within the Uniform Data System for Medical Rehabilitation database. MEASUREMENTS: 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. RESULTS: 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. CONCLUSION: 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: Donna Huang; Chloe Slocum; Julie K Silver; James W Morgan; Richard Goldstein; Ross Zafonte; Jeffrey C Schneider Journal: J Spinal Cord Med Date: 2018-03-29 Impact factor: 1.985
Authors: Cristina A Shea; Razvan Turcu; Bonny S Wong; Michelle E Brassil; Chloe S Slocum; Richard Goldstein; Ross D Zafonte; Shirley L Shih; Jeffrey C Schneider Journal: J Am Med Dir Assoc Date: 2021-05-11 Impact factor: 4.669
Authors: Shirley L Shih; Marisa Flavin; Richard Goldstein; Chloe Slocum; Colleen M Ryan; Aneesh Singhal; Jason Frankel; Ross Zafonte; Jeffrey C Schneider Journal: Am J Phys Med Rehabil Date: 2020-01 Impact factor: 3.412