11 Jun Functional Status Is Important Predictor of Hospital Readmission
MedicalResearch.com Interview with:
Jeffrey C. Schneider, M.D.
Medical Director, Trauma, Burn & Orthopedic Program
Assistant Professor, Dept. of Physical Medicine and Rehabilitation
Harvard Medical School
Spaulding Rehabilitation Hospital
Boston, MA 02129
Medical Research: What is the background for this study? What are the main findings?
Response: Hospitalizations account for the largest share of healthcare costs in the U.S., comprising nearly one-third of all healthcare expenditures. In 2011, readmissions within 30 days of hospital discharge represented more than $41 billion in hospital costs. Financial penalties for excess 30-day hospital readmissions were instituted by the Centers for Medicare and Medicaid Services in 20124; more than 2,200 hospitals were fined a total of $280 million in reduced Medicare payments in fiscal year 2013.
Most readmission risk prediction models have targeted specific medical diagnoses and have utilized comorbidities and demographic data as the central risk factors for hospital readmission. Yet, large U.S. administrative datasets have demonstrated poor discriminative ability (c-statistics: 0.55-0.65) in predicting readmissions. However, few studies have considered functional status as potential readmission risk factors.
There is increasing evidence that functional status is a good predictor of other health outcomes. To date, acute care hospital administrative databases do not routinely include functional status measures. Therefore, inpatient rehabilitation setting is an ideal population in which to examine the impact of functional status on readmission risk, because:
(1) inpatient rehabilitation patients often have complex care transitions after acute care discharge, and represent a significant proportion of total readmissions;
2) inpatient rehabilitation facilities routinely document functional status using a valid instrument—the FIM®; and
(3) a majority of U.S. IRFs participate in one of the only national datasets that contain standardized functional data—the Uniform Data System for Medical Rehabilitation.
Limitations of prior work include small and single-center study designs, narrowly defined patient populations, and defining readmissions beyond the 30-day period. Overall, there is a lack of literature on the utility of function as a readmission predictor in a large population of medical patients. Moreover, function is a modifiable risk factor with potential to impact readmission outcomes if function-based interventions are instituted early. Therefore, the objective of this study was to compare functional status with medical comorbidities as predictors of acute care readmissions in the medically complex rehabilitation population. We hypothesized that acute care readmission prediction models based on functional status would outperform models based on comorbidities,and that the addition of comorbidity variables to function-based models would not significantly enhance predictive performance.
To test our hypothesis we performed a retrospective database study of 120,957 patients from 1041 facilities in the Uniform Data System for Medical Rehabilitation admitted to inpatient rehabilitation facilities under the medically complex impairment group code between 2002 and 2011. In our analysis, a Basic Model based on gender and functional status was developed using logistic regression to predict the odds of 3-, 7-, and 30-day readmission from inpatient rehabilitation facilities to acute care hospitals.
Functional status was measured by the FIM® motor score. The Basic Model was compared to six other predictive models—three Basic Plus Models that added a comorbidity measure to the Basic Model and three Gender-Comorbidity Models that included only gender and a comorbidity measure. The three comorbidity measures used were the Elixhauser index, Deyo-Charlson index, and Medicare comorbidity tier system. The c-statistic was the primary measure of model performance. Basic Model c-statistics predicting 3-, 7-, and 30-day readmissions were 0.69, 0.64, and 0.65, respectively.
The best-performing Basic Plus Model (Basic+Elixhauser) c-statistics were only 0.02 better than the Basic Model (0.70, 0.65, 0.66) and the best-performing Gender-Comorbidity Model (Gender+Elixhauser) c-statistics were more than 0.07 worse than the Basic Model (0.57, 0.57, 0.57).
Medical Research: What should clinicians and patients take away from your report?
Response: Functional status is a frequently overlooked risk factor for readmissions and is a more valuable predictor of readmission risk than medical comorbidities in the medically complex inpatient rehabilitation population. Our results add to the growing body of evidence that functional status is an important predictor of readmissions. There is opportunity to improve current national readmission risk models to more accurately predict readmissions and more fairly reimburse hospitals based on performance.
Medical Research: What recommendations do you have for future research as a result of this study?
Response: Early mobilization is an area that has been studied recently and shown to improve clinical outcomes and reduce healthcare costs. Acute care hospitals do not routinely collect functional status information. Future efforts are needed to explore early clinical assessment and treatment of functional impairments to reduce hospital readmissions. Additionally, studies are needed in other populations to further assess the robustness of this study’s findings.
J Gen Intern Med. 2015 May 9. [Epub ahead of print]
Functional Status Outperforms Comorbidities in Predicting Acute Care Readmissions in Medically Complex Patients.
Shih SL1, Gerrard P, Goldstein R, Mix J, Ryan CM, Niewczyk P, Kazis L, Hefner J, Ackerly DC, Zafonte R, Schneider JC.
Jeffrey C. Schneider, M.D., Medical Director, Trauma, Burn & Orthopedic Program, Assistant Professor, Dept. of Physical Medicine and Rehabilitation, Harvard Medical School, Spaulding Rehabilitation Hospital, & Boston, MA 02129 (2015). Functional Status Is Important Predictor of Hospital Readmission