Advertisement

Development of a 30-Day Readmission Risk Calculator for the Inpatient Rehabilitation Setting

  • Author Footnotes
    † Co first authors.
    Tawnee L. Sparling
    Footnotes
    † Co first authors.
    Affiliations
    Department of Physical Medicine and Rehabilitation, Spaulding Rehabilitation Hospital, Harvard Medical School, Boston, MA, USA
    Search for articles by this author
  • Author Footnotes
    † Co first authors.
    Erika T. Yih
    Footnotes
    † Co first authors.
    Affiliations
    Department of Physical Medicine and Rehabilitation, Spaulding Rehabilitation Hospital, Harvard Medical School, Boston, MA, USA
    Search for articles by this author
  • Richard Goldstein
    Affiliations
    Department of Physical Medicine and Rehabilitation, Spaulding Rehabilitation Hospital, Harvard Medical School, Boston, MA, USA
    Search for articles by this author
  • Chloe S. Slocum
    Affiliations
    Department of Physical Medicine and Rehabilitation, Spaulding Rehabilitation Hospital, Harvard Medical School, Boston, MA, USA

    Department of Physical Medicine and Rehabilitation, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
    Search for articles by this author
  • Colleen M. Ryan
    Affiliations
    Surgical Services, Shriners Hospitals for Children, Boston, MA, USA

    Sumner Redstone Burn Center, Department of Surgery, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
    Search for articles by this author
  • Ross Zafonte
    Affiliations
    Department of Physical Medicine and Rehabilitation, Spaulding Rehabilitation Hospital, Harvard Medical School, Boston, MA, USA

    Department of Physical Medicine and Rehabilitation, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
    Search for articles by this author
  • Jeffrey C. Schneider
    Correspondence
    Address correspondence to Jeffrey C. Schneider, MD, Department of Physical Medicine and Rehabilitation, Spaulding Rehabilitation Hospital, Harvard Medical School 300 1st Ave, Charlestown, MA 02129 USA.
    Affiliations
    Department of Physical Medicine and Rehabilitation, Spaulding Rehabilitation Hospital, Harvard Medical School, Boston, MA, USA

    Department of Physical Medicine and Rehabilitation, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
    Search for articles by this author
  • Author Footnotes
    † Co first authors.
Published:September 20, 2022DOI:https://doi.org/10.1016/j.jamda.2022.08.005

      Abstract

      Objectives

      Readmission to acute care from the inpatient rehabilitation facility (IRF) setting is potentially preventable and an important target of quality improvement and cost savings. The objective of this study was to develop a risk calculator to predict 30-day all-cause readmissions from the IRF setting.

      Design

      Retrospective database analysis using the Uniform Data System for Medical Rehabilitation (UDSMR) from 2015 through 2019.

      Setting and Participants

      In total, 956 US inpatient rehabilitation facilities and 1,849,768 IRF discharges comprising patients from 14 impairment groups.

      Methods

      Logistic regression models were developed to calculate risk-standardized 30-day all-cause hospital readmission rates for patients admitted to an IRF. Models for each impairment group were assessed using 12 common clinical and demographic variables and all but 4 models included various special variables. Models were assessed for discrimination (c-statistics), calibration (calibration plots), and internal validation (bootstrapping). A readmission risk scoring system was created for each impairment group population and was graphically validated.

      Results

      The mean age of the cohort was 68.7 (15.2) years, 50.7% were women, and 78.3% were Caucasian. Medicare was the primary payer for 73.1% of the study population. The final models for each impairment group included between 4 and 13 total predictor variables. Model c-statistics ranged from 0.65 to 0.70. There was good calibration represented for most models up to a readmission risk of 30%. Internal validation of the models using bootstrap samples revealed little bias. Point systems for determining risk of 30-day readmission were developed for each impairment group.

      Conclusions and Implications

      Multivariable risk factor algorithms based upon administrative data were developed to assess 30-day readmission risk for patients admitted from IRF. This report represents the development of a readmission risk calculator for the IRF setting, which could be instrumental in identifying high risk populations for readmission and targeting resources towards a diverse group of IRF impairment groups.

      Keywords

      To read this article in full you will need to make a payment

      Purchase one-time access:

      Academic & Personal: 24 hour online accessCorporate R&D Professionals: 24 hour online access
      One-time access price info
      • For academic or personal research use, select 'Academic and Personal'
      • For corporate R&D use, select 'Corporate R&D Professionals'

      Subscribe:

      Subscribe to Journal of the American Medical Directors Association
      Already a print subscriber? Claim online access
      Already an online subscriber? Sign in
      Institutional Access: Sign in to ScienceDirect

      References

        • Centers for Medicare and Medicaid Services
        Readmission Reductions Program.
        • Rau J.
        New round of Medicare readmission penalties hits 2,583 hospitals.
        • Singh S.
        • Lin Y.L.
        • Kuo Y.F.
        • et al.
        Variation in the risk of readmission among hospitals: the relative contribution of patient, hospital and inpatient provider characteristics.
        J Gen Intern Med. 2013; 29: 572-578
        • Barnett M.L.
        • Hsu J.
        • McWilliams M.
        Patient characteristics and differences in hospital readmission rates.
        JAMA Intern Med. 2015; 175: 1803-1812
        • Huckfeldt P.J.
        • Mehrotra A.
        • Hussey P.S.
        The relative importance of post-acute care and readmissions for post-discharge spending.
        Health Serv Res. 2016; 51: 1919-1938
        • Shih S.L.
        • Zafonte R.
        • Bates D.W.
        • et al.
        Functional status outperforms comorbidities as a predictor of 30-day acute care readmissions in the inpatient rehabilitation population.
        J Am Med Dir Assoc. 2016; 17: 921-926
        • The Lewin Group
        Trends in profile of short term acute care hospitals discharged to post-acute care settings: final report.
        • The Medicare Payment Advisory Commission
        Health care spending and the Medicare program.
        • UB Foundation Activities
        The inpatient rehabilitation facility-patient assessment instrument (IRF-PAI) training manual.
        • Stineman M.G.
        • Shea J.A.
        • Jette A.
        • et al.
        The functional independence measure: tests of scaling assumptions, structure, and reliability across 20 diverse impairment categories.
        Arch Phys Med Rehab. 1996; 77: 1101-1108
        • Ramey L.
        • Goldstein R.
        • Zafonte R.
        • et al.
        Variation in 30-day readmission rates among medically complex patients at inpatient rehabilitation facilities and contributing factors.
        J Am Med Dir Assoc. 2016; 17: 730-736
        • Schneider J.C.
        • Simko L.C.
        • Goldstein R.
        • et al.
        Predicting heterotopic ossification early after burn injuries.
        Ann Surg. 2017; 266: 179-184
        • Newgard C.D.
        • Lewis R.J.
        Missing data: how to best account for what is not known.
        JAMA. 2015; 314: 940-941
        • Sterne J.
        • White I.
        • Carlin J.
        • et al.
        Multiple imputation for missing data in epidemiological and clinical research: potential and pitfalls.
        Brit Med J. 2009; 338: b2393
        • Steyerberg E.
        • Vickers A.
        • Cook N.
        • et al.
        Assessing the performance of prediction models: a framework for some traditional and novel measures.
        Epidemiology. 2010; 21: 128-138
        • Grunkemeier G.L.
        • Wu Y.
        Bootstrap resampling methods: something for nothing?.
        Ann Thorac Surg. 2004; 77: 1142-1144
        • Schomaker M.
        • Heumann C.
        Bootstrap inference when using multiple imputation.
        Stat Med. 2018; 37: 2252-2266
        • Sullivan L.M.
        • Massaro J.M.
        • D'Agostino R.B.
        Presentation of multivariate data for clinical use: the Framingham Study risk score functions.
        Stat Med. 2004; 23: 1631-1660
        • Middleton A.
        • Graham J.E.
        • Deutch A.
        • et al.
        Potentially preventable within-stay readmissions among Medicare fee-for-service beneficiaries receiving inpatient rehabilitation.
        PM&R. 2017; 9: 1095-1105
        • Slocum C.
        • Gerrard P.
        • Black-Schaffer R.
        • et al.
        Functional status predicts acute care readmissions from inpatient rehabilitation in the stroke population.
        PloS One. 2015; 10: e0142180
        • Ko D.T.
        • Sivaswamy A.
        • Sud M.
        • et al.
        Calibration and discrimination of the Framingham risk score and the pooled cohort equations.
        CMAJ. 2020; 192: E442-E449
        • Pencina M.J.
        • D'Agostino Sr R.B.
        • Larson M.B.
        • et al.
        Predicting the 30-year risk of cardiovascular disease: the Framingham Heart Study.
        Circulation. 2009; 119: 3078-3084
        • Parmar P.
        • Krishnamurthi R.
        • Ikram M.A.
        • et al.
        The Stroke RiskometerTM App: validation of a data collection tool and stroke risk predictor.
        Int J Stroke. 2015; 10: 231-244
        • Verma A.
        • Towfighi A.
        • Brown A.
        • et al.
        Moving towards equity with digital health innovations for stroke care.
        Stroke. 2022; 29: 689-697
        • Schneider J.C.
        • Gerrard P.
        • Goldstein R.
        • et al.
        Predictors of transfer from rehabilitation to acute care in burn injuries.
        J Trauma Acute Care Surg. 2012; 73: 1596-1601