Advertisement

Combining Items From 3 Federally Mandated Assessments Using Rasch Measurement to Reliably Measure Cognition Across Postacute Care Settings

Published:August 01, 2020DOI:https://doi.org/10.1016/j.apmr.2020.07.003

      Abstract

      Objective

      To combine items from the Functional Independence Measure, Minimum Data Set (MDS) 2.0, and the Outcome and Assessment Information Set (OASIS)-B to reliably measure cognition across postacute care settings and facilitate future studies of patient cognitive recovery.

      Design

      Rasch analysis of data from a prospective, observational cohort study.

      Setting

      Postacute care inclusive of inpatient rehabilitation facilities, skilled nursing facilities, and home health agencies.

      Participants

      Patients (N=147) receiving rehabilitation services.

      Interventions

      Not applicable.

      Main Outcome Measures

      Functional Independence Measure, MDS 2.0, and the OASIS-B.

      Results

      Six cognition items demonstrated good construct validity with no misfitting items, unidimensionality, good precision (person separation reliability, 0.95), and an item hierarchy that reflected a clinically meaningful continuum of cognitive challenge.

      Conclusions

      This is the first attempt to combine the cognition items from the 3 historically, federally mandated assessments to create a common metric for cognition. These 6 items could be adopted as standardized patient assessment data elements to improve cognitive assessment across postacute care settings.

      Keywords

      List of abbreviations:

      FIM (Functional Independence Measure), HH (home health), IRF (inpatient rehabilitation facility), LTM (long-term memory), MDS (Minimum Data Set), OASIS (Outcome and Assessment Information Set), PAC (postacute care), SPADE (standardized patient assessment data elements), STM (short-term memory), SNF (skilled nursing facility)
      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 Archives of Physical Medicine and Rehabilitation
      Already a print subscriber? Claim online access
      Already an online subscriber? Sign in
      Institutional Access: Sign in to ScienceDirect

      References

        • Rundek T.
        • Mast H.
        • Hartmann A.
        • et al.
        Predictors of resource use after acute hospitalization: the Northern Manhattan Stroke Study.
        Neurology. 2000; 55: 1180-1187
        • Hurford R.
        • Charidimou A.
        • Fox Z.
        • Cipolotti L.
        • Werring D.
        Domain-specific trends in cognitive impairment after acute ischaemic stroke.
        J Neurol. 2013; 260: 237-241
        • McDonald M.
        • Black S.
        • Copland D.
        • et al.
        Cognition in stroke rehabilitation and recovery research: consensus-based core recommendations from the second Stroke Recovery and Rehabilitation Roundtable.
        Neurorehabil Neural Repair. 2019; 33: 943-950
        • Dombovy M.L.
        • Basford J.R.
        • Whisnant J.P.
        • Bergstralh E.J.
        Disability and use of rehabilitation services following stroke in Rochester, Minnesota, 1975-1979.
        Stroke. 1987; 18: 830-836
        • Coster W.J.
        • Haley S.M.
        • Ludlow L.H.
        • Andres P.L.
        • Ni P.S.
        Development of an applied cognition scale to measure rehabilitation outcomes.
        Arch phys med rehabil. 2004; 85: 2030-2035
        • Holthaus D.
        • Kramer A.
        • Gage B.
        • et al.
        Uniform patient assessment for post-acute care: final report.
        Aurora: University of Colorado at Denver and Health Sciences Center, 2006
        • Centers for Medicare & Medicaid (CMS)
        IMPACT Act of 2014 data standardization & cross setting measures.
        (Available at:)
      1. Centers for Medicare & Medicaid. Input on Standardized Patient Assessment Data Elements (SPADEs) received after November 27, 2018 stakeholder meeting. 2019.

      2. Centers for Medicare & Medicaid. National field test assessment protocol: admission. Baltimore, MD: Centers for Medicare & Medicaid Services. Available at: https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/Downloads/National-Field-Test-Assessment-Protocol_Admission.pdf. 2017, Accessed August 24, 2020.

      3. Centers for Medicare & Medicaid. National field test assessment protocol: non-communicative. Baltimore, MD: Centers for Medicare & Medicaid Services. Available at: https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/Downloads/National-Field-Test-Assessment-Protocol_Non-Communicative.pdf. 2017, Accessed August 24, 2020.

        • Masters G.N.
        Common-person equating with the Rasch model.
        Appl Psychol Meas. 1985; 9: 73-82
        • Mallinson T.
        • Deutsch A.
        • Bateman J.
        • et al.
        Comparison of discharge functional status after rehabilitation in skilled nursing, home health, and medical rehabilitation settings for patients after hip fracture repair.
        Arch Phys Med Rehabil. 2014; 95: 209-217
        • Mallinson T.R.
        • Bateman J.
        • Tseng H.Y.
        • et al.
        A comparison of discharge functional status after rehabilitation in skilled nursing, home health, and medical rehabilitation settings for patients after lower-extremity joint replacement surgery.
        Arch Phys Med Rehabil. 2011; 92: 712-720
        • von Elm E.
        • Altman D.G.
        • Egger M.
        • et al.
        The Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) statement: guidelines for reporting observational studies.
        Ann Intern Med. 2007; 147: 573-577
      4. Tennant A. Guidelines for reporting studies using Rasch analysis. J Rehabil Med. Available at: https://www.medicaljournals.se/jrm/guidelines-for-reporting-studies-using-rasch-analysis. Accessed August 24, 2020.

        • Tennant A.
        • Conaghan P.G.
        The Rasch measurement model in rheumatology: what is it and why use it? When should it be applied, and what should one look for in a Rasch paper?.
        Arthritis Rheum. 2007; 57: 1358
        • Ottenbacher K.J.
        • Hsu Y.
        • Granger C.V.
        • Fiedler R.C.
        The reliability of the functional independence measure: a quantitative review.
        Arch Phys Med Rehabil. 1996; 77: 1226-1232
        • 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 Rehabil. 1996; 77: 1101-1108
        • Dodds T.A.
        • Martin D.P.
        • Stolov W.C.
        • Deyo R.A.
        A validation of the Functional Independence Measurement and its performance among rehabilitation inpatients.
        Arch Phys Med Rehabil. 1993; 74: 531-536
        • Hawes C.
        • Morris J.N.
        • Phillips C.D.
        • Mor V.
        • Fries B.E.
        • Nonemaker S.
        Reliability estimates for the Minimum Data Set for nursing home resident assessment and care screening (MDS).
        Gerontologist. 1995; 35: 172-178
        • Lawton M.P.
        • Casten R.
        • Parmelee P.A.
        • Van Haitsma K.
        • Corn J.
        • Kleban M.H.
        Psychometric characteristics of the minimum data set II: validity.
        J Am Geriatr Soc. 1998; 46: 736-744
        • Velozo C.A.
        • Byers K.L.
        • Ying-Chih W.
        • Joseph B.R.
        Translating measures across the continuum of care: using Rasch analysis to create a crosswalk between the Functional Independence Measure and the Minimum Data Set.
        J Rehabil Res Dev. 2007; 44: 467-478
        • Wang Y.C.
        • Byers K.L.
        • Velozo C.A.
        Rasch analysis of Minimum Data Set mandated in skilled nursing facilities.
        J Rehabil Res Dev. 2008; 45: 1385
        • Fortinsky H.R.
        • Garcia I.R.
        • Joseph Sheehan A.T.
        • Madigan A.E.
        • Tullai-Mcguinness A.S.
        Measuring disability in Medicare home care patients: application of Rasch modeling to the outcome and assessment information set.
        Med Care. 2003; 41: 601-615
        • Abt Associates
        Deliverable 27: OASIS Field Test Summary Report.
        Centers for Medicare & Medicaid Services, Baltimore2018
        • Bond T.G.
        • Fox C.M.
        Applying the Rasch model fundamental measurement in the human sciences.
        3rd edition. Taylor & Francis, New York2015
        • Mallinson T.
        Rasch analysis of repeated measures.
        Rasch Meas Trans. 2011; : 1317
        • Linacre J.M.
        Sample size and item calibrations stability.
        Rasch Meas Trans. 1994; 7
        • Hula W.D.
        • Doyle P.J.
        • Austermann Hula S.N.
        Patient-reported cognitive and communicative functioning: 1 construct or 2?.
        Arch Phys Med Rehabil. 2010; 91: 400-406
        • Linacre J.M.
        A user's guide to WINSTEPS & MINISTEP Rasch-model computer programs. Program Manual 4.3.1.
        (Available at:) (Accessed May 10, 2019)
        • Linacre J.M.
        Table 23.1, 23.11, ...principal components/contrast plots of item loadings.
        (Available at:)
      5. Linacre JM. WINSTEPS help. Available at: https://www.winsteps.com/a/Winsteps-Manual.pdf. Accessed May 10, 2019.

        • Smith R.M.
        • Schumacker R.E.
        • Bush M.J.
        Using item mean squares to evaluate fit to the Rasch model.
        J Outcome Meas. 1998; 2: 66-78
        • Linacre J.M.
        Table 23. Variance components for items.
        (Available at:) (Accessed May 10, 2019)
        • Linacre J.M.
        Dimensionality investigation--an example.
        (Available at:)
        • Portney L.G.
        • Watkins M.P.
        Foundations of clinical research: application to practice.
        3rd ed. F.A. Davis Company, Philadelphia2015
        • Andresen E.M.
        Criteria for assessing the tools of disability outcomes research.
        Arch Phys Med Rehabil. 2000; 81: S15-20
        • Andresen E.M.
        • Rothenberg B.M.
        • Panzer R.
        • Katz P.
        • McDermott M.P.
        Selecting a generic measure of health-related quality of life for use among older adults: a comparison of candidate instruments.
        Eval Health Prof. 1998; 21: 244-264
        • Wright B.D.
        Separation, reliability and skewed distributions: statistically different sample-independent levels of performance.
        Rasch Meas Trans. 2001; 14
        • Baum C.M.
        • Wolf T.J.
        • Wong A.W.K.
        • et al.
        Validation and clinical utility of the executive function performance test in persons with traumatic brain injury.
        Neuropsychol Rehabil. 2017; 27: 603-617
        • Dawson D.R.
        • Anderson N.D.
        • Burgess P.
        • Cooper E.
        • Krpan K.M.
        • Stuss D.T.
        Further development of the Multiple Errands Test: standardized scoring, reliability, and ecological validity for the Baycrest version.
        Arch Phys Med Rehabil. 2009; 90: S41-51
        • Sohlberg M.
        • Mateer C.
        Introduction to cognitive rehabilitation.
        The Guilford Press, New York1989
        • Cowan N.
        What are the differences between long-term, short-term, and working memory?.
        Essence Mem. 2008; 169: 323-338
        • Levy L.L.
        • Burns T.
        The Cognitive Disabilities Reconsidered Model: rehabilitation of adults with dementia.
        in: Katz N. Cognition, occupation, and participation across the life span. 3rd ed. AOTA Press, Bethesda2011
        • Eichenbaum H.
        A cortical-hippocampal system for declarative memory.
        Nat Rev Neurosci. 2000; 1: 41-50
        • Centers for Medicare & Medicaid Services
        IRF-PAI training manual.
        Centers for Medicare & Medicaid Services, Baltimore2002
        • Velozo C.A.
        • Woodbury M.L.
        Translating measurement findings into rehabilitation practice: an example using Fugl-Meyer Assessment-Upper Extremity with patients following stroke.
        J Rehabil Res Dev. 2011; 48: 1211-1222
        • Woodbury M.L.
        • Velozo C.A.
        • Richards L.G.
        • Duncan P.W.
        Rasch analysis staging methodology to classify upper extremity movement impairment after stroke.
        Arch Phys Med Rehabil. 2013; 94: 1527-1533
        • Conti J.
        • Sterr A.
        • Brucki S.M.D.
        • Conforto A.B.
        Diversity of approaches in assessment of executive functions in stroke: limited evidence?.
        eNeurologicalSci. 2015; 1: 12-20
        • Alloway T.P.
        • Copello E.
        Working memory: the what, the why, and the how.
        Aust Educ Dev Psychol. 2013; 30: 105-118
        • van Schouwen-van Kranen E.T.
        Clinical reasoning in cognitive rehabilitation therapy.
        NeuroRehabilitation. 2014; 34: 15