The Extended Barthel Index (EBI) can Be Reported as a Unidimensional Interval-Scaled Metric – A Psychometric Study

Roxanne Maritz; Alan Tennant; Carolina Saskia Fellinghauer; Gerold Stucki; Birgit Prodinger; on behalf of the NRP74 StARS clinics

doi:10.1055/a-0820-4642

Physikalische Medizin, Rehabilitationsmedizin, Kurortmedizin, Table of Contents

CC BY-NC-ND 4.0 · Physikalische Medizin, Rehabilitationsmedizin, Kurortmedizin 2019; 29(04): 224-232
DOI: 10.1055/a-0820-4642

Original Article

The Extended Barthel Index (EBI) can Be Reported as a Unidimensional Interval-Scaled Metric – A Psychometric Study

Der Erweiterte Barthel Index (EBI) kann als eindimensionale intervallskalierte Metrik berichtet werden – eine psychometrische Studie

Authors

Roxanne Maritz

¹Swiss Paraplegic Research, Nottwil, Switzerland

²Department of Health Sciences and Health Policy, University of Lucerne, Lucerne, Switzerland
Alan Tennant

¹Swiss Paraplegic Research, Nottwil, Switzerland

²Department of Health Sciences and Health Policy, University of Lucerne, Lucerne, Switzerland
Carolina Saskia Fellinghauer

¹Swiss Paraplegic Research, Nottwil, Switzerland
Gerold Stucki

¹Swiss Paraplegic Research, Nottwil, Switzerland

²Department of Health Sciences and Health Policy, University of Lucerne, Lucerne, Switzerland
Birgit Prodinger

¹Swiss Paraplegic Research, Nottwil, Switzerland

³Faculty of Applied Health and Social Sciences, Rosenheim,Technische Hochschule Rosenheim, Germany

on behalf of the NRP74 StARS clinics

Abstract

Background The Extended Barthel Index (EBI), consisting of the original Barthel Index plus 6 cognitive items, provides a tool to monitor patients’ outcomes in rehabilitation. Whether the EBI provides a unidimensional metric, thus can be reported as a valid sum-score, remains to be examined.

Objective To examine whether the EBI can be reported as unidimensional interval-scaled metric for neurological and musculoskeletal rehabilitation.

Methods Rasch analysis of a calibration sample of 800 cases from neurological or musculoskeletal rehabilitation in 2016 in Switzerland.

Results In the baseline analysis no fit to the Rasch Model was achieved. When accommodating local dependencies with a testlet approach satisfactory fit to the Rasch Model was achieved, and an interval scale transformation table was created.

Conclusion The results support the reporting of adapted EBI total scores for both rehabilitation groups by applying the interval scaled transformation table presented in this study.

Zusammenfassung

Hintergrund Der Erweiterte Barthel Index (EBI), der den Barthel Index um 6 kognitive Items ergänzt, ist ein Assessmentinstrument für die Rehabilitation. Ob der EBI eine eindimensionale Metrik liefert und somit als valider Gesamtscore berichtet werden kann, ist unklar.

Ziel Untersuchung ob der EBI für die neurologische und muskuloskelettale Rehabilitation als eindimensionale intervallskalierte Metrik berichtet werden kann.

Methode Rasch-Analyse einer Stichprobe von 800 neurologischen und muskuloskelettalen Rehapatienten aus der Schweiz.

Ergebnisse In der Basisanalyse wurde keine Übereinstimmung mit den Annahmen des Rasch-Modells erreicht. Nachdem lokale Item-Abhängigkeiten mit 2 Testlets angepasst wurden, wurde die Übereinstimmung erreicht und eine intervallskalierte Transformationstabelle erstellt.

Konklusion Die Ergebnisse unterstützen die Verwendung eines angepassten EBI Gesamtscores für beide Rehabilitationsgruppen unter Anwendung der intervallskalierten Transformationstabelle.

Key words

activities of daily living - assessment instruments - outcome assessment - rehabilitation - Rasch model - quality management

Schlüsselwörter

Alltagsaktivität - Ergebnismessung - Qualitätsmanagement - Rehabilitation - Rasch Modell - Assessmentinstrumente

Full Text

References

References
1 Stucki G, Bickenbach J. Functioning: the third health indicator in the health system and the key indicator for rehabilitation. European journal of physical and rehabilitation medicine 2017; 53: 134-138
2 Nelles G. Neurologische Rehabilitation. Stuttgart: Georg Thieme Verlag; 2004
3 Stucki G, Prodinger B, Bickenbach J. Four steps to follow when documenting functioning with the International Classification of Functioning, Disability and Health. European journal of physical and rehabilitation medicine 2017; 53: 144-149
4 Doganay Erdogan B, Leung YY, Pohl C. et al. Minimal clinically important difference as applied in rheumatology: An OMERACT rasch working group systematic review and critique. The Journal of rheumatology 2016; 43: 194-202
5 Gorter R, Fox JP, Twisk JW. Why item response theory should be used for longitudinal questionnaire data analysis in medical research. BMC Med Res Methodol 2015; 15: 55
6 Grimby G, Tennant A, Tesio L. The use of raw scores from ordinal scales: Time to end malpractice?. Journal of rehabilitation medicine 2012; 44: 97-98
7 Andrich D. Rating scales and Rasch measurement. Expert review of pharmacoeconomics & outcomes research 2011; 11: 571-585
8 Christensen KB, Kreiner S, Mesbah M. Rasch Models in Health. London / Hoboken: ISTE Ltd / John Wiley & Sons, Inc.; 2013
9 Prosiegel M, Böttger S, Schenk T. et al. The Extended Barthel Index – a new scale for the assessment of diability in neurological patients [German]. Neurologie & Rehabilitation 1996; 7-13
10 Deutsches Institut für Medizinische Dokumentation und Information DIMDI https://www.dimdi.de/static/de/klassifikationen/icd/icd-10-gm/kode-suche/htmlgm2018/zusatz-06-barthelindex.htm Accessed [2018 Nov 14]
11 ANQ Nationaler Verein für Qualitätsentwicklung in Spitälern und Kliniken, Bern https://www.anq.ch/wp-content/uploads/2017/12/ANQ_Module_23_Auswertungskonzept.pdf Accessed [2018 June 22]
12 Federal Statistical Office. [ https://www.bfs.admin.ch/bfs/de/home/statistiken/kataloge-datenbanken/publikationen.assetdetail.1940914.html ] Accessed [2018 June 22]
13 Swiss DRG https://www.swissdrg.org/application/files/9315/0997/9991/ST_Reha_Format_und_Inhalt_Daten_2018.pdf Accessed [2018 June 22]
14 Mahoney FI, Barthel DW. Functional Evaluation: The Barthel Index. Maryland state medical journal 1965; 14: 61-65
15 Grill E, Stucki G, Scheuringer M. et al. Validation of International Classification of Functioning, Disability, and Health (ICF) Core Sets for early postacute rehabilitation facilities: comparisons with three other functional measures. American journal of physical medicine & rehabilitation 2006; 85: 640-649
16 Haigh R, Tennant A, Biering-Sorensen F. et al. The use of outcome measures in physical medicine and rehabilitation within Europe. Journal of rehabilitation medicine 2001; 33: 273-278
17 Houlden H, Edwards M, McNeil J. et al. Use of the Barthel Index and the Functional Independence Measure during early inpatient rehabilitation after single incident brain injury. Clin Rehabil 2006; 20: 153-159
18 Kwon S, Hartzema AG, Duncan PW. et al. Disability measures in stroke: relationship among the Barthel Index, the Functional Independence Measure, and the Modified Rankin Scale. Stroke 2004; 35: 918-923
19 Marolf MV, Vaney C, Konig N. et al. Evaluation of disability in multiple sclerosis patients: a comparative study of the Functional Independence Measure, the Extended Barthel Index and the Expanded Disability Status Scale. Clin Rehabil 1996; 10: 309-313
20 Jansa J, Pogacnik T, Gompertz P. An evaluation of the Extended Barthel Index with acute ischemic stroke patients. Neurorehabilitation & Neural Repair 2004; 18: 37-41
21 Bath PM, Iddenden R, Bath FJ. et al. Tirilazad for acute ischaemic stroke. The Cochrane database of systematic reviews 2001; CD002087
22 Jörger M, Beer S, Kesselring J. Impact of neurorehabilitation on disability in patients with acutely and chronically disabling diseases of the nervous system measured by the Extended Barthel Index. Neurorehabilitation & Neural Repair 2001; 15: 15-22
23 Schuster-Amft C, Henneke A, Hartog-Keisker B. et al. Intensive virtual reality-based training for upper limb motor function in chronic stroke: a feasibility study using a single case experimental design and fMRI. Disability & Rehabilitation: Assistive Technology 2015; 10: 385-392
24 Adroher ND, Prodinger B, Fellinghauer CS. et al. All metrics are equal, but some metrics are more equal than others: A systematic search and review on the use of the term ‘metric’. PloS one 2018; 13: e0193861
25 Smith AB, Rush R, Fallowfield LJ. et al. Rasch fit statistics and sample size considerations for polytomous data. BMC Medical Research Methodology 2008; 8: 33
26 R Core Team. R Foundation for Statistical Computing. Vienna: https://www.R-project.org/ Accessed [2018 Nov 14]
27 Linacre JM. Archives of the Rasch Measurement SIG. https://www.rasch.org/rmt/rmt74m.htm Accessed [2018 Nov 14]
28 Hagell P, Westergren A. Sample Size and Statistical Conclusions from Test of Fit to the Rasch Model Accodring to the Rasch Unidimensional Measurement Model (RUMM) Program in Health Outcome Measurement. Journal of Applied Measurement 2016; 17: 416-431
29 Mallinson T. Rasch Analysis of Repeated Measures. Rasch Measurement Transactions 2011; 251: 1317
30 Rasch G. Probabilistic models for some intelligence and attainment tests. The University of Chicago Press; Chicago: 1980
31 Andrich D, Sheridan B, Luo G. Rumm Laboratory Pty Ltd. http://www.rummlab.com.au/ Accessed [2018 Nov 14]
32 Masters GN. A Rasch model for partial credit scoring. Psychometrika 1982; 47: 149–174
33 Tennant A, Conaghan PG. 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 and rheumatism 2007; 57: 1358-1362
34 Andrich D. Components of Variance of Scales With a Bifactor Subscale Structure From Two Calculations of alpha. Educational Measurement: Issues and Practice 2016; 35: 25-30
35 Wainer H, Kiely G. Item clusters and computer adaptive testing: A case for testlets. Journal of Educational Measurement 1987; 185-202
36 Wilson M. Detecting and interpreting local item dependence using a family of Rasch models. Applied psychological measurement 1988; 353-364
37 Tuerlinckx F, De Boeck P. The effect of ignoring item interactions on the estimated discrimination parameters in item response theory. Psychological Methods 2001; 6: 181-195
38 Lundgren Nilsson A, Tennant A. Past and present issues in Rasch analysis: the functional independence measure (FIM) revisited. Journal of rehabilitation medicine 2011; 43: 884-891
39 Christensen KB, Makransky G, Horton M. Critical Values for Yen's Q3: Identification of Local Dependence in the Rasch Model Using Residual Correlations. Applied psychological measurement 2017; 41: 178-194
40 Andrich D. The Polytomous Rasch Model and the Equating of Two Instruments. In: Christensen KB, Kreiner S, Mesbah M. (editors.) Rasch Models in Health. London, UK: ILSTE Ltd; 2013: 164-196
41 Andrich D, Hagquist C. Real and Artificial Differential Item Functioning in Polytomous Items. Educ Psychol Meas 2015; 75: 185-207
42 Morris SB, DeShon RP. Combining effect size estimates in meta-analysis with repeated measures and independent-groups designs. Psychological Methods 2002; 7: 105-125
43 Cohen J. Statistical Power Analysis for the Behavioral Sciences. Hillsdale: Lawrence Erlbaum Associates; 1988
44 ANQ Nationaler Verein für Qualitätsentwicklung in Spitälern und Kliniken, Bern, Switzerland https://www.anq.ch/wp-content/uploads/2018/02/ANQ_Module_2_EBI.pdf Accessed [2018 Jul 19]
45 Hammond A, Tennant A, Tyson SF. et al. The reliability and validity of the English version of the Evaluation of Daily Activity Questionnaire for people with rheumatoid arthritis. Rheumatology (Oxford, England) 2015; 54: 1605-1615
46 Mainz J. Defining and classifying clinical indicators for quality improvement. International journal for quality in health care: journal of the International Society for Quality in Health Care 2003; 15: 523-530
47 Prodinger B, Tennant A, Stucki G. Standardized reporting of functioning information on ICF-based common metrics. European journal of physical and rehabilitation medicine 2018; 54: 110-117

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