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Gepubliceerd in:

19-07-2021

Mapping the Edmonton Symptom Assessment System-Revised: Renal to the EQ-5D-5L in patients with chronic kidney disease

Auteurs: Jiabi Wen, Xuejing Jin, Fatima Al Sayah, Hilary Short, Arto Ohinmaa, Sara N. Davison, Michael Walsh, Jeffrey A. Johnson

Gepubliceerd in: Quality of Life Research | Uitgave 2/2022

Abstract

Purpose

The Edmonton Symptom Assessment System-Revised: Renal (ESAS-r: Renal) is a disease-specific patient-reported outcome measure (PROM) that assesses symptoms common in chronic kidney disease (CKD). There is no preference-based scoring system for the ESAS-r: Renal or a mapping algorithm to predict health utility values. We aimed to develop a mapping algorithm from the ESAS-r: Renal to the Canadian EQ-5D-5L index scores.

Methods

We used data from a multi-centre cluster randomized-controlled trial of the routine measurement and reporting of PROMs in hemodialysis units in Northern Alberta, Canada. In two arms of the trial, both the ESAS-r: Renal and the EQ-5D-5L were administered to CKD patients undergoing hemodialysis. We used data from one arm for model estimation, and data from the other for validation. We explored direct and indirect mapping models; model selection was based on statistical fit and predictive power.

Results

Complete data were available for 506 patient records in the estimation sample and 242 in the validation sample. All models tended to perform better in patients with good health, and worse in those with poor health. Generalized estimating equations (GEE) and generalized linear model (GLM) on selected ESAS-r: Renal items were selected as final models as they fitted the best in estimation and validation sample.

Conclusion

When only ESAS-r: Renal data are available, one could use GEE and GLM to predict EQ-5D-5L index scores for use in economic evaluation. External validation on populations with different characteristics is warranted, especially where renal-specific symptoms are more prevalent.

vorige artikel The impact of multimorbidity patterns on health-related quality of life in the general population: results of the Belgian Health Interview Survey

volgende artikel Feasibility and performance of smartphone-based daily micro-surveys among patients recovering from cancer surgery

Alleen toegankelijk voor geautoriseerde gebruikers

Levin, A., Hemmelgarn, B., Culleton, B., Tobe, S., McFarlane, P., Ruzicka, M., et al. (2008). Guidelines for the management of chronic kidney disease. CMAJ, 179(11), 1154–1162. https://doi.org/10.1503/cmaj.080351CrossRefPubMedPubMedCentral

Bello, A. K., Levin, A., & Lunney, M. (2019). Global Kidney Health Atlas: A report by the International Society of Nephrology on the global burden of end-stage kidney disease and capacity for kidney replacement therapy and conservative care across world countries and regions. Brussels: International Society of Nephrology.

Cruz, M. C., Andrade, C., Urrutia, M., Draibe, S., Nogueira-Martins, L. A., & de Sesso, R. C. C. (2011). Quality of life in patients with chronic kidney disease. Clinics, 66(6), 991–995. https://doi.org/10.1590/S1807-59322011000600012CrossRefPubMedPubMedCentral

Peipert, J. D., & Hays, R. D. (2017). Using patient-reported measures in dialysis clinics. Clinical Journal of the American Society of Nephrology, 12(11), 1889–1891. https://doi.org/10.2215/CJN.02250217CrossRefPubMedPubMedCentral

Davison, S. N., Jhangri, G. S., & Johnson, J. A. (2006). Longitudinal validation of a modified Edmonton symptom assessment system (ESAS) in haemodialysis patients. Nephrology Dialysis Transplantation, 21(11), 3189–3195. https://doi.org/10.1093/ndt/gfl380CrossRef

Davison, S. N., Jhangri, G. S., & Johnson, J. A. (2006). Cross-sectional validity of a modified Edmonton symptom assessment system in dialysis patients: A simple assessment of symptom burden. Kidney International, 69(9), 1621–1625. https://doi.org/10.1038/sj.ki.5000184CrossRefPubMed

Evans, J. M., Glazer, A., Lum, R., Heale, E., MacKinnon, M., Blake, P. G., & Walsh, M. (2020). Implementing a patient-reported outcome measure for hemodialysis patients in routine clinical care: Perspectives of patients and providers on ESAS-r:Renal. Clinical Journal of the American Society of Nephrology, 15(9), 1299–1309. https://doi.org/10.2215/CJN.01840220CrossRefPubMedPubMedCentral

Canadian Institute for Health Information (CIHI). (2017). Patient-Centred Measurement and Reporting in Canada: Launching the Discussion Toward a Future State (p. 46). Retrieved from https://www.cihi.ca/sites/default/files/document/visioning-day-paper-en-web.pdf

Hays, R. D., Kallich, J. D., Mapes, D. L., Coons, S. J., & Carter, W. B. (1994). Development of the kidney disease quality of life (KDQOL) instrument. Quality of Life Research, 3(5), 329–338. https://doi.org/10.1007/BF00451725CrossRefPubMed

10.

Manns, B., Hemmelgarn, B., Tonelli, M., Au, F., So, H., Weaver, R., et al. (2019). The cost of care for people with chronic kidney disease. Canadian Journal of Kidney Health and Disease, 6, 2054358119835521. https://doi.org/10.1177/2054358119835521CrossRefPubMedPubMedCentral

11.

Senanayake, S., Graves, N., Healy, H., Baboolal, K., & Kularatna, S. (2020). Cost-utility analysis in chronic kidney disease patients undergoing kidney transplant; what pays? A systematic review. Cost Effectiveness and Resource Allocation, 18(1), 1–13. https://doi.org/10.1186/s12962-020-00213-zCrossRef

12.

Devlin, N. J., & Brooks, R. (2017). EQ-5D and the EuroQol Group: Past, present and future. Applied Health Economics and Health Policy, 15(2), 127–137. https://doi.org/10.1007/s40258-017-0310-5CrossRefPubMedPubMedCentral

13.

Gibbons, E., & Fitzpatrick, R. (2010). A structured review of patient-reported outcome measures for people with chronic kidney disease. Department of Public Health University of Oxford.

14.

Elliott, M. J., & Hemmelgarn, B. R. (2019). Patient-reported outcome measures in CKD care: The importance of demonstrating need and value. American Journal of Kidney Diseases, 74(2), 148–150.CrossRef

15.

Longworth, L., & Rowen, D. (2013). Mapping to obtain EQ-5D utility values for use in NICE health technology assessments. Value in Health, 16(1), 202–210. https://doi.org/10.1016/j.jval.2012.10.010CrossRefPubMed

16.

Guidelines for the economic evaluation of health technologies: Canada. 4th ed. (2017). CADTH. Retrieved from https://www.cadth.ca/about-cadth/how-we-do-it/methods-and-guidelines/guidelines-for-the-economic-evaluation-of-health-technologies-canada

17.

Petrou, S., Rivero-Arias, O., Dakin, H., Longworth, L., Oppe, M., Froud, R., & Gray, A. (2015). The MAPS reporting statement for studies mapping onto generic preference-based outcome measures: Explanation and elaboration. PharmacoEconomics, 33(10), 993–1011. https://doi.org/10.1007/s40273-015-0312-9CrossRefPubMed

18.

Wailoo, A. J., Hernandez-Alava, M., Manca, A., Mejia, A., Ray, J., Crawford, B., et al. (2017). Mapping to estimate health-state utility from non-preference-based outcome measures: An ISPOR good practices for outcomes research task force report. Value in Health, 20(1), 18–27. https://doi.org/10.1016/j.jval.2016.11.006CrossRefPubMed

19.

Johnson, J. A., Al Sayah, F., Buzinski, R., Corradetti, B., Davison, S. N., Elliott, M. J., et al. (2020). A cluster randomized controlled trial for the Evaluation of routinely Measured PATient reported outcomes in HemodialYsis care (EMPATHY): A study protocol. BMC Health Services Research, 20(1), 731. https://doi.org/10.1186/s12913-020-05557-zCrossRefPubMedPubMedCentral

20.

Alava, M. H., & Wailoo, A. (2015). Fitting adjusted limited dependent variable mixture models to EQ-5D. The Stata Journal, 15(3), 737–750. https://doi.org/10.1177/1536867X1501500307CrossRef

21.

Cohen, J. (1992). A power primer. Psychological Bulletin, 112(1), 155.CrossRef

22.

Hui, D., & Bruera, E. (2017). The edmonton symptom assessment system 25 years later: past, present and future developments. Journal of Pain and Symptom Management, 53(3), 630–643. https://doi.org/10.1016/j.jpainsymman.2016.10.370CrossRefPubMed

23.

Herdman, M., Gudex, C., Lloyd, A., Janssen, Mf., Kind, P., Parkin, D., et al. (2011). Development and preliminary testing of the new five-level version of EQ-5D (EQ-5D-5L). Quality of Life Research, 20(10), 1727–1736. https://doi.org/10.1007/s11136-011-9903-xCrossRefPubMedPubMedCentral

24.

Xie, F., Pullenayegum, E., Gaebel, K., Bansback, N., Bryan, S., Ohinmaa, A., et al. (2016). A time trade-off-derived value set of the EQ-5D-5L for Canada. Medical Care, 54(1), 98–105. https://doi.org/10.1097/MLR.0000000000000447CrossRefPubMed

25.

Mukaka, M. (2012). A guide to appropriate use of Correlation coefficient in medical research. Malawi Medical Journal, 24(3), 69–71.PubMedPubMedCentral

26.

Vokó, Z., Németh, R., Nagyjánosi, L., Jermendy, G., Winkler, G., Hídvégi, T., et al. (2014). Mapping the nottingham health profile onto the preference-based EuroQol-5D instrument for patients with diabetes. Value in Health Regional Issues, 4, 31–36. https://doi.org/10.1016/j.vhri.2014.06.002CrossRefPubMed

27.

Chuang, L.-H., & Kind, P. (2009). Converting the SF-12 into the EQ-5D. PharmacoEconomics, 27(6), 491–505. https://doi.org/10.2165/00019053-200927060-00005CrossRefPubMed

28.

Gu, N. Y., Botteman, M. F., Ji, X., Bell, C. F., Carter, J. A., & van Hout, B. (2011). Mapping of the Insomnia Severity Index and other sleep measures to EuroQol EQ-5D health state utilities. Health and Quality of Life Outcomes, 9, 119. https://doi.org/10.1186/1477-7525-9-119CrossRefPubMedPubMedCentral

29.

Dakin, H., Gray, A., & Murray, D. (2013). Mapping analyses to estimate EQ-5D utilities and responses based on Oxford Knee Score. Quality of Life Research, 22(3), 683–694. https://doi.org/10.1007/s11136-012-0189-4CrossRefPubMed

30.

Rowen, D., Brazier, J., & Roberts, J. (2009). Mapping SF-36 onto the EQ-5D index: How reliable is the relationship? Health and Quality of Life Outcomes, 7(1), 1–9. https://doi.org/10.1186/1477-7525-7-27CrossRef

31.

Khan, K. A., Petrou, S., Rivero-Arias, O., Walters, S. J., & Boyle, S. E. (2014). Mapping EQ-5D utility scores from the PedsQL^TM generic core scales. PharmacoEconomics, 32(7), 693–706. https://doi.org/10.1007/s40273-014-0153-yCrossRefPubMed

32.

Kay, S., Tolley, K., Colayco, D., Khalaf, K., Anderson, P., & Globe, D. (2013). Mapping EQ-5D utility scores from the incontinence quality of life questionnaire among patients with neurogenic and idiopathic overactive bladder. Value in Health, 16(2), 394–402. https://doi.org/10.1016/j.jval.2012.12.005CrossRefPubMed

33.

Gray, L. A., & Alava, M. H. (2018). A command for fitting mixture regression models for bounded dependent variables using the beta distribution. The Stata Journal, 18(1), 51–75. https://doi.org/10.1177/1536867X1801800105CrossRef

34.

van Hout, B., Janssen, M. F., Feng, Y.-S., Kohlmann, T., Busschbach, J., Golicki, D., et al. (2012). Interim scoring for the EQ-5D-5L: Mapping the EQ-5D-5L to EQ-5D-3L value sets. Value in Health, 15(5), 708–715. https://doi.org/10.1016/j.jval.2012.02.008CrossRefPubMed

35.

Kiadaliri, A., Alava, M. H., Roos, E. M., & Englund, M. (2020). Mapping EQ-5D-3L from the knee injury and osteoarthritis outcome score (KOOS). Quality of Life Research, 29(1), 265–274. https://doi.org/10.1007/s11136-019-02303-9CrossRefPubMed

36.

Steyerberg, E. W., & Harrell, F. E. (2016). Prediction models need appropriate internal, internal-external, and external validation. Journal of Clinical Epidemiology, 69, 245–247. https://doi.org/10.1016/j.jclinepi.2015.04.005CrossRefPubMed

37.

Brazier, J. E., Yang, Y., Tsuchiya, A., & Rowen, D. L. (2010). A review of studies mapping (or cross walking) non-preference based measures of health to generic preference-based measures. The European Journal of Health Economics, 11(2), 215–225. https://doi.org/10.1007/s10198-009-0168-zCrossRefPubMed

38.

McClure, N. S., Sayah, F. A., Xie, F., Luo, N., & Johnson, J. A. (2017). Instrument-defined estimates of the minimally important difference for EQ-5D-5L index scores. Value in Health: The Journal of the International Society for Pharmacoeconomics and Outcomes Research, 20(4), 644–650. https://doi.org/10.1016/j.jval.2016.11.015CrossRef

39.

Moskovitz, M., Jao, K., Su, J., Brown, M. C., Naik, H., Eng, L., et al. (2019). Combined cancer patient-reported symptom and health utility tool for routine clinical implementation: A real-world comparison of the ESAS and EQ-5D in multiple cancer sites. Current Oncology, 26(6), 15. https://doi.org/10.3747/co.26.5297CrossRef

40.

Davison, N. J., Thompson, A. J., Turner, A. J., Longworth, L., McElhone, K., Griffiths, C. E. M., & Payne, K. (2018). Generating EQ-5D-3L utility scores from the dermatology life quality index: A mapping study in patients with psoriasis. Value in Health: The Journal of the International Society for Pharmacoeconomics and Outcomes Research, 21(8), 1010–1018. https://doi.org/10.1016/j.jval.2017.10.024CrossRef

41.

Acaster, S., Pinder, B., Mukuria, C., & Copans, A. (2015). Mapping the EQ-5D index from the cystic fibrosis questionnaire-revised using multiple modelling approaches. Health and Quality of Life Outcomes. https://doi.org/10.1186/s12955-015-0224-6CrossRefPubMedPubMedCentral

42.

Versteegh, M. M., Rowen, D., Brazier, J. E., & Stolk, E. A. (2010). Mapping onto Eq-5 D for patients in poor health. Health and Quality of Life Outcomes, 8, 141. https://doi.org/10.1186/1477-7525-8-141CrossRefPubMedPubMedCentral

43.

Yang, F., Devlin, N., & Luo, N. (2019). Impact of mapped EQ-5D utilities on cost-effectiveness analysis: In the case of dialysis treatments. The European Journal of Health Economics, 20(1), 99–105. https://doi.org/10.1007/s10198-018-0987-xCrossRefPubMed

44.

Yang, F., Lau, T., Lee, E., Vathsala, A., Chia, K. S., & Luo, N. (2015). Comparison of the preference-based EQ-5D-5L and SF-6D in patients with end-stage renal disease (ESRD). The European Journal of Health Economics: HEPAC, 16(9), 1019–1026. https://doi.org/10.1007/s10198-014-0664-7CrossRefPubMed

Titel: Mapping the Edmonton Symptom Assessment System-Revised: Renal to the EQ-5D-5L in patients with chronic kidney disease
Auteurs: Jiabi Wen
Xuejing Jin
Fatima Al Sayah
Hilary Short
Arto Ohinmaa
Sara N. Davison
Michael Walsh
Jeffrey A. Johnson
Publicatiedatum: 19-07-2021
Uitgeverij: Springer International Publishing
Gepubliceerd in: Quality of Life Research / Uitgave 2/2022
Print ISSN: 0962-9343
Elektronisch ISSN: 1573-2649
DOI: https://doi.org/10.1007/s11136-021-02948-5

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Abstract

Purpose

Methods

Results

Conclusion

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