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

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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.

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References

  1. 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.080351

    Article  PubMed  PubMed Central  Google Scholar 

  2. 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.

    Google Scholar 

  3. 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/S1807-59322011000600012

    Article  PubMed  PubMed Central  Google Scholar 

  4. 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.02250217

    Article  PubMed  PubMed Central  Google Scholar 

  5. 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/gfl380

    Article  Google Scholar 

  6. 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.5000184

    Article  CAS  PubMed  Google Scholar 

  7. 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.01840220

    Article  PubMed  PubMed Central  Google Scholar 

  8. 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

  9. 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/BF00451725

    Article  CAS  PubMed  Google Scholar 

  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/10.1177/2054358119835521

    Article  PubMed  PubMed Central  Google Scholar 

  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-z

    Article  Google Scholar 

  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/s40258-017-0310-5

    Article  PubMed  PubMed Central  Google Scholar 

  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.

    Article  Google Scholar 

  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.2012.10.010

    Article  PubMed  Google Scholar 

  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/s40273-015-0312-9

    Article  PubMed  Google Scholar 

  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.2016.11.006

    Article  PubMed  Google Scholar 

  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-z

    Article  PubMed  PubMed Central  Google Scholar 

  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/10.1177/1536867X1501500307

    Article  Google Scholar 

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

    Article  CAS  Google Scholar 

  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.2016.10.370

    Article  PubMed  Google Scholar 

  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-x

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  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.0000000000000447

    Article  PubMed  Google Scholar 

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

    CAS  PubMed  PubMed Central  Google Scholar 

  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.2014.06.002

    Article  PubMed  Google Scholar 

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

    Article  PubMed  Google Scholar 

  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/10.1186/1477-7525-9-119

    Article  PubMed  PubMed Central  Google Scholar 

  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/s11136-012-0189-4

    Article  PubMed  Google Scholar 

  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/10.1186/1477-7525-7-27

    Article  Google Scholar 

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

    Article  PubMed  Google Scholar 

  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.2012.12.005

    Article  PubMed  Google Scholar 

  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/10.1177/1536867X1801800105

    Article  Google Scholar 

  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.2012.02.008

    Article  PubMed  Google Scholar 

  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/s11136-019-02303-9

    Article  PubMed  Google Scholar 

  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.2015.04.005

    Article  PubMed  Google Scholar 

  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-z

    Article  PubMed  Google Scholar 

  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.2016.11.015

    Article  Google Scholar 

  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.26.5297

    Article  Google Scholar 

  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.2017.10.024

    Article  Google Scholar 

  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/s12955-015-0224-6

    Article  PubMed  PubMed Central  Google Scholar 

  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/10.1186/1477-7525-8-141

    Article  PubMed  PubMed Central  Google Scholar 

  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-x

    Article  PubMed  Google Scholar 

  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/s10198-014-0664-7

    Article  PubMed  Google Scholar 

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Acknowledgements

We would like to acknowledge the contributions of the following individuals for designing, planning, and implementing the EMPATHY Trial in Alberta Kidney Care—North: Buzinski R, Davison SN, Duperron P (deceased), Johnson JA, Klarenbach S, Nhan J, Short H, Wasylynuk BA. We appreciate the following individuals for providing insights into this paper: Angie Chiu, Katherine Harback, Manikarnika Kanjilal, Negar Razavilar. The statistics assistance provided by Sentil Senthilselvan and Peiran Yao was greatly appreciated.

Funding

EMPATHY is a project of the Can-SOLVE CKD Network, supported by the Canadian Institutes of Health Research under Canada’s Strategy for Patient-Oriented Research (Reference #SCA-145103). This large network supports a large number of research projects, including the EMPATHY Trial and provides basic infrastructure and resources. Walsh M is supported by a Clive Kearon Mid Career Research Award from the Department of Medicine.

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Correspondence to Jeffrey A. Johnson.

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The authors have no conflicts of interest to declare that are relevant to the content of this article.

Ethical approval and consent to participate

The University of Alberta Health Research Ethics Board granted the EMPATHY trial ethics approval (Pro00077850) to be conducted under a waiver of consent. This was considered appropriate because PROM collection was occurring already, or was planned to be implemented, as determined by the renal program; the intervention (linking the PROM to patient/provider discussion) is of minimal risk to patients, and all treatments are ascribed based on the provider judgment, not by study protocol; seeking informed consent would not be feasible in the framework of making this part of routine clinical care; seeking informed consent would likely bias participation resulting in inaccurate estimations of effect which would render the results of the trial uninformative for the use of these measures as part of routine care.

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Wen, J., Jin, X., Al Sayah, F. et al. Mapping the Edmonton Symptom Assessment System-Revised: Renal to the EQ-5D-5L in patients with chronic kidney disease. Qual Life Res 31, 567–577 (2022). https://doi.org/10.1007/s11136-021-02948-5

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