4.5 Article

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

期刊

QUALITY OF LIFE RESEARCH
卷 31, 期 2, 页码 567-577

出版社

SPRINGER
DOI: 10.1007/s11136-021-02948-5

关键词

Mapping; Utility; EQ-5D-5L; ESAS-r; Renal; Chronic kidney disease; Hemodialysis

资金

  1. Canadian Institutes of Health Research under Canada's Strategy for Patient-Oriented Research [SCA-145103]
  2. Clive Kearon Mid Career Research Award from the Department of Medicine

向作者/读者索取更多资源

The study aimed to develop a mapping algorithm from ESAS-r: Renal to Canadian EQ-5D-5L index scores using data from a multi-center trial in hemodialysis units in northern Alberta, Canada. Generalized estimating equations (GEE) and generalized linear model (GLM) were found to be the best models for predicting EQ-5D-5L index scores in economic evaluations when only ESAS-r: Renal data are available. Further validation in populations with different characteristics is recommended, particularly where renal-specific symptoms are more prevalent.
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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