Jiabi Wen1, Xuejing Jin2, Fatima Al Sayah1, Hilary Short1, Arto Ohinmaa1, Sara N Davison3, Michael Walsh4,5,6,7, Jeffrey A Johnson8. 1. School of Public Health, University of Alberta, Edmonton, AB, Canada. 2. Centre for Evidence-Based Chinese Medicine, Beijing University of Chinese Medicine, Beijing, China. 3. Division of Nephrology and Immunology, Department of Medicine, University of Alberta, Edmonton, AB, Canada. 4. Department of Medicine, McMaster University, Hamilton, ON, Canada. 5. Department of Health Research Methods, Evidence and Impact, McMaster University, Hamilton, Canada. 6. Population Health Research Institute, Hamilton, Canada. 7. St. Joseph's Healthcare Hamilton, Hamilton, Canada. 8. School of Public Health, University of Alberta, Edmonton, AB, Canada. jeff.johnson@ualberta.ca.
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.
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.
Authors: Adeera Levin; Brenda Hemmelgarn; Bruce Culleton; Sheldon Tobe; Philip McFarlane; Marcel Ruzicka; Kevin Burns; Braden Manns; Colin White; Francoise Madore; Louise Moist; Scott Klarenbach; Brendan Barrett; Robert Foley; Kailash Jindal; Peter Senior; Neesh Pannu; Sabin Shurraw; Ayub Akbari; Adam Cohn; Martina Reslerova; Vinay Deved; David Mendelssohn; Gihad Nesrallah; Joanne Kappel; Marcello Tonelli Journal: CMAJ Date: 2008-11-18 Impact factor: 8.262
Authors: Stavros Petrou; Oliver Rivero-Arias; Helen Dakin; Louise Longworth; Mark Oppe; Robert Froud; Alastair Gray Journal: Pharmacoeconomics Date: 2015-10 Impact factor: 4.981
Authors: Jenna M Evans; Alysha Glazer; Rebecca Lum; Esti Heale; Marnie MacKinnon; Peter G Blake; Michael Walsh Journal: Clin J Am Soc Nephrol Date: 2020-08-25 Impact factor: 8.237
Authors: Braden Manns; Brenda Hemmelgarn; Marcello Tonelli; Flora Au; Helen So; Rob Weaver; Amity E Quinn; Scott Klarenbach Journal: Can J Kidney Health Dis Date: 2019-04-04