The Symptom Monitoring with Feedback Trial (SWIFT): protocol for a registry-based cluster randomised controlled trial in haemodialysis
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Date
2022
Authors
Greenham, L.
Bennett, P.N.
Dansie, K.
Viecelli, A.K.
Jesudason, S.
Mister, R.
Smyth, B.
Westall, P.
Herzog, S.
Brown, C.
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Journal article
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Trials, 2022; 23(1):419-1-419-13
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Lavern Greenham, Paul N. Bennett, Kathryn Dansie, Andrea K. Viecelli, Shilpanjali Jesudason, Rebecca Mister, Brendan Smyth, Portia Westall, Samuel Herzog, Chris Brown, William Handke, Suetonia C. Palmer, Fergus J. Caskey, Cecile Couchoud, John Simes, Stephen P. McDonald, and Rachael L. Morton
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Abstract
Background: Kidney failure prevalence is increasing worldwide. Haemodialysis, peritoneal dialysis or kidney transplantation are undertaken to extend life with kidney failure. People receiving haemodialysis commonly experience fatigue, pain, nausea, cramping, itching, sleeping difficulties, anxiety and depression. This symptom burden contributes to poor health-related quality of life (QOL) and is a major reason for treatment withdrawal and death. The Symptom monitoring WIth Feedback Trial (SWIFT) will test the hypothesis that regular symptom monitoring with feedback to people receiving haemodialysis and their treating clinical team can improve QOL. Methods: We are conducting an Australia and New Zealand Dialysis and Transplant (ANZDATA) registry-based cluster randomised controlled trial to determine the clinical- and cost-effectiveness at 12 months, of 3-monthly symptom monitoring using the Integrated Palliative Outcome Scale-Renal (IPOS-Renal) survey with clinician feedback, compared with usual care among adults treated with haemodialysis. Participants complete symptom scoring using a tablet, which are provided to participants and to clinicians. The trial aims to recruit 143 satellite haemodialysis centres, (up to 2400 participants). The primary outcome is change in health-related QOL, as measured by EuroQol 5-Dimension, 5-Level (EQ-5D-5L) instrument. Secondary outcomes include overall survival, symptom severity (including haemodialysis- associated fatigue), healthcare utilisation and cost-effectiveness. Discussion: SWIFT is the first registry-based trial in the Australian haemodialysis population to investigate whether regular symptom monitoring with feedback to participants and clinicians improves QOL. SWIFT is embedded in the ANZDATA Registry facilitating pragmatic recruitment from public and private dialysis clinics, throughout Australia. SWIFT will inform future collection, storage and reporting of patient-reported outcome measures (PROMs) within a clinical quality registry. As the first trial to rigorously estimate the efficacy and cost-effectiveness of routine PROMs collection and reporting in haemodialysis units, SWIFT will provide invaluable information to health services, clinicians and researchers working to improve the lives of those with kidney failure.
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Data source: Supplementary information, https://doi.org/10.1186/s13063-022-06355-0
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© The Author(s) 2022. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http:// creativecommons. org/ licenses/ by/4. 0/. The Creative Commons Public Domain Dedication waiver (http:// creativecommons. org/ publicdomain/ zero/1. 0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.