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Cardiovascular and kidney outcomes of uric acid-lowering therapy in patients with different kidney functions: study protocol for a systematic review, pairwise and network meta-analysis
INTRODUCTION: Hyperuricaemia has been implicated in the development of kidney function in populations with chronic kidney disease; however, the benefits of urate-lowering therapy (ULT) remain uncertain in different clinical studies. The different kidney functions of enrolled populations and distinct...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BMJ Publishing Group
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9923315/ https://www.ncbi.nlm.nih.gov/pubmed/36754561 http://dx.doi.org/10.1136/bmjopen-2021-059096 |
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author | Zhang, Yaqing Song, Runxia Hua, Ying Su, Xiaole Wang, Lihua |
author_facet | Zhang, Yaqing Song, Runxia Hua, Ying Su, Xiaole Wang, Lihua |
author_sort | Zhang, Yaqing |
collection | PubMed |
description | INTRODUCTION: Hyperuricaemia has been implicated in the development of kidney function in populations with chronic kidney disease; however, the benefits of urate-lowering therapy (ULT) remain uncertain in different clinical studies. The different kidney functions of enrolled populations and distinct pharmacokinetic characteristics of ULT might be of the essence for the contrasting results. In this study, we will synthesise all available data from randomised controlled trials (RCTs) and cohort studies, then evaluate the outcomes of ULT in patients stratified by different estimated glomerular filtration rate (eGFR) stratifications. Furthermore, we will attempt to explore a relatively optimal ULT regimen using a Bayesian network meta-analysis in different eGFRs. METHODS AND ANALYSIS: We searched published and unpublished data from MEDLINE, EMBASE, the Cochrane Central Register of Controlled trials and ClinicalTrials.gov website (before March 2022) for RCTs and cohort studies without language restriction. In the pairwise meta-analysis, all regimens of ULT will be pooled as a whole and compared with controls in different eGFRs. The random-effects model will be applied to generate the summary values using the software Stata V.12.0 (StataCorp). Network meta-analysis within a Bayesian framework will be conducted to explore the relative efficacy profiles of different ULTs and to find optimal ULT in different eGFRs. The software of WinBUGS V.1.4.3 and R2WinBUGS package of R V.3.1.1 will be used in the network meta-analysis. Primary outcomes will be the occurrence of major cardiovascular events and kidney failure events. Secondary outcomes will include the rate of change in eGFR per year, all-cause death, changes in serum uric acid level and major adverse events. Two authors will independently review study selection, data extraction and quality assessment. ETHICS AND DISSEMINATION: The meta-analysis does not require ethical certification. The results will be disseminated through publication in a peer-reviewed journal and through presentations at academic conferences. PROSPERO REGISTRATION NUMBER: CRD42021226163. |
format | Online Article Text |
id | pubmed-9923315 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | BMJ Publishing Group |
record_format | MEDLINE/PubMed |
spelling | pubmed-99233152023-02-14 Cardiovascular and kidney outcomes of uric acid-lowering therapy in patients with different kidney functions: study protocol for a systematic review, pairwise and network meta-analysis Zhang, Yaqing Song, Runxia Hua, Ying Su, Xiaole Wang, Lihua BMJ Open Renal Medicine INTRODUCTION: Hyperuricaemia has been implicated in the development of kidney function in populations with chronic kidney disease; however, the benefits of urate-lowering therapy (ULT) remain uncertain in different clinical studies. The different kidney functions of enrolled populations and distinct pharmacokinetic characteristics of ULT might be of the essence for the contrasting results. In this study, we will synthesise all available data from randomised controlled trials (RCTs) and cohort studies, then evaluate the outcomes of ULT in patients stratified by different estimated glomerular filtration rate (eGFR) stratifications. Furthermore, we will attempt to explore a relatively optimal ULT regimen using a Bayesian network meta-analysis in different eGFRs. METHODS AND ANALYSIS: We searched published and unpublished data from MEDLINE, EMBASE, the Cochrane Central Register of Controlled trials and ClinicalTrials.gov website (before March 2022) for RCTs and cohort studies without language restriction. In the pairwise meta-analysis, all regimens of ULT will be pooled as a whole and compared with controls in different eGFRs. The random-effects model will be applied to generate the summary values using the software Stata V.12.0 (StataCorp). Network meta-analysis within a Bayesian framework will be conducted to explore the relative efficacy profiles of different ULTs and to find optimal ULT in different eGFRs. The software of WinBUGS V.1.4.3 and R2WinBUGS package of R V.3.1.1 will be used in the network meta-analysis. Primary outcomes will be the occurrence of major cardiovascular events and kidney failure events. Secondary outcomes will include the rate of change in eGFR per year, all-cause death, changes in serum uric acid level and major adverse events. Two authors will independently review study selection, data extraction and quality assessment. ETHICS AND DISSEMINATION: The meta-analysis does not require ethical certification. The results will be disseminated through publication in a peer-reviewed journal and through presentations at academic conferences. PROSPERO REGISTRATION NUMBER: CRD42021226163. BMJ Publishing Group 2023-02-08 /pmc/articles/PMC9923315/ /pubmed/36754561 http://dx.doi.org/10.1136/bmjopen-2021-059096 Text en © Author(s) (or their employer(s)) 2023. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ. https://creativecommons.org/licenses/by-nc/4.0/This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, appropriate credit is given, any changes made indicated, and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/ (https://creativecommons.org/licenses/by-nc/4.0/) . |
spellingShingle | Renal Medicine Zhang, Yaqing Song, Runxia Hua, Ying Su, Xiaole Wang, Lihua Cardiovascular and kidney outcomes of uric acid-lowering therapy in patients with different kidney functions: study protocol for a systematic review, pairwise and network meta-analysis |
title | Cardiovascular and kidney outcomes of uric acid-lowering therapy in patients with different kidney functions: study protocol for a systematic review, pairwise and network meta-analysis |
title_full | Cardiovascular and kidney outcomes of uric acid-lowering therapy in patients with different kidney functions: study protocol for a systematic review, pairwise and network meta-analysis |
title_fullStr | Cardiovascular and kidney outcomes of uric acid-lowering therapy in patients with different kidney functions: study protocol for a systematic review, pairwise and network meta-analysis |
title_full_unstemmed | Cardiovascular and kidney outcomes of uric acid-lowering therapy in patients with different kidney functions: study protocol for a systematic review, pairwise and network meta-analysis |
title_short | Cardiovascular and kidney outcomes of uric acid-lowering therapy in patients with different kidney functions: study protocol for a systematic review, pairwise and network meta-analysis |
title_sort | cardiovascular and kidney outcomes of uric acid-lowering therapy in patients with different kidney functions: study protocol for a systematic review, pairwise and network meta-analysis |
topic | Renal Medicine |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9923315/ https://www.ncbi.nlm.nih.gov/pubmed/36754561 http://dx.doi.org/10.1136/bmjopen-2021-059096 |
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