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Efficacy of traditional Chinese medicine injection for diabetic kidney disease: A network meta analysis and systematic review

Background: Diabetic kidney disease (DKD) is an important public health problem worldwide that increases the mortality of patients and incurs high medical costs. Traditional Chinese Medicine injections (TCMIs) are widely used in clinical practice. However, their efficacy is unknown owing to a lack o...

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Autores principales: Long, Caiyi, Feng, Haoyue, Liu, Zheng, Li, Zihan, Liu, Jun, Jiang, Yayi, Yue, Rensong
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9981802/
https://www.ncbi.nlm.nih.gov/pubmed/36874023
http://dx.doi.org/10.3389/fphar.2023.1028257
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author Long, Caiyi
Feng, Haoyue
Liu, Zheng
Li, Zihan
Liu, Jun
Jiang, Yayi
Yue, Rensong
author_facet Long, Caiyi
Feng, Haoyue
Liu, Zheng
Li, Zihan
Liu, Jun
Jiang, Yayi
Yue, Rensong
author_sort Long, Caiyi
collection PubMed
description Background: Diabetic kidney disease (DKD) is an important public health problem worldwide that increases the mortality of patients and incurs high medical costs. Traditional Chinese Medicine injections (TCMIs) are widely used in clinical practice. However, their efficacy is unknown owing to a lack of definitive evidence. This study conducted a network meta-analysis (NMA) to evaluate the efficacy and safety of traditional Chinese medicine injections in the treatment of DKD to provide a reference for clinical treatment. Methods: Total 7 databases had been searched, which included PubMed, Embase, Cochrane Library, Web of Science, China National Knowledge Infrastructure (CNKI), Chinese scientific journal database (VIP), WanFang, and SinoMed. Only randomised controlled trials (RCT) had been included for analysis. The retrieval time limit was from the establishment of the database until 20 July 2022. Cochrane Risk of Bias 2.0 tool was used to evaluate the quality of the studies. Network meta-analyses, and Trial Sequential Analyses (TSA) were used to analysis the effectiveness of the included RCTs for DKD. The Stata 15.1 and R 4.0.4 were used to perform the network meta-analysis. Sensitivity analysis was used to assess the robustness of the findings. The effect of the intervention evidence are summarized on the basis of the minimum background framework. Results: NMA showed that the total effective rate of SMI, DCI, DHI, HQI, and SKI combined with alprostadil injection (PGE1) was better than PGE1 single used. Based on the surface under the cumulative ranking curve values, PGE1+DHI was the most effective for urinary albumin excretion rate and 24 h urinary albumin, PGE1+HQI was the most effective for the total response rate and β2-MG, and PGE1+SKI was the most effective for serum creatinine and blood urea nitrogen. Cluster analysis found that PGE1+HQI and PGE1+SKI could be the best treatments in terms of primary outcome measures. PGE1+SKI was found to be most effective on glomerular filtration function. PGE1+DHI was most effective for urinary protein-related indices. Conclusion: The efficacy of TCMI combined with PGE1 was higher than PGE1 single used. PGE1+HQI and PGE1+SKI were the most effective treatments. The safety of TCMI treatment should be investigated further. This study needs to be validated using large-sample, double-blind, multicentre RCTs. Systematic Review Registration: [https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=348333], identifier [CRD42022348333].
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spelling pubmed-99818022023-03-04 Efficacy of traditional Chinese medicine injection for diabetic kidney disease: A network meta analysis and systematic review Long, Caiyi Feng, Haoyue Liu, Zheng Li, Zihan Liu, Jun Jiang, Yayi Yue, Rensong Front Pharmacol Pharmacology Background: Diabetic kidney disease (DKD) is an important public health problem worldwide that increases the mortality of patients and incurs high medical costs. Traditional Chinese Medicine injections (TCMIs) are widely used in clinical practice. However, their efficacy is unknown owing to a lack of definitive evidence. This study conducted a network meta-analysis (NMA) to evaluate the efficacy and safety of traditional Chinese medicine injections in the treatment of DKD to provide a reference for clinical treatment. Methods: Total 7 databases had been searched, which included PubMed, Embase, Cochrane Library, Web of Science, China National Knowledge Infrastructure (CNKI), Chinese scientific journal database (VIP), WanFang, and SinoMed. Only randomised controlled trials (RCT) had been included for analysis. The retrieval time limit was from the establishment of the database until 20 July 2022. Cochrane Risk of Bias 2.0 tool was used to evaluate the quality of the studies. Network meta-analyses, and Trial Sequential Analyses (TSA) were used to analysis the effectiveness of the included RCTs for DKD. The Stata 15.1 and R 4.0.4 were used to perform the network meta-analysis. Sensitivity analysis was used to assess the robustness of the findings. The effect of the intervention evidence are summarized on the basis of the minimum background framework. Results: NMA showed that the total effective rate of SMI, DCI, DHI, HQI, and SKI combined with alprostadil injection (PGE1) was better than PGE1 single used. Based on the surface under the cumulative ranking curve values, PGE1+DHI was the most effective for urinary albumin excretion rate and 24 h urinary albumin, PGE1+HQI was the most effective for the total response rate and β2-MG, and PGE1+SKI was the most effective for serum creatinine and blood urea nitrogen. Cluster analysis found that PGE1+HQI and PGE1+SKI could be the best treatments in terms of primary outcome measures. PGE1+SKI was found to be most effective on glomerular filtration function. PGE1+DHI was most effective for urinary protein-related indices. Conclusion: The efficacy of TCMI combined with PGE1 was higher than PGE1 single used. PGE1+HQI and PGE1+SKI were the most effective treatments. The safety of TCMI treatment should be investigated further. This study needs to be validated using large-sample, double-blind, multicentre RCTs. Systematic Review Registration: [https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=348333], identifier [CRD42022348333]. Frontiers Media S.A. 2023-02-17 /pmc/articles/PMC9981802/ /pubmed/36874023 http://dx.doi.org/10.3389/fphar.2023.1028257 Text en Copyright © 2023 Long, Feng, Liu, Li, Liu, Jiang and Yue. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Pharmacology
Long, Caiyi
Feng, Haoyue
Liu, Zheng
Li, Zihan
Liu, Jun
Jiang, Yayi
Yue, Rensong
Efficacy of traditional Chinese medicine injection for diabetic kidney disease: A network meta analysis and systematic review
title Efficacy of traditional Chinese medicine injection for diabetic kidney disease: A network meta analysis and systematic review
title_full Efficacy of traditional Chinese medicine injection for diabetic kidney disease: A network meta analysis and systematic review
title_fullStr Efficacy of traditional Chinese medicine injection for diabetic kidney disease: A network meta analysis and systematic review
title_full_unstemmed Efficacy of traditional Chinese medicine injection for diabetic kidney disease: A network meta analysis and systematic review
title_short Efficacy of traditional Chinese medicine injection for diabetic kidney disease: A network meta analysis and systematic review
title_sort efficacy of traditional chinese medicine injection for diabetic kidney disease: a network meta analysis and systematic review
topic Pharmacology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9981802/
https://www.ncbi.nlm.nih.gov/pubmed/36874023
http://dx.doi.org/10.3389/fphar.2023.1028257
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