Open AccessTherapies for diabetic gastroparesis
Author(s) -
Shengju Wang,
Ruili Wang,
Yanli Zhang,
Xu Zhang,
Baochao Cai,
Yan Lü,
Yuguo Xia,
Chen Qiu
Publication year - 2020
Publication title -
medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.59
H-Index - 148
eISSN - 1536-5964
pISSN - 0025-7974
DOI - 10.1097/md.0000000000020461
Subject(s) - medicine , medline , cochrane library , glycemic , meta analysis , randomized controlled trial , diabetes mellitus , intensive care medicine , critical appraisal , systematic review , clinical trial , data extraction , alternative medicine , pathology , political science , law , endocrinology
Background: Diabetic gastroparesis (DG) is a common autonomic neuropathy which impacts on nutritional state and quality of life in diabetic patients, and it also adversely affects glycemic control in diabetes. The prevalence of DG is growing with the number of patients with diabetes continues to increase. However, there is no definitive answer as to which of the current therapies is the best for the clinical treatment of the different manifestations of DG. The subject of this study is to answer the following question: what is the best intervention for diabetic patients with gastroparesis? Methods: Comprehensive searches of the Cochrane Library, PubMed, Embase, Medline, Central and Web of Science, and 4 Chinese databases, including China National Knowledge Infrastructure, VIP Database for Chinese Technical Periodicals, Chinese Biomedical Literature Database, and WanFang will be completed using the following keywords DG and therapies and related entry terms. Studies will be included, according to the eligibility criteria (randomized controlled trials and controlled clinical trials, considering specific outcome measures for DG). The reference lists of included studies will be manual searched. Relevant data will be extracted from included studies using a specially designed data extraction sheet. Risk of bias of the included studies will be assessed, and the overall strength of the evidence will be summarized through GRADE. A random effects model will be used for all pairwise meta-analyses (with a 95% confidence interval). A Bayesian network meta-analysis will explore the relative benefits between the various therapies. The review will be reported using the Preferred Reporting Items for Systematic Reviews incorporating Network Meta-Analyses statement. Network meta-analysis will be performed using a Bayesian framework through the Winbugs software. Results: This network meta-analysis will identify the best effective therapy for DG. Conclusion: This study will compare and evaluate current therapies for DG, and find the best treatment of DG.