Comparative efficacy of Chinese patent medicines for non-alcoholic fatty liver disease: A network meta-analysis.

The incidence of Non-alcoholic fatty liver disease (NAFLD) is increasing year by year. Researches showed that Chinese patent medicines (CPMs) had achieved good efficacy in the treatment of Non-alcoholic fatty liver disease. However, the debate on optimum Chinese patent medicine (CPM) persists. Therefore, we conducted a network meta-analysis to objectively compare the efficacy of different Chinese patent medicines in the treatment of Non-alcoholic fatty liver disease. PubMed, Embase, Cochrane Library, Web of Science, China National Knowledge Infrastructure, Wanfang Database, China Science and Technology Journal Database, and Chinese Biomedical Literature Database were used as databases for RCT researches retrieval. The retrieval time was from establishment of the database to July 2022. After effective data was extracted, Review Manager 5.4 and Cochrane Collaboration System Evaluator's Manual were used to assess bias risk. STATA 16.0 based on frequency theory was used for the network meta-analysis. Totally 39 studies were included, involving 13 Chinese patent medicines, including 4049 patients, of which 42 patients were lost. In terms of improving clinical efficiency rate, Zhibitai capsule was most likely the best choice of Chinese patent medicine for Non-alcoholic fatty liver disease. Liuwei Wuling tablet had the best effect in reducing serum ALT and AST; Gandan Shukang capsule had the best effect in reducing serum GGT; Qianggan capsule had the best effect in reducing serum TG; Dangfei Liganning capsule had the best effect in reducing serum TC. None of the included studies had serious adverse reactions. For patients with Non-alcoholic fatty liver disease in this NMA, Zhibitai capsule, Liuwei Wuling tablet, Gandan Shukang capsule, Qianggan capsule, Dangfei Liganning capsule might be noteworthy. Due to the uclear risk bias, better designed double-blind, multi center and large sample RCTs are needed which resolve the problems of blinding, selective reporting and allocation concealment. https://www.crd.york.ac.uk/prospero/, identifier CRD42022341240.