TCM nonpharmacological interventions for ankylosing spondylitis: A protocol for systematic review and network meta-analysis

BACKGROUND: Ankylosing spondylitis (AS) is a common infammatory rheumatic disease that affects the axial skeleton. Traditional Chinese medicine (TCM) nonpharmacological interventions are gaining an increasing popularity for AS. Nevertheless, the evidence of efficacy and safety of random controlled trials (RCTs) remains controversial. This study aims to evaluate the efficacy and acceptability of different TCM nonpharmacological therapies by systematic review and network meta-analysis. METHODS: According to the strategy, the authors will retrieve a total of 7 electronic databases by December 2020, including PubMed, the Cochrane Library, EMbase, China National Knowledge Infrastructure, China Biological Medicine, Chongqing VIP, and Wan-fang databases After a series of screening, 2 researchers will use Aggregate Data Drug Information System and Stata software to analyze the data extracted from the randomized controlled trials of TCM nonpharmacological interventions for AS. The primary outcome will be the improvement of Pain intensity and functional status/disability and the secondary outcomes will include lobal improvement, health-related quality of life, satisfaction with treatment, and adverse events. Both classical meta-analysis and network meta-analysis will be implemented to investigate direct and indirect evidences on this topic. The quality of the evidence will be evaluated using the Grading of Recommendations Assessment, Development and Evaluation instrument. RESULTS: This study will provide a reliable evidence for the selection of TCM nonpharmacological therapies in the treatment of AS. CONCLUSION: This study will generate evidence for different TCM nonpharmacological therapies for AS and provide a decision-making reference for clinical research. ETHICS AND DISSEMINATION: This study does not require ethical approval. The results will be disseminated through a peer-reviewed publication. OSF REGISTRATION NUMBER: DOI 10.17605/OSF.IO/FHD2U