• Junfang Lei, Chengdong Zhang, Jialin Gai, Xiaohua Fan, and Jiqin Tang.
• College of Rehabilitation Medicine, Shandong University of Traditional Chinese Medicine, Jinan, China.
• Medicine (Baltimore). 2023 Apr 25; 102 (17): e33602e33602.

BackgroundSpasticity is one of the most common complications and sequelae of stroke, with the main clinical manifestations being increased muscle tension, pain, stiffness, and other disorders. It not only increases the length of hospitalization and medical costs but also affects the quality of daily life and the stress of returning to society, increasing the burden on patients and their families. At present, 2 driver types of deep muscle stimulator (DMS) have been used in the clinical treatment of post-stroke spasticity (PSS) with good clinical results, but there is no evidence of clinical efficacy and safety. Therefore, this study aims to integrate direct and indirect comparative clinical evidence through a systematic review and network meta-analysis (NMA). According to the data, different driver types for DMS with the same body of evidence will be collected, analyzed, and sequenced in a quantitative and comprehensive manner and then screened for the optimal driver type of DMS device for PSS treatment. The study also aims to provide reference value and an evidence-based theoretical basis for the clinical optimization of DMS equipment selection.MethodsA comprehensive retrieval of China National Knowledge Infrastructure, Chinese scientific journal database, China biological feature database, Wanfang Chinese databases and the Cochrane Library, PubMed, Web of Science, and Embase foreign databases will be conducted. Randomized controlled trials of these 2 driver types of DMS devices combined with conventional rehabilitation training of PSS will be searched and published. The retrieval time is from the establishment of the database to December 20, 2022. The 2 first authors will screen references that meet the inclusion criteria, independently extract data according to predesigned rules, and assess the quality of the included studies and the risk of bias according to the Cochrane 5.1 Handbook criteria. R programming and Aggregate Data Drug Information System software will be used to perform a combined NMA of the data and to evaluate the probability of ranking for all interventions.ResultsThe NMA and probability ranking will determine the best driver type of DMS device for PSS.ConclusionThis study will offer a comprehensive evidence-based approach to DMS therapy and assist doctors, PSS patients, and decision-makers in selecting a more efficient, secure, and cost-effective treatment option.Copyright © 2023 the Author(s). Published by Wolters Kluwer Health, Inc.

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