The effect of traditional Chinese medicine treatment for post-viral olfactory dysfunction: A protocol for systematic review and meta-analysis.
Post-viral olfactory dysfunction (PVOD) have been reported in infections caused by several respiratory viruses, especially in COVID-19 which influence severely the quality of life of affected subjects. Few study has been published on the treatment of PVOD. Traditional Chinese medicine (TCM) is an effective method for PVOD which effects and safety have been confirmed. Therefore, this study is aim to evaluate the effects of TCM on PVOD. A searching strategy will be carried out mainly in the following databases in English and Chinese, PubMed, EMBASE, Cochrane Central Register of Controlled Trials, China Network Knowledge Infrastructure (CNKI), Chinese Scientific Journal Database (VIP), Chinese Biomedical and Medical Database (CBM), and Wanfang Database. Only randomized controlled trials related to TCM for PVOD will be included to enhance effectiveness. The primary outcome is the effective rate of PVOD. The secondary outcomes are included olfactory domain value examination, visual analogue scale (VAS), questionnaires of olfactory disorders (QOD), T&T olfactometer test, Sniffin ticks test, and any other clinical assessments. Two authors will independently perform study selection, data extraction, and quality assessment to ensure the quality of the systematic evaluation. Every disagreement will be deal with by the third author. Data synthesis and subgroup analysis will be performed in the Review Manager V 5.3.3. This study is aim to evaluate the efficacy and safety of TCM in PVOD. : This meta-analysis may provide more reliable evidence-based medical evidence for clinical practice to assist patient in relieving PVOD. There is no need to acquire ethical approval for individuals come from literatures instead of recruiting directly. The findings of this review will be reported in peer-reviewed publications and/or presented at relevant conferences. CRD42021238977.
Fangfang Ma, Hewei Zhang, Bingxue Li, Peiyu Cheng, Yunfei Ma, Mingwei Yu, Xiaomin Wang