Chen Min-Li, Qian Shi-Yan, Yang Jiang-Li, Zheng Jue-Yan, Wang Li-Xiang, Wu Jing-Ying, Ye Hai-Qin, Wang Yan, Zheng Guo-Qing
Science and Technology Innovation Center, Guangzhou University of Chinese Medicine, Guangzhou, China.
Department of Neurology, The First Affiliated Hospital of Zhejiang Chinese Medical University (Zhejiang Provincial Hospital of Chinese Medicine), Hangzhou, China.
Front Pharmacol. 2025 Mar 31;16:1532290. doi: 10.3389/fphar.2025.1532290. eCollection 2025.
Chinese herbal medicine (CHM) formulas played an important role during the pandemic of coronavirus disease 2019 (COVID-19). Many randomized controlled trials (RCTs) on CHM for COVID-19 were quickly published. Concerns have been raised about their quality. In addition, inadequate detailed information on CHM formula intervention may arouse suspicion about their effectiveness. We aim to assess the most recent evidence of the methodological reporting quality of these RCTs with strict randomization, and the precise reporting of the CHM formula intervention.
RCTs on CHM formulas for COVID-19 were searched from nine databases. The CONSORT 2010, CONSORT-CHM Formulas 2017, and risk of bias were the guidelines used to assess the included RCTs. The checklist of sub-questions based on CONSORT-CHM Formulas 2017 was used to evaluate the precise reporting of CHM formula intervention. A comparison was made between RCTs that enrolled participants during and after the first wave of the pandemic (defined here as December 2019 to March 2020).
The average score for 66 studies evaluated based on three guidelines, the CONSORT 2010, the CONSORT-CHM Formulas 2017, and the checklist of sub-questions based on the CONSORT-CHM Formulas 2017, is 16.4, 15.2, and 17.2, respectively. The reporting rate of sample size calculation, allocation concealment, and blinding is less than 30%. The checklist of sub-questions based on the CONSORT-CHM formulas 2017 can help report and assess CHM formula intervention more precisely. Most studies assessed an "unclear risk of bias" due to insufficient information. RCTs published in English and recruited subjects during the first wave of the pandemic have a higher risk of participant blinding bias than the studies recruited subjects after that ( < 0.05).
The methodological reporting quality in strictly randomized RCTs on CHM formulas for COVID-19 is inadequate-the reporting of sample size calculation, allocation concealment, and blinding need to improve especially. The checklist of sub-questions based on CONSORT-CHM formulas 2017 can help report and assess CHM formula intervention more precisely. The methodological reporting quality of RCTs published in English and enrolled participants during the first wave of the pandemic is worse than the studies that recruited subjects after the first wave of the pandemic.
在2019年冠状病毒病（COVID-19）大流行期间，中药配方发挥了重要作用。许多关于中药治疗COVID-19的随机对照试验（RCT）迅速发表。人们对其质量提出了担忧。此外，关于中药配方干预的详细信息不足可能会引发对其有效性的怀疑。我们旨在评估这些严格随机化的RCT在方法学报告质量方面的最新证据，以及中药配方干预的精确报告情况。
从九个数据库中检索关于中药配方治疗COVID-19的RCT。使用CONSORT 2010、CONSORT-CHM配方2017和偏倚风险作为评估纳入RCT的指南。基于CONSORT-CHM配方2017的子问题清单用于评估中药配方干预的精确报告。对在疫情第一波期间（此处定义为2019年12月至2020年3月）和之后招募参与者的RCT进行了比较。
根据CONSORT 2010、CONSORT-CHM配方2017以及基于CONSORT-CHM配方2017的子问题清单这三个指南对66项研究进行评估，平均得分分别为16.4、15.2和17.2。样本量计算、分配隐藏和盲法的报告率低于30%。基于CONSORT-CHM配方2017的子问题清单有助于更精确地报告和评估中药配方干预。由于信息不足，大多数研究评估为“偏倚风险不明确”。在疫情第一波期间发表英文文章并招募受试者的RCT比之后招募受试者的研究存在更高的参与者盲法偏倚风险（<0.05）。
关于COVID-19的中药配方严格随机化RCT的方法学报告质量不足——样本量计算、分配隐藏和盲法的报告尤其需要改进。基于CONSORT-CHM配方2017的子问题清单有助于更精确地报告和评估中药配方干预。在疫情第一波期间发表英文文章并招募参与者的RCT的方法学报告质量比在第一波疫情之后招募受试者的研究更差。
