Clinical evidence of three traditional Chinese medicine drugs and three herbal formulas for COVID-19: A systematic review and meta-analysis of the Chinese population.

BACKGROUND: The coronavirus disease 2019 (COVID-19) continues to spread worldwide. Integrated Chinese and Western medicine have had some successes in treating COVID-19. OBJECTIVE: This study aims to evaluate the efficacy and safety of three traditional Chinese medicine drugs and three herbal formulas (3-drugs-3-formulas) in patients with COVID-19. SEARCH STRATEGY: Relevant studies were identified from 12 electronic databases searched from their establishment to April 7, 2022. INCLUSION CRITERIA: Randomized controlled trials (RCTs), non-RCTs and cohort studies that evaluated the effects of 3-drugs-3-formulas for COVID-19. The treatment group was treated with one of the 3-drugs-3-formulas plus conventional treatment. The control group was treated with conventional treatment. DATA EXTRACTION AND ANALYSIS: Two evaluators screened and selected literature independently, then extracted basic information and assessed risk of bias. The treatment outcome measures were duration of main symptoms, hospitalization time, aggravation rate and mortality. RevMan 5.4 was used to analyze the pooled results reported as mean difference (MD) with 95% confidence interval (CI) for continuous data and risk ratio (RR) with 95% CI for dichotomous data. RESULTS: Forty-one studies with a total of 13,260 participants were identified. Our analysis suggests that compared with conventional treatment, the combination of 3-drugs-3-formulas might shorten duration of fever (MD = -1.39; 95% CI: -2.19 to -0.59; P < 0.05), cough (MD = -1.57; 95% CI: -2.16 to -0.98; P < 0.05) and fatigue (MD = -1.36; 95% CI: -2.21 to -0.51; P < 0.05), decrease length of hospital stay (MD = -2.62; 95% CI -3.52 to -1.72; P < 0.05), the time for nucleic acid conversion (MD = -2.92; 95% CI: -4.26 to -1.59; P < 0.05), aggravation rate (RR = 0.49; 95% CI: 0.38 to 0.64; P < 0.05) and mortality (RR = 0.34; 95% CI: 0.19 to 0.62; P < 0.05), and increase the recovery rate of chest computerized tomography manifestations (RR = 1.22; 95% CI: 1.14 to 1.3; P < 0.05) and total effectiveness (RR = 1.24; 95% CI: 1.09 to 1.42; P < 0.05). CONCLUSION: The 3-drugs-3-formulas can play an active role in treating all stages of COVID-19. No severe adverse events related to 3-drugs-3-formulas were observed. Hence, 3-drugs-3-formulas combined with conventional therapies have effective therapeutic value for COVID-19 patients. Further long-term high-quality studies are essential to demonstrate the clinical benefits of each formula. Please cite this article as: You LZ, Dai QQ, Zhong XY, Yu DD, Cui HR, Kong YF, Zhao MZ, Zhang XY, Xu QQ, Guan ZY, Wei XX, Zhang XC, Han SJ, Liu WJ, Chen Z, Zhang XY, Zhao C, Jin YH, Shang HC. Clinical evidence of three traditional Chinese medicine drugs and three herbal formulas for COVID-19: A systematic review and meta-analysis of the Chinese population. J Integr Med. 2023; 21(5): 441-454.

Evaluating the methodology of studies conducted during the global COVID-19 pandemic: A systematic review of randomized controlled trials.

BACKGROUND: The therapeutic evidence collected from well-designed studies is needed to help manage the global pandemic of the coronavirus disease 2019 (COVID-19). Evaluating the quality of therapeutic data collected during this most recent pandemic is important for improving future clinical research under similar circumstances. OBJECTIVE: To assess the methodological quality and variability in implementation of randomized controlled trials (RCTs) for treating COVID-19, and to analyze the support that should be provided to improve data collected during an urgent pandemic situation. SEARCH STRATEGY: PubMed, Excerpta Medica Database, China National Knowledge Infrastructure, Wanfang, and Chongqing VIP, and the preprint repositories including Social Science Research Network and MedRxiv were systematically searched, up to September 30, 2020, using the keywords "coronavirus disease 2019 (COVID-19)," "2019 novel coronavirus (2019-nCoV)," "severe acute respiratory syndrome-related coronavirus-2 (SARS-CoV-2)," "novel coronavirus pneumonia (NCP)," "randomized controlled trial (RCT)" and "random." INCLUSION CRITERIA: RCTs studying the treatment of COVID-19 were eligible for inclusion. DATA EXTRACTION AND ANALYSIS: Screening of published RCTs for inclusion and data extraction were each conducted by two researchers. Analysis of general information on COVID-19 RCTs was done using descriptive statistics. Methodological quality was assessed using the risk-of-bias tools in the Cochrane Handbook for Systematic Reviews of Interventions (Version 5.1.0). Variability in implementation was assessed by comparing consistency between RCT reports and registration information. RESULTS: A total of 5886 COVID-19 RCTs were identified. Eighty-one RCTs were finally included, of which, 45 had registration information. Methodological quality of the RTCs was not optimal due to deficiencies in five main domains: allocation concealment, blinding of participants and personnel, blinding of outcome assessment, incomplete outcome data, and selective reporting. Comparisons of consistency between published protocols and registration information showed that the 45 RCTs with registration information had common deviations in seven items: inclusion and exclusion criteria, sample size, outcomes, research sites of recruitment, interventions, and blinding. CONCLUSION: The methodological quality of COVID-19 RCTs conducted in early to mid 2020 was consistently low and variability in implementation was common. More support for implementing high-quality methodology is needed to obtain the quality of therapeutic evidence needed to provide positive guidance for clinical care. We make an urgent appeal for accelerating the construction of a collaborative sharing platform and preparing multidisciplinary talent and professional teams to conduct excellent clinical research when faced with epidemic diseases of the future. Further, variability in RCT implementation should be clearly reported and interpreted to improve the utility of data resulting from those trials.