Efficacy and safety of Lianhuaqingwen for mild or moderate coronavirus disease 2019: A meta-analysis of randomized controlled trials

Abstract

Background: Coronavirus disease 2019 (COVID-19) is an emerging and rapidly evolving disease, with no recommended effective anti-coronavirus treatments. Traditional Chinese Medicine (TCM) has been widely used to treat COVID-19 in China, and the most used one is Lianhuaqingwen (LH). This study aimed to assess the efficacy and safety of LH combined with usual treatment vs usual treatment alone in treating mild or moderate COVID-19 by a meta-analysis of randomized controlled trials (RCTs).Methods and analysis: We systematically searched the Medline (OVID), Embase, the Cochrane Library, and 4 Chinese databases from inception to July 2020 to include the RCTs that evaluated the efficacy and safety of LH in combination with usual treatment vs usual treatment for mild or moderate COVID-19. A meta-analysis was performed to calculate the risk ratio (RR) and 95% confidence interval (CI) for binary outcomes and mean difference (MD) for continuous outcomes.Results: A total of 5 RCTs with 824 individuals with mild or moderate COVID 19 were included. Compared with the usual treatment alone, LH in combination with usual treatment significantly improved the overall clinical efficacy (RR = 2.39, 95% CI 1.61-3.55), increased the rate of recovery of chest computed tomographic manifestations (RR = 1.80, 95% CI 1.08-3.01), reduced the rate of conversion to severe cases (RR = 0.47, 95% CI 0.29-0.74), shorten the duration of fever (MD = -1.00, 95% CI -1.17 to -0.84). Moreover, LH in combination with usual treatment did not increase the occurrence of the adverse event compared to usual treatment alone.Conclusion: Our meta-analysis of RCTs indicated that LH in combination with usual treatment may improve the clinical efficacy in patients with mild or moderate COVID-19 without increasing adverse events. However, given the limitations and poor quality of included trials in this study, further large-sample RCTs or high-quality real-world studies are needed to confirm our conclusions.