key: cord-0740220-myenxr4j
authors: Cheng, Qinglin; Zhao, Gang; Chen, Junfang; Jia, Qingjun; Fang, Zijian
title: Efficacy and safety of current treatment interventions for patients with severe COVID‐19 infection: A network meta‐analysis of randomized controlled trials
date: 2021-12-18
journal: J Med Virol
DOI: 10.1002/jmv.27512
sha: 528d87cc1c13c9cb4f40931eb320e416d800f674
doc_id: 740220
cord_uid: myenxr4j

This study aimed to assess the efficacy and safety of different medications available at present for severe coronavirus disease 2019 (COVID‐19) infection. We searched databases for randomized controlled trials (RCTs) published up to April 30, 2021, with Chinese or English language restriction, of medications recommended for patients (aged 18 years or older) with severe COVID‐19 infection. We extracted data on trials and patient characteristics, and the following primary outcomes: all‐cause mortality (ACM), and treatment‐emergent adverse events (TEAEs). We identified 1855 abstracts and of these included 15 RCTs comprising 3073 participants through database searches and other sources. In terms of efficacy, compared with the standard of care (SOC) group, no significant decrease in ACM was found in α‐lipoic acid, convalescent plasma (CP), azithromycin, tocilizumab, methylprednisolone, interferon beta, CP/SOC, high dosage sarilumab, low dosage sarilumab, remdesivir, lopinavir–ritonavir, auxora, and placebo group. Compared with placebo, we found that a significant decrease in ACM was only found in methylprednisolone (odds ratio [OR]: 0.16, 95% confidence interval [CI]: 0.03–0.75]. With respect to TEAEs, the CP group showed lower TEAEs than placebo (OR: 0.07, 95% CI: 0.01–0.58) or SOC (OR: 0.05, 95% CI: 0.01–0.42) group for the therapy of severe COVID‐19 patients. This study only demonstrated that methylprednisolone was superior to placebo in treating patients with severe COVID‐19 infection. Meanwhile, this further confirmed that the safety of other treatment interventions might be inferior to CP for the therapy of severe COVID‐19 patients.

Currently, coronavirus disease 2019 is the leading cause of the global burden of disease and public health, and this burden has substantially increased since 2019, largely driven by cases growth and mortality. 1 So far, the World Health Organization (WHO) has confirmed more than 160 million cases. 2 The mortality in patients with was estimated at 2.08%. 2 Due to its high morbidity and high impact on the COVID-19 infection, which is one of the most challenging problems adversely affecting public health and human security worldwide. 1 Antiviral drugs were the most frequently used treatment and showed efficacy in patients with COVID-19. 3 Besides antiviral drugs, there were other pharmacological options for the treatment of COVID-19, for example, convalescent plasma (CP), monoclonal antibody, and hormone drugs. [4] [5] [6] However, there were many compounds that differ in efficacy and safety, and which was the "best" medication for severe COVID-19 infection was still unclear.

Due to small effect sizes in the single-arm meta-analysis in patients with COVID-19, there was also a debate about the efficacy and safety of medications and some results of studies were contradictory. 3 Most of the previous reviews mainly focused on the medications for COVID-19 patients with all infection levels. 7 There was a critical shortage for an analysis stratified by different infection levels (i.e., mild, moderate, and severe infection) in medications of COVID-19. Patients with COVID-19 differ substantially from the infection levels, and such differences should lead to individual treatment of this sensitive subgroup.

Although the network metaanalysis (NMA) has been done in previous studies, 8 infection. Full search strategies were listed in Appendix S1. We extracted data on RCTs, patient and medications characteristics (Table S1 ).

Data were extracted via the search strategy by at least two independent investigators. We reviewed potentially relevant articles' abstracts and full-texts for eligibility. We selected articles for the assessment according to the criterion: At least one statistical analysis of the association between severe COVID-19 and treatments was presented and described as an estimate for efficacy and safety.

Additional relevant research identified was manually retrieved.

We included the RCTs, of at least 1 week's duration, that enrolled confirmed patients (aged ≥18 years) with severe COVID-19 infection according to the COVID-19 laboratory diagnostics of WHO. 11 All RCTs studies that measured the efficacy or safety between medications and severe COVID-19 infection were considered for inclusion. Full inclusion and exclusion criteria were listed in the appendix (Appendix S2). We resolved any ambiguity through mutual discussion and consensus during selecting eligible studies.

Two reviewers (J. Q. J. and F. Z. J.) independently worked for data extraction and entered onto all data by using a standardized form. The main data extracted were estimates of efficacy and safety. We collected the following information: (1) basic characteristics, including author name, publication year, country/countries of origin, study design, method of COVID-19 testing, patient population, sample size (SS), interventions, treatment medication dose, controls, control medication dose, follow-up time, and primary outcomes; (2) primary outcomes, including all-cause mortality (ACM) and rate of treatment-emergent adverse events (TEAEs). We also contacted corresponding authors for breakdowns of the above data between severe COVID-19-infected patients and therapies if this information was not reported in the published article. One reviewer undertook the initial extraction of studies, and another reviewed the extraction. Differences were discussed, and a third investigator (C. Q. L.) was involved if consensus was not reached.

At least two reviewers (C. Q. L., J. Q. J., C. J. F., and F. Z. J.) estimated the risk of bias for all study designs. We assessed the risk of bias with the Cochrane Risk-of-Bias Tool. 12 We evaluated the certainty of evidence by using the Grading of Recommendations Assessment, Development, and Evaluation approach for the NMA. 13 

The primary outcomes were the ACM and TEAEs in patients with severe COVID-19 infection, from the beginning of the intervention to the end of follow-up. The ACM referred to the proportion of death due to any cause from treatment initiation to end of follow-up in severe COVID-19 patients. The TEAEs ratio reported as the proportion of any TEAEs for severe COVID-19 infection from the beginning to the end of medications. The severe COVID-19 infection represented cases with fever or suspected respiratory infection, plus one of the following: respiratory rate >30 breaths/min, severe respiratory distress, or SpO 2 ≤ 93% on room air. 14 2.5 | Data analysis 2.5.1 | Network meta-analysis Network meta-analyses were performed using STATA statistical software (Version 15; Stata Corporation). Binary variables (ACM and TEAEs) were analyzed using odds ratio (OR) with 95% confidence interval (CI). Additional details were reported in the appendix (Appendix S3). Statistical significance was defined as p < 0.05.

We merged simultaneously the direct evidence and indirect evidence or different indirect evidence through an NMA. In NMA, the group-level data were used to analyze the effect of the intervention.

The pooled effect size was measured by using the Z-test. We synthesized the effect sizes of NMA using a fixed-effect model (i.e., I 2 ≤ 50%) or a random-effect model (i.e., I 2 > 50%). The surface under the cumulative ranking area (SUCRA) curve and mean ranks were used to rank the therapies for each outcome. 15 Furthermore, the endpoints which lower was better would indicate Rank 1 was best and Rank N was worst from figures and vice versa.

To reveal the disagreement of unequal evidence sources, we used statistical inconsistency to evaluate it by using local and global approaches. 16 The node splitting method, which split evidence on a specific comparison into direct and indirect evidence, was used to assess the inconsistency of the NMA. 16 No significant inconsistency existed in outcomes if p > 0.05. Moreover, the smallstudy effect was estimated by using funnel plots in this NMA. 16 

We estimated the risk of bias of included studies using the Cochrane Collaboration's Tool for Assessing Risk of Bias, 12 classifying the risk of bias as high, unclear, or low ( Figure 2 ). We used comparison-adjusted funnel plots to evaluate publication bias.

We hypothesized that the inclusion of various study designs and populations might contribute to heterogeneity and inconsistency.

Sensitivity analyses were performed to assess the effect of our conclusions. We analyzed the data with the following restrictions: a multicenter study (MS), duration of study (DS), blinding, crossover design, SS, industry sponsorship, and risk of reported bias (RRB).

We used the netmeta package in R (version 4.0.5) to duplicate the NMA of the primary outcomes. 

In total, included RCTs was generally good quality and the risk of bias summary was shown in Figure 2 and Table S2 .

In the network of connected RCTs (Figure 3 ), the width of the lines corresponded to the number of trials included each treatment comparison. From Figure 3 we could see that the result of this network was well connected. As shown in Figure 3A In Figure 3B there was also a direct connection between SOC and CP, LPV/r, IFN-β, and AZM, or between placebo and HS or LS.

Several indirect connections were available between SOC and sarilumab, placebo ( Figure 3B ). CHENG ET AL. | 1619

Fourteen studies [17] [18] [19] [20] [21] [22] [23] [24] [25] [26] [27] [28] [29] [30] reported ACM as outcome measurement (Table S3) 

In the safety outcome, the data from seven studies, 17, 18, 20, 23, 26, 28, 30 were merged for analysis (Table S4) 

According to the node-slitting analysis (Table S5) , no significant inconsistency or qualitative difference was available for the ACM and TEAEs. Thus, the consistency hypothesis was accepted in this NMA.

We analyzed the potential sources of heterogeneity or inconsistency by using subgroup and meta-regression analyses. Univariable in clinical practice. 31 We only evaluated the efficacy and safety of medications for COVID-19 infection based on randomized placebocontrolled trials from our previous study. 32 However, the efficacy and safety of medications based on the control of placebo group or the control of SOC group still need further clarification. In view of this, we performed this NMA study. We found that medications seemed to vary in ACM between the controls of placebo and SOC for severe COVID-19 patients.

Interestingly, we only found that methylprednisolone was more efficacious than placebo for decreasing the ACM of severe COVID-19 infection in all 15 treatment interventions of this study. This study supports evidence from previous observations. 23,33 As we know, prolonged glucocorticoid treatment is associated with improved outcomes of acute respiratory distress syndrome (ARDS). 34 Several reports have also shown that treatment with methylprednisolone could significantly reduce the risk of death among patients with ARDS. 35 However, the present finding seemed to be inconsistent with our previous report. 32 Juul et al. 36 were associated with decreased ACM in patients with COVID-19. 18, 19, 21, 22, 37 It is difficult to explain this result, but it might be related to the difference of participants selection in differential studies. 38 For instance, we selected subjects with all infection levels, and were inadequate for the analyses stratified by different infection levels (i.e., nonsevere and severe infection) in medications of COVID-19. [7] [8] [9] Previous studies suggested that patients with COVID-19 differ from the infection levels often leading to different outcomes of treatment. 39 While no significant decrease in ACM was found between SOC and 14 other medications or placebo for severe COVID-19 infection. This result seems to be inconsistent with prior NMA studies by Zhang et al., 40 Wu et al., 41 and Siemieniuk et al. 42 They suggested that tocilizumab or corticosteroids might reduce the ACM compared with SOC for COVID-19 infection. A possible explanation for this was that we compared the ACM of SOC, which existed the bias due to the differential SOC of every country (i.e., the SOC is not standardized) except for the reasons given above. [19] [20] [21] [22] The present study raised the possibility that our findings might 

In terms of safety, we summarized the TEAEs. We only found that the CP group showed lower TEAEs than the placebo or SOC group. As mentioned in previous literature reviews, 43 

This study was constrained by some limitations. First, included studies might be small in this NMA, which should be considered when interpreting the findings. Second, the published data we extracted included only two types of outcomes, some important outcomes such as discharge ratio, clinical improvement ratio, and the ratio of virological cure were not analyzed. Additionally, we also did not eval- 

The authors thank all study authors who responded to our data requests. The authors also thank the numerous researchers who sent information for our previous reviews on which this report was built. 

The authors declare that there are no conflict of interests. 

The data that supports the findings of this study are available in the Supporting Information Material of this article.

https://orcid.org/0000-0003-2899-7626

Gang Zhao http://orcid.org/0000-0002-5564-911X

Rethinking pandemic preparation: Global Health Security Index (GHSI) is predictive of COVID-19 burden, but in the opposite direction

World Health Organization. WHO coronavirus (COVID-19) dashboard. Geneva: WHO; 2020

Repurposed antiviral drugs for Covid-19-interim WHO solidarity trial results

Randomized controlled trial of convalescent plasma therapy against standard therapy in patients with severe COVID-19 disease

A neutralizing monoclonal antibody for hospitalized patients with Covid-19

Growth hormone therapy at the time of Covid-19 pandemic: adherence and drug supply issues

Drug-drug interactions of antithrombotic medications during treatment of COVID-19

Efficacy and safety of remdesivir in hospitalized Covid-19 patients: systematic review and meta-analysis including network meta-analysis [published online ahead of print

Randomised controlled trials for COVID-19: evaluation of optimal randomisation methodologies-need for data validation of the completed trials and to improve ongoing and future randomised trial designs

Insights from compassionate use of tocilizumab for COVID-19 to inform appropriate design of randomised controlled trials

Laboratory testing for coronavirus disease (COVID-19) in suspected human cases: interim guidance

The Cochrane Collaboration's tool for assessing risk of bias in randomised trials

The Eastern Association of the surgery of trauma approach to practice management guideline development using Grading of Recommendations, Assessment, Development, and Evaluation (GRADE) methodology

Hydroxychloroquine in the treatment of COVID-19: a multicenter randomized controlled study

Approaches to interpreting and choosing the best treatments in network meta-analyses

Network meta-analysis: an introduction for clinicians

Convalescent plasma for COVID-19: a multicenter, randomized clinical trial

A trial of lopinavir-ritonavir in adults hospitalized with severe Covid-19

A randomized clinical trial of the efficacy and safety of interferon β-1a in treatment of severe COVID-19

Intravenous methylprednisolone pulse as a treatment for hospitalised severe COVID-19 patients: results from a randomised controlled clinical trial

Azithromycin in addition to standard of care versus standard of care alone in the treatment of patients admitted to the hospital with severe COVID-19 in Brazil (COALITION II): a randomised clinical trial

Sarilumab in patients admitted to hospital with severe or critical COVID-19: a randomised, double-blind, placebo-controlled, phase 3 trial

Effect of convalescent plasma therapy on time to clinical improvement in patients with severe and lifethreatening COVID-19: a randomized clinical trial

Auxora versus standard of care for the treatment of severe or critical COVID-19 pneumonia: results from a randomized controlled trial

Remdesivir for severe COVID-19 versus a cohort receiving standard of care

Interferon β-1b in treatment of severe COVID-19: a randomized clinical trial

The therapeutic potential of convalescent plasma therapy on treating critically-ill COVID-19 patients residing in respiratory care units in hospitals in Baghdad

A randomized trial of convalescent plasma in Covid-19 severe pneumonia

Effect of tocilizumab on clinical outcomes at 15 days in patients with severe or critical coronavirus disease 2019: randomised controlled trial

A randomized, single-blind, group sequential, active-controlled study to evaluate the clinical efficacy and safety of α-Lipoic acid for critically ill patients with coronavirus disease 2019 (COVID-19)

Efficacy and safety of current therapeutic options for COVID-19-lessons to be learnt from SARS and MERS epidemic: a systematic review and meta-analysis

Efficacy and safety of current medications for treating severe and non-severe COVID-19 patients: an updated network meta-analysis of randomized placebocontrolled trials

Efficacy and safety of convalescent plasma for severe COVID-19 based on evidence in other severe respiratory viral infections: a systematic review and metaanalysis

Prolonged glucocorticoid treatment is associated with improved ARDS outcomes: analysis of individual patients' data from four randomized trials and trial-level meta-analysis of the updated literature

Risk factors associated with acute respiratory distress syndrome and death in patients with coronavirus disease

Interventions for treatment of COVID-19: second edition of a living systematic review with metaanalyses and trial sequential analyses (The LIVING Project)

A randomized double-blind controlled trial of convalescent plasma in adults with severe COVID-19

Choosing the right medicare prescription drug plan: the effect of age, strategy selection, and choice set size

Dynamic antibody responses in patients with different severity of COVID-19: a retrospective study

Efficacy of COVID-19 treatments: a Bayesian network meta-analysis of randomized controlled trials

Comparative effectiveness and safety of 32 pharmacological interventions recommended by guidelines for coronavirus disease 2019: a systematic review and network meta-analysis combining 66 trials

Drug treatments for covid-19: living systematic review and network meta-analysis

Collecting and evaluating convalescent plasma for COVID-19 treatment: why and how? Vox Sang

Assessment of the safety and therapeutic benefits of convalescent plasma in COVID-19 treatment: a systematic review and meta-analysis

A potentially effective treatment for COVID-19: a systematic review and meta-analysis of convalescent plasma therapy in treating severe infectious disease

Safety update: COVID-19 convalescent plasma in 20,000 hospitalized patients

Early safety indicators of COVID-19 convalescent plasma in 5000 patients

Impaired type I interferon activity and inflammatory responses in severe COVID-19 patients

A comprehensive review on sarilumab in COVID-19

Efficacy and safety of current treatment interventions for patients with severe COVID-19 infection: a network meta-analysis of randomized controlled trials