Interleukin-6 blocking agents for treating COVID-19: a living systematic review.

Background Different forms of vaccines have been developed to prevent the SARS‐CoV‐2 virus and subsequent COVID‐19 disease. Several are in widespread use globally. Objectives To assess the efficacy and safety of COVID‐19 vaccines (as a full primary vaccination series or a booster dose) against SARS‐CoV‐2. Search methods We searched the Cochrane COVID‐19 Study Register and the COVID‐19 L·OVE platform (last search date 5 November 2021). We also searched the WHO International Clinical Trials Registry Platform, regulatory agency websites, and Retraction Watch. Selection criteria We included randomized controlled trials (RCTs) comparing COVID‐19 vaccines to placebo, no vaccine, other active vaccines, or other vaccine schedules. Data collection and analysis We used standard Cochrane methods. We used GRADE to assess the certainty of evidence for all except immunogenicity outcomes. We synthesized data for each vaccine separately and presented summary effect estimates with 95% confidence intervals (CIs). Main results We included and analyzed 41 RCTs assessing 12 different vaccines, including homologous and heterologous vaccine schedules and the effect of booster doses. Thirty‐two RCTs were multicentre and five were multinational. The sample sizes of RCTs were 60 to 44,325 participants. Participants were aged: 18 years or older in 36 RCTs; 12 years or older in one RCT; 12 to 17 years in two RCTs; and three to 17 years in two RCTs. Twenty‐nine RCTs provided results for individuals aged over 60 years, and three RCTs included immunocompromized patients. No trials included pregnant women. Sixteen RCTs had two‐month follow‐up or less, 20 RCTs had two to six months, and five RCTs had greater than six to 12 months or less. Eighteen reports were based on preplanned interim analyses. Overall risk of bias was low for all outcomes in eight RCTs, while 33 had concerns for at least one outcome. We identified 343 registered RCTs with results not yet available. This abstract reports results for the critical outcomes of confirmed symptomatic COVID‐19, severe and critical COVID‐19, and serious adverse events only for the 10 WHO‐approved vaccines. For remaining outcomes and vaccines, see main text. The evidence for mortality was generally sparse and of low or very low certainty for all WHO‐approved vaccines, except AD26.COV2.S (Janssen), which probably reduces the risk of all‐cause mortality (risk ratio (RR) 0.25, 95% CI 0.09 to 0.67; 1 RCT, 43,783 participants; high‐certainty evidence). Confirmed symptomatic COVID‐19 High‐certainty evidence found that BNT162b2 (BioNtech/Fosun Pharma/Pfizer), mRNA‐1273 (ModernaTx), ChAdOx1 (Oxford/AstraZeneca), Ad26.COV2.S, BBIBP‐CorV (Sinopharm‐Beijing), and BBV152 (Bharat Biotect) reduce the incidence of symptomatic COVI...

Interleukin-1 blocking agents for treating COVID-19 (Review)

Background Researchers worldwide are actively engaging in research activities to search for preventive and therapeutic interventions against COVID-19. Our aim was to describe the planning of randomized controlled trials (RCTs) in terms of timing related to the course of the COVID-19 epidemic and research question evaluated. Method We performed a living mapping of RCTs registered in the WHO International Clinical Trials Registry Platform. We systematically search the platform every week for all RCTs evaluating preventive interventions and treatments for COVID-19 and created a publicly available interactive mapping tool at https://covid-nma.com to visualize all trials registered. Results By August 12, 2020, 1,568 trials for COVID-19 were registered worldwide. Overall, the median ([Q1-Q3]; range) delay between the first case recorded in each country and the first RCT registered was 47 days ([33-67]; 15-163). For the 9 countries with the highest number of trials registered, most trials were registered after the peak of the epidemic (from 100% trials in Italy to 38% in the United States). Most trials evaluated treatments (1,333 trials; 85%); only 223 (14%) evaluated preventive strategies and 12 post-acute period intervention. A total of 254 trials were planned to assess different regimens of hydroxychloroquine with an expected sample size of 110,883 patients. Conclusion This living mapping analysis showed that COVID-19 trials have relatively small sample size with certain redundancy in research questions. Most trials were registered when the first peak of the pandemic have passed.

Summary Background In reported systematic reviews and meta‐analyses of randomized controlled trials (RCTs) assessing treatments for psoriasis, the proportion of serious adverse events (SAEs) did not differ between treatments and placebo. Including cases of psoriasis worsening as SAEs may explain the lack of difference. Objectives This systematic review and meta‐analysis aimed to explore this possibility. Methods Among the 140 RCTs included in the Living Network Cochrane Review (last search on 8 May 2019), we selected those comparing a biologic treatment against placebo. The primary outcome was the numbers of SAEs in the treatment and placebo arms after excluding cases of psoriasis worsening. Secondary outcomes were the number of adverse events (AEs) of special interest. The trial was registered on PROSPERO (CRD42019124495). Results We analysed 51 RCTs. Of these, 21 included at least one anti‐tumour necrosis factor (TNF)‐α arm, 15 one anti‐interleukin (IL)‐17 arm, 11 one anti‐IL‐23 arm and nine one anti‐IL‐12/23 arm. With cases of psoriasis worsening included, the risk of occurrence of SAEs between biologic treatments and placebo did not differ: risk ratio (RR) 1·09, 95% confidence interval (CI) 0·88–1·36. After excluding cases of psoriasis worsening, the RR became significant (RR 1·30, 95% CI 1·02–1·65). By drug class, the RRs were for anti‐TNF‐α, 1·68 (95% CI 1·11–2·54; no missing data); anti‐IL‐17, 1·28 (95% CI 0·88–1·85; no missing data); anti‐IL‐23, 0·95 (95% CI 0·59–1·52; no missing data) and anti‐IL‐12/23, 1·18 (95% CI 0·72–1·94; no missing data). We were unable to examine potential differences in AEs of special interest between biologic treatments and placebo arms because of the small number of events. Conclusions On excluding cases of worsening psoriasis, the risk of occurrence of SAEs is higher in the biologic than in the placebo arm. Given the rare events, we could not highlight whether this higher risk of SAEs was related to AEs of special interest. Reporting of SAEs in clinical trials has to be changed to provide more transparency through the separate reporting of disease flares leading to hospital admission and other SAEs.

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Network meta-analysis (NMA) of rare events has attracted little attention in the literature. Until recently, networks of interventions with rare events were analyzed using the inverse-variance NMA approach. However, when events are rare the normal approximations made by this model can be poor and effect estimates are potentially biased. Other methods for the synthesis of such data are the recent extension of the Mantel-Haenszel approach to NMA or the use of the noncentral hypergeometric distribution. In this article, we suggest a new common-effect NMA approach that can be applied even in networks of interventions with extremely low or even zero number of events without requiring study exclusion or arbitrary imputations. Our method is based on the implementation of the penalized likelihood function proposed by Firth for bias reduction of the maximum likelihood estimate to the logistic expression of the NMA model. A limitation of our method is that heterogeneity cannot be taken into account as an additive parameter as in most meta-analytical models. However, we account for heterogeneity by incorporating a multiplicative overdispersion term using a two-stage approach. We show through simulation that our method performs consistently well across all tested scenarios and most often results in smaller bias than other available methods. We also illustrate the use of our method through two clinical examples. We conclude that our "penalized likelihood NMA" approach is promising for the analysis of binary outcomes with rare events especially for networks with very few studies per comparison and very low control group risks.

"Living" evidence synthesis is of primary interest for decision-makers to overcome the COVID-19 pandemic. The COVID-NMA provides open-access living meta-analyses assessing different therapeutic and preventive interventions. Data are posted on a platform (https://covid-nma.com/) and analyses are updated every week. However, guideline developers and other stakeholders also need to investigate the data and perform their own analyses. This requires resources, time, statistical expertise, and software knowledge. To assist them, we created the "metaCOVID" application which, based on automation processes, facilitates the fast exploration of the data and the conduct of analyses tailored to end-users needs. metaCOVID has been created in R and is freely available as an R-Shiny application. The application conducts living meta-analyses for every outcome. Several options are available for subgroup and sensitivity analyses. The results are presented in downloadable forest plots. metaCOVID is freely available from https://covid-nma.com/metacovid/ and the source code from https://github.com/TEvrenoglou/metaCovid.