Comparative efectiveness of neutralising monoclonal antibodies in high risk COVID‑19 patients: a Bayesian network meta‑analysis
The purpose of this work was to review and synthesise the evidence on the comparative efectiveness of neutralising monoclonal antibody (nMAB) therapies in individuals exposed to or infected with SARS-CoV-2 and at high risk of developing severe COVID-19. Outcomes of interest were mortality, healthcare utilisation, and safety. A rapid systematic review was undertaken to identify and synthesise relevant RCT evidence using a Bayesian Network Meta-Analysis. Relative treatment efects for individual nMABs (compared with placebo and one another) were estimated. Pooled efects for the nMAB class compared with placebo were estimated. Relative efects were combined with baseline natural history models to predict the expected risk reductions per 1000 patients treated. Eight articles investigating four nMABs (bamlanivimab, bamlanivimab/etesevimab, casirivimab/ imdevimab, sotrovimab) were identifed. All four therapies were associated with a statistically signifcant reduction in hospitalisation (70–80% reduction in relative risk; absolute reduction of 35–40 hospitalisations per 1000 patients). For mortality, ICU admission, and invasive ventilation, the risk was lower for all nMABs compared with placebo with moderate to high uncertainty due to small event numbers. Rates of serious AEs and infusion reactions were comparable between nMABs and placebo. Pairwise comparisons between nMABs were typically uncertain, with broadly comparable efcacy. In conclusion, nMABs are efective at reducing hospitalisation among infected individuals at high-risk of severe COVID-19, and are likely to reduce mortality, ICU admission, and invasive ventilation rates; the efect on these latter outcomes is more uncertain. Widespread vaccination and the emergence of nMAB-resistant variants make the generalisability of these results to current patient populations difcult.
History
Publication
Science Reports, 12, 17561Also affiliated with
- Health Research Institute (HRI)
- MACSI - Mathematics Application Consortium for Science & Industry
External identifier
Department or School
- Mathematics & Statistics