Remdesivir and systemic corticosteroids for the treatment of COVID-19: A Bayesian re-analysis - Todd C. Lee; Emily G. McDonald; Guillaume Butler-Laporte; Luke B. Harrison; Matthew P. Cheng; James M. Brophy (International Journal of Infectious Diseases, 2021)
Background: The global death toll from coronavirus disease 2019 (COVID-19) has exceeded 2 million, and treatments to decrease mortality are needed urgently. Objectives: To examine the probabilities of a clinically meaningful reduction in mortality for remdesivir and systemic corticosteroids. Design, setting and participants: This was a probabilistic re-analysis of clinical trial data for corticosteroids and remdesivir in the treatment of hospitalized patients with COVID-19 using a Bayesian random effects meta-analytic approach. Studies were identified from existing meta-analyses performed by the World Health Organization. Main outcomes and measures: Posterior probabilities of an absolute decrease in mortality compared with control patients, by subgroups based on oxygen requirements, were calculated for corticosteroids and remdesivir. Probabilities of ≥1%, ≥2% and ≥5% absolute decrease in mortality were quantified. Results: For patients needing mechanical ventilation, the probability of ≥1% absolute decrease in mortality was 4% for remdesivir and 93% for corticosteroids. For patients needing supplemental oxygen without mechanical ventilation, the probability of ≥1% absolute decrease in mortality was 81% for remdesivir and 93% for dexamethasone. Finally, for patients who did not need oxygen support, the probability of ≥1% absolute decrease in mortality was 29% for remdesivir and 4% for dexamethasone. Conclusions and relevance: Using a Bayesian analytic approach, remdesivir had low probability of achieving a clinically meaningful reduction in mortality, except for patients needing supplemental oxygen without mechanical ventilation. Corticosteroids were more promising for patients needing oxygen support, especially mechanical ventilation. While awaiting more definitive studies, this probabilistic interpretation of the evidence will help to guide treatment decisions for clinicians, as well as guideline and policy makers.