Dataset Information

Remdesivir and systemic corticosteroids for the treatment of COVID-19: A Bayesian re-analysis.

ABSTRACT:

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.

SUBMITTER: Lee TC

PROVIDER: S-EPMC7849442 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:PurposeCoronavirus disease-2019 (COVID-19) continues to be a global threat and remains a significant cause of hospitalizations. Recent clinical guidelines have supported the use of corticosteroids or remdesivir in the treatment of COVID-19. However, uncertainty remains about which patients are most likely to benefit from treatment with either drug; such knowledge is crucial for avoiding preventable adverse effects, minimizing costs, and effectively allocating resources. This study presents a machine-learning system with the capacity to identify patients in whom treatment with a corticosteroid or remdesivir is associated with improved survival time.MethodsGradient-boosted decision-tree models used for predicting treatment benefit were trained and tested on data from electronic health records dated between December 18, 2019, and October 18, 2020, from adult patients (age ≥18 years) with COVID-19 in 10 US hospitals. Models were evaluated for performance in identifying patients with longer survival times when treated with a corticosteroid versus remdesivir. Fine and Gray proportional-hazards models were used for identifying significant findings in treated and nontreated patients, in a subset of patients who received supplemental oxygen, and in patients identified by the algorithm. Inverse probability-of-treatment weights were used to adjust for confounding. Models were trained and tested separately for each treatment.FindingsData from 2364 patients were included, with men comprising slightly more than 50% of the sample; 893 patients were treated with remdesivir, and 1471 were treated with a corticosteroid. After adjustment for confounding, neither corticosteroids nor remdesivir use was associated with increased survival time in the overall population or in the subpopulation that received supplemental oxygen. However, in the populations identified by the algorithms, both corticosteroids and remdesivir were significantly associated with an increase in survival time, with hazard ratios of 0.56 and 0.40, respectively (both, P = 0.04).ImplicationsMachine-learning methods have the capacity to identify hospitalized patients with COVID-19 in whom treatment with a corticosteroid or remdesivir is associated with an increase in survival time. These methods may help to improve patient outcomes and allocate resources during the COVID-19 crisis.