Fluvoxamine for Outpatient COVID-19 to Prevent Hospitalization: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Abstract Importance Widely available and affordable options for the outpatient management of COVID-19 are needed, particularly therapies that prevent hospitalization. Objective Perform a meta-analysis of the available randomized clinical trial evidence for fluvoxamine in the outpatient management of COVID-19. Data Sources World Health Organization International Clinical Trials Registry Platform and ClinicalTrials.gov. Study Selection Completed outpatient trials with available results which compared fluvoxamine to placebo. Data Extraction and Synthesis We followed the PRISMA 2020 guidelines. We extracted study details in terms of inclusion criteria, trial demographics and the pre-specified outcome of all-cause hospitalization. Risk of bias was assessed by the Cochrane Risk of Bias 2 tool. We conducted a frequentist random effects meta-analysis, as well as two sensitivity analyses using a Bayesian random effects meta-analysis with different estimates of prior probability: a weakly neutral prior (50% chance of efficacy with 95% confidence interval for Risk Ratio [RR] between 0.5 and 2) and a moderately optimistic prior (85% chance of efficacy). We contextualized the results by estimating the probability of any effect (RR ≤1) and moderate effect (RR ≤0.9) on reducing hospitalization. Main Outcome(s) and Measure(s) All cause hospitalization. Results 2196 participants were included from 3 identified trials. The risk ratios for hospitalization were 0.75 (95%CI, 0.57-0.97) for the frequentist analysis, 0.78 (95%CI 0.58-1.08) for the Bayesian weakly neutral prior, and 0.73 (95%CI, 0.53-1.01) for the Bayesian moderately optimistic prior. Depending on the scenario, the probability of any effect on hospitalization ranged from 94.1% to 98.3% and a moderate effect from 81.6% to 91.1%. Conclusions and Relevance Under a variety of assumptions, fluvoxamine shows a high probability of preventing hospitalization in outpatients with COVID-19. While ongoing randomized trials are important to evaluate alternative doses, explore the effectiveness in vaccinated patients, and provide further refinement to these estimates, fluvoxamine could be recommended as a treatment option, particularly in resource-limited settings or persons without access to SARS-CoV-2 monoclonal antibody therapy or direct antivirals. Key Points Question Does early administration of fluvoxamine prevent hospitalization in symptomatic adult outpatients with confirmed COVID-19? Findings In this meta-analysis with Bayesian sensitivity analyses that accounted for varying prior probabilities, there was a high probability (94.1% to 98.3%) that fluvoxamine reduces hospitalization with frequentist risk ratio of 0.75 (95%CI 0.57-0.97). Meaning Fluvoxamine is a widely available and inexpensive option that prevents hospitalization in patients with early COVID-19 based on randomized controlled trial evidence to date.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".