Early Treatment with Fluvoxamine Among Patients with COVID-19: A Cost-Consequence Model
Bibliographic record
Abstract
Summary Background Three randomized trials have been conducted indicating a clinical benefit of early treatment with fluvoxamine versus placebo for adults with symptomatic COVID-19. We assessed the cost-consequences associated with the use of this early treatment in outpatient populations. Methods Using results from the three completed trials of fluvoxamine vs. placebo for the treatment of COVID-19, we performed a meta-analysis. We conducted a cost-consequence analysis using a decision-model to assess the health system benefits of the avoidance of progression to severe COVID-19. Outcomes of relevance to resource planning decisions in the US and elsewhere, including costs and days of hospitalization avoided, were reported. We constructed a decision-analytic model in the form of a decision tree to evaluate two treatment strategies for high-risk patients with confirmed, symptomatic COVID-19, from the perspective of a third-party payer:(1) treatment with a 10-day course of fluvoxamine (100mg twice daily); (2) current standard-of-care; (3) molnupiravir 5-day course. We used a time horizon of 28 days. Results Administration of fluvoxamine to symptomatic outpatients with COVID-19 at high-risk of developing progression to severe COVID-19 complications is substantially cost-saving in the US, in the amount of $232 per eligible patient, and saves an average of 0.15 hospital days per patient treated is likely to be similarly beneficial in other settings. Fluvoxamine is cost saving in locations where total hospital costs are >$738. Molnupiravir had an additional cost to the healthcare system of $404 per patient treated. Conclusions Fluvoxamine is cost-saving for COVID-19 outpatient therapy. Funding FastGrants and Rainwater Charitable Foundation
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.018 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 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 source (direct Gemma or distilled Codex), 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".