The Effectiveness and Safety of Nafamostat Mesylate in the Treatment of COVID-19: a Meta-Analysis
Bibliographic record
Abstract
Nafamostat mesylate, a synthetic serine protease inhibitor, has been shown to have antiviral activity against SARS-CoV-2 and anticoagulant properties that may be beneficial in the treatment of COVID-19. We conducted a meta-analysis to evaluate the effectiveness and safety of nafamostat mesylate for the treatment of COVID-19. PubMed, Embase, Cochrane Library, Scopus, Web of Science, medRxiv, and bioRxiv were searched up to July 2023 for studies comparing the outcomes of nafamostat mesylate treatment and no nafamostat mesylate treatment in patients with COVID-19. Mortality, disease progression, and adverse events were analyzed. Six studies involving 16,195 patients were included in the analysis. Meta-analysis revealed no significant difference in mortality (odds ratio [OR]: 0.88, 95% CI: 0.20-3.75, P = 0.86) or disease progression (OR: 2.76, 95% CI: 0.31-24.68, P = 0.36) between groups. However, nafamostat mesylate was associated with an increased risk of hyperkalemia (OR: 7.15, 95% CI: 2.66-19.24, P < 0.0001). Nafamostat mesylate did not improve mortality or morbidity in hospitalized patients with COVID-19. The risk of hyperkalemia is a serious concern that requires monitoring and preventive measures. Further research in different COVID-19 populations is required.
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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 | 0.013 | 0.020 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.018 | 0.058 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".