Effectiveness of Antivirals Nirmatrelvir-Ritonavir and Molnupiravir in Viral Sepsis: Retrospective Cohort Study
Bibliographic record
Abstract
Background: Viral infections, including those leading to sepsis, are common but often overlooked in clinical practice, yet the treatment strategies for viral sepsis remain inadequately defined. Objective: This study aims to investigate the effectiveness of antivirals nirmatrelvir-ritonavir and molnupiravir in the treatment of culture-negative sepsis. Methods: This retrospective cohort study was conducted across public hospitals in Hong Kong. We included patients diagnosed with COVID-19 between February 22, 2022, and June 30, 2023, who had no secondary bacterial or fungal infections. Propensity score matching was used to assess the efficacy of the antivirals nirmatrelvir-ritonavir and molnupiravir in patient subgroups with or without organ dysfunction at hospital admission, including circulatory shock, respiratory failure, acute kidney injury, coagulopathy, acute liver impairment, a composite of all organ dysfunctions, or no organ dysfunction. Key outcomes were in-hospital mortality and length of stay, reported as hazard ratios (HR) and mean differences, respectively. Results: The study included 15,599 COVID-19 patients with a mean age of 75.1 (SD 15.9) years. Molnupiravir treatment was associated with a significantly lower risk of mortality in patients in both the presence of any organ dysfunction (HR 0.75, 95% CI 0.58 to 0.96) and without organ dysfunction (HR 0.29, 95% CI 0.15-0.56). Nirmatrelvir-ritonavir was associated with decreased mortality with respiratory failure (absolute risk difference: 9.5%, 95% CI 6.26-12.72) and without organ dysfunction (HR 0.17, 95% CI 0.05-0.56). Antivirals also reduced the length of hospital stay; nirmatrelvir-ritonavir reduced length of stay in respiratory failure by an average of 3.37 (95% CI 2.32-4.42) days, acute kidney injury by 7.25 (95% CI 2.97-11.52) days, and coagulopathy by 7.04 (95% CI 2.99-4.05) days. Molnupiravir reduced the length of stay in acute kidney injury by an average of 6.7 (95% CI 2.39-11.08) days and coagulopathy by 5.68 (95% CI 1.20-10.16) days. Conclusions: Antivirals reduced mortality among hospitalized COVID patients, with the greatest reduction observed in patients without organ dysfunction. Antivirals were also effective in reducing the length of hospital stay.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".