Real-World Effectiveness of Nirmatrelvir-Ritonavir in Preventing Coronavirus Disease 2019–Associated Hospitalization: A Population-Based Cohort Study in the Province of Québec, Canada
Bibliographic record
Abstract
BACKGROUND: The nirmatrelvir-ritonavir has shown to reduce coronavirus disease 2019 (COVID-19) hospitalization and death before the Omicron era, but updated real-world evidence studies are needed. The current study aimed to assess whether nirmatrelvir-ritonavir reduces the risk of COVID-19-associated hospitalization among high-risk outpatients. METHODS: This was a retrospective cohort study of severe acute respiratory syndrome coronavirus 2-infected outpatients between 15 March and 15 October 2022, using data from the Québec clinico-administrative databases. Propensity score matching was used to compare infected outpatients treated with nirmatrelvir-ritonavir with those not receiving nirmatrelvir-ritonavir. The relative risk (RR) of COVID-19-associated hospitalization occurring within 30 days following the index date was assessed using a Poisson regression. RESULTS: A total of 14 756 treated outpatients were matched to controls. Regardless of vaccination status, nirmatrelvir-ritonavir treatment was associated with a 74% reduced RR of hospitalization (RR, 0.26 [95% confidence interval [CI], .23-.29]; number needed to treat [NNT, 15). The effect was more pronounced in outpatients with an incomplete primary vaccination course (RR, 0.13 [95% CI, .08-.20]; NNT, 9). Benefit was also found in those with a complete primary vaccination course (RR, 0.28 [95% CI, .25-.32]; NNT, 17) regardless of age and the delay since the last vaccination. Subgroups analysis among high-risk outpatients with a primary vaccination course showed that nirmatrelvir-ritonavir treatment was associated with a significant decrease in the RR of hospitalization in severely immunocompromised outpatients (RR, 0.28 [95% CI, .21-.36]; NNT, 7), regardless of the delay since the last vaccination. CONCLUSIONS: Nirmatrelvir-ritonavir reduces the risk of COVID-19-associated hospitalization among incompletely and completely vaccinated high-risk outpatients, as well as immunocompromised individuals, regardless of age and the delay since the last vaccination.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".