REAL-WORLD EFFECTIVENESS OF NIRMATRELVIR/RITONAVIR ON COVID-19-ASSOCIATED HOSPITALIZATION PREVENTION: A POPULATION-BASED COHORT STUDY IN THE PROVINCE OF QUÉBEC, CANADA
Bibliographic record
Abstract
ABSTRACT Introduction The nirmatrelvir/ritonavir (PAXLOVID™) is an antiviral blocking the replication of SARS-CoV-2. Early treatment with this antiviral has showed to reduce COVID-19 hospitalization and death in unvaccinated outpatients with mild-to-moderate COVID-19 and high risk of progression to severe disease with variants before Omicron. However, the current epidemiological context and the level of immunity in the population (vaccination and/or natural infection) have evolved considerably since the disclosure of these results. Thus, real-world evidence studies in vaccinated outpatients with lineage and sublineage of the variant are needed. Objective To assess whether nirmatrelvir/ritonavir treatment reduces the risk of COVID-19-associated hospitalization among Québec outpatients with mild-to-moderate COVID-19 at high risk of progression to severe disease in a real-world context, regardless of vaccination status and circulating variants, in the province of Québec. Methods This was a retrospective cohort study of SARS-CoV-2-infected outpatients who received nirmatrelvir/ritonavir between March 15 and August 15, 2022, using data from the Québec provincial clinico-administrative databases. Outpatients treated with nirmatrelvir/ritonavir were compared to unexposed ones. The treatment group was matched with controls using propensity-score matching in a ratio of 1:1. The outcome was COVID-19-associated hospitalization occurring within 30 days following the index date. Poisson regression with robust error variance was used to estimate the relative risk of hospitalization among the treatment group compared to the control group. Results A total of 16,601 and 242,341 outpatients were eligible to be included in the treatment (nirmatrelvir/ritonavir) and control groups respectively. Among treated outpatients, 8,402 were matched to controls. Regardless of vaccination status, nirmatrelvir/ritonavir-treated outpatient status was associated with a 69% reduced relative risk of COVID-19-associated hospitalization (RR: 0.31 [95% CI: 0.28; 0.36]). The effect was more pronounced in outpatients without a complete primary vaccination course (RR: 0.04 [95% CI: 0.03; 0.06]), while treatment with nirmatrelvir/ritonavir was not associated with benefit when outpatients with a complete primary vaccination course were considered (RR: 0.93 [95% CI: 0.78; 1.08]) Subgroups analysis among outpatients with a primary vaccination course showed that nirmatrelvir/ritonavir treatment was associated with a significant decrease in relative risk of hospitalization in severely immunocompromised outpatients (RR: 0.66 [95% CI: 0.50; 0.89]) and in outpatients aged 70 years and older (RR: 0.50 [95% CI: 0.34; 0.74]) when the last dose of the vaccine was received more than six months before. Conclusions Among SARS-CoV-2-infected outpatients at high risk for severe COVID-19 during Omicron BA.2 and BA.4/5 surges, treatment with nirmatrelvir/ritonavir was associated with a significant reduced relative risk of COVID-19-associated hospitalization. This effect was observed in outpatients with incomplete primary vaccination course and in outpatients who were severely immunocompromised. Except for severely immunocompromised outpatients, no evidence of benefit was found in any category of outpatient with a complete primary vaccination course whose last dose of COVID-19 vaccine was received within six months.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".