Nirmatrelvir-ritonavir use among adults hospitalized with COVID-19 during the Omicron phase of the COVID-19 pandemic, Canadian Nosocomial Infection Surveillance Program
Bibliographic record
Abstract
Background: Recent studies have demonstrated the effectiveness of nirmatrelvir-ritonavir in reducing the risk of progression to severe disease among outpatients with mild to moderate coronavirus disease 2019 (COVID-19); however, data are limited regarding the use and role of nirmatrelvir-ritonavir among hospitalized patients. This study describes the use and outcomes of nirmatrelvir-ritonavir among adults hospitalized with COVID-19 in a sentinel network of Canadian acute care hospitals during the Omicron variant phase of the pandemic. Methods: The Canadian Nosocomial Infection Surveillance Program conducts surveillance of hospitalized patients with COVID-19 in acute care hospitals across Canada. Demographic, clinical, treatment and 30-day outcome data were collected by chart review by trained infection control professionals using standardized questionnaires. Results: From January 1 to December 31, 2022, 13% (n=490/3,731) of adult patients (18 years of age and older) hospitalized with COVID-19 in 40 acute care hospitals received nirmatrelvir-ritonavir either at admission or during hospitalization. Most inpatients who received nirmatrelvir-ritonavir, 79% of whom were fully vaccinated, had at least one pre-existing comorbidity (97%) and were of advanced age (median=79 years). Few were admitted to an intensive care unit (2.3%) and among the 490 nirmatrelvir-ritonavir treated inpatients, there were 13 (2.7%) deaths attributable to COVID-19. Conclusion: These findings from a large sentinel network of Canadian acute-care hospitals suggest that nirmatrelvir-ritonavir is being used to treat adult COVID-19 patients at admission who are at risk of progression to severe disease or those who acquired COVID-19 in hospital. Additional research on the efficacy and indications for nirmatrelvir-ritonavir use in hospitalized patients is warranted to inform future policies and guidelines.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 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".