Patient‐Reported Adverse Drug Events on Nirmatrelvir–Ritonavir: A Prospective Observational Study of a Pharmacist‐Led Follow‐Up and Monitoring Service
Bibliographic record
Abstract
ABSTRACT Background There is a paucity of population‐based data on adherence and adverse drug events (ADEs) associated with the treatment of coronavirus disease 2019 (COVID‐19) with nirmatrelvir and ritonavir (NMV/r). We sought to contribute data on patient‐reported treatment adherence and ADEs with NMV/r using data collected by community pharmacists. Methods Data on patient‐reported NMV/r adherence and ADEs, the study outcomes, were collected by pharmacists through the Paxlovid Follow‐up (PAX‐F) service, with linkage to administrative data from the British Columbia (BC) Ministry of Health. The cohort study included individuals in BC prescribed NMV/r between January 31, 2022, and December 31, 2022. Pharmacists conducted phone assessments with patients or their caregivers after completion of NMV/r treatment. Responses were recorded in the PharmaNet database, a provincewide data network. Logistic regression was used to identify characteristics associated with the study outcomes. Results PAX‐F service was provided to 10 200 individuals. Median age was 72 years, and 56.4% were female. There were 87.6% of respondents (n = 9034) who reported completing all 5 days of their NMV/r medication; 7351 individuals with a PAX‐F assessment (72.1%) were dispensed medications with a known interaction with NMV/r. 47.8% of respondents (n = 4960) reported experiencing a total of 7439 ADEs, of which 1.5% of ADEs (n = 115 in 79 individuals) were reported to have been managed at a hospital or emergency room. Female sex was associated with an increased risk of nonadherence to NMV/r compared with men (odds ratio [OR], 1.53; 95% confidence interval [CI], 1.41–1.68), and an increased risk of ADEs (OR, 1.32; 95% CI, 1.15–1.53). Nonvaccination was associated with nonadherence to NMV/r (OR, 3.05; 95% CI, 2.41–3.87), but not with reporting an ADE (OR, 1.04; 95% CI, 0.85–1.29). Conclusion Nearly half of NMV/r users in BC reported ADEs with treatment, suggesting that careful consideration of the risks and benefits of prescribing NMV/r is warranted.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".