COVID-SAFER: Deprescribing Guidance for Nirmatrelvir-ritonavir Drug Interactions in Older Adults
Bibliographic record
Abstract
Abstract Importance Older adults, at high-risk of developing complications from COVID-19, could benefit from nirmatrelvir-ritonavir, an oral antiviral treatment for outpatients at high risk of complications from COVID-19; however, due to its potent CYP3A4 inhibition, nirmatrelvir-ritonavir is associated with many drug-drug interactions (DDI). Objectives Identify how common DDIs are between nirmatrelvir-ritonavir, common medications, and PIMs in older adults with polypharmacy. Craft anticipatory deprescribing guidance for PIMs that interact with nirmatrelvir-ritonavir to help prioritize deprescribing resources, and increase the proportion of older adults potentially benefitting from treatment. Design In this secondary analysis, we retrospectively analyzed all patients in the MedSafer cluster randomized deprescribing trial (N=5698 participants) to investigate the proportion of older adults (age >65) with polypharmacy (≥5 usual home medications) who would be ineligible for treatment with nirmatrelvir-ritonavir due to pre-existing DDIs. Setting The setting of the primary study was in medical inpatient units at 11 Canadian acute care hospitals. Participants Hospitalized persons, age 65 years and older, on 5 or more daily home medications, with an expected survival of 3 months or longer were included in this secondary analysis. Main outcomes and measures We identified the prevalence of (PIMs), as defined by the MedSafer software. We then developed deprescribing guidance, so clinicians could proactively deprescribe in an effort to increase the proportion of older adults eligible for safe treatment with nirmatrelvir-ritonavir in the event of a SARS-CoV-2 infection. Results Of 5698 participants, a total of 3869 (68%) were taking a medication with a known nirmatrelvir-ritonavir DDI, and of these 823 (21%) had at least one PIM. Of 823 PIMs, 627 (76%) were medications with a known high risk DDI and 213 (26%) were considered moderate risk DDIs with nirmatrelvir-ritonavir. Many of the PIMs required “advanced deprescribing” and could not simply be stopped, held, or adjusted at the time of nirmatrelvir-ritonavir receipt. Conclusions and relevance Older adults are at high risk of developing severe complications from COVID-19. Deprescribing PIMs in advance of a COVID-19 infection could increase the proportion of older adults who can safely receive nirmatrelvir-ritonavir, in addition to the usual benefits observed with medication management. Impact Statement We certify that this work is novel. This timely clinical investigation explores the unforeseen consequences of polypharmacy and the use of potentially inappropriate medication in older adults during the COVID-19 pandemic. This manuscript addresses the many drug-drug interactions between nirmatrelvir-ritonavir, an antiviral treatment for COVID-19, and potentially inappropriate medications in older adults with polypharmacy from the MedSafer cluster randomized trial. Our work highlights that the pandemic has created an even greater urgency to examine the medication lists of older adults and proactively deprescribe to improve the safety and tolerability of different COVID-19 treatments. Key Points Question: How does polypharmacy affect the eligibility of older adults to receive Nirmatrelvir-ritonavir? Findings: 68% of older adults in the MedSafer cluster randomized trial had a DDI with nirmatrelvir-ritonavir, and 21% were taking at least 1 potentially inappropriate medication. Meaning: Due to its potent CYP3A4 inhibition, nirmatrelvir-ritonavir is associated with many drug-drug interactions (DDI).
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".