<scp>COVID‐SAFER</scp> : Deprescribing Guidance for Hydroxychloroquine Drug Interactions in Older Adults
Bibliographic record
Abstract
BACKGROUND/OBJECTIVES: Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection causes high morbidity and mortality in older adults with chronic illnesses. Several trials are currently underway evaluating the antimalarial drug hydroxychloroquine as a potential treatment for acute infection. However, polypharmacy predisposes patients to increased risk of drug-drug interactions with hydroxychloroquine and may render many in this population ineligible to participate in trials. We aimed to quantify the degree of polypharmacy and burden of potentially inappropriate medications (PIMs) that older hospitalized adults are taking that would interact with hydroxychloroquine. METHODS: We reanalyzed data from the cohort of patients 65 years and older enrolled in the MedSafer pilot study. We first identified patients taking medications with potentially harmful drug-drug interactions with hydroxychloroquine that might exclude them from participation in a typical 2019 coronavirus disease (COVID-19) therapeutic trial. Next, we identified medications that were flagged by MedSafer as potentially inappropriate and crafted guidance around medication management if contemplating the use of hydroxychloroquine. RESULTS: The cohort contained a total of 1,001 unique patients with complete data on their home medications at admission. Of these 1,001 patients, 590 (58.9%) were receiving one or more home medications that could potentially interact with hydroxychloroquine, and of these, 255 (43.2%) were flagged as potentially inappropriate by the MedSafer tool. Common classes of PIMs observed were antipsychotics, cardiac medications, and antidiabetic agents. CONCLUSION: The COVID-19 pandemic highlights the importance of medication optimization and deprescribing PIMs in older adults. By acting now to reduce polypharmacy and use of PIMs, we can better prepare this vulnerable population for inclusion in trials and, if substantiated, pharmacologic treatment or prevention of COVID-19. J Am Geriatr Soc 68:1636-1646, 2020.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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) | 0.000 | 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 teacher head, 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".