Adverse events related to drug–drug interactions in COVID-19 patients. A persistent concern in the post-pandemic era: a systematic review
Bibliographic record
Abstract
Since COVID-19 patients are often polytreated, monitoring drug–drug interaction (DDIs) is necessary. We evaluated whether drugs used after the second COVID-19 pandemic wave were associated with DDI-related adverse events and the role of drug interaction checkers in identifying them. The study (PROSPERO-ID: CRD42024507634) included: 1) consulting the drug interaction checkers Drugs.com, Liverpool COVID-19 Interactions, LexiComp, Medscape, and Micromedex; 2) systematic review; 3) reviewed studies analysis; 4) evaluating drug interaction checkers potential to anticipate DDI-related adverse events. The systematic review was performed searching PubMed, Scopus, ScienceDirect, and Cochrane databases from 1 March 2022 to 11 November 2023. Observational studies, and clinical trials were included. Article without reporting direct association between DDIs and adverse events were excluded. The risk of bias was assessed by Newcastle-Ottawa scale. The most frequent DDIs involved nirmatrelvir/ritonavir (N/R) and fluvoxamine. Fifteen studies, including 150 patients and 35 DDI-related outcomes, were analyzed. The most frequent DDIs involved tacrolimus with N/R, resulting in creatinine increase. Eighty percent of reported DDI-related adverse events would have been identified by all drug-interaction checkers, while the remaining 20% by at least 2 of them. Drug interaction checkers are useful but show inconsistencies. Multiple sources are needed to tailor treatment in the context of COVID-19.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 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 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".