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.
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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 | 0.007 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.008 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".