Antibiotic prescribing guideline recommendations in COVID-19: a systematic survey
Bibliographic record
Abstract
Background: COVID-19 and antimicrobial resistance (AMR) are two intersecting public health crises. Antimicrobial overuse in patients with COVID-19 threatens to worsen AMR. Guidelines are fundamental in encouraging antimicrobial stewardship. We sought to assess the quality of antibiotic prescribing guidelines and recommendations in the context of COVID-19, and whether they incorporate principles of antimicrobial stewardship. Methods: We performed a systematic survey which included a search using the concepts "antibiotic/antimicrobial" up to November 15, 2022 of the eCOVID-19 living map of recommendations (RecMap) which aggregates guidelines across a range of international sources and all languages. Guidelines providing explicit recommendations regarding antibacterial use in COVID-19 were eligible for inclusion. Guideline and recommendation quality were assessed using the AGREE II and AGREE-REX instruments, respectively. We extracted guideline characteristics including panel representation and the presence or absence of explicit statements related to antimicrobial stewardship (i.e., judicious antibiotic use, antimicrobial resistance or adverse effects as a consequence of antibiotic use). We used logistic regression to evaluate the relationship between guideline characteristics including quality and incorporation of antimicrobial stewardship principles. Protocol registration (OSF): https://osf.io/4pgtc. Findings: Twenty-eight guidelines with 63 antibiotic prescribing recommendations were included. Recommendations focused on antibiotic initiation (n = 52, 83%) and less commonly antibiotic selection (n = 13, 21%), and duration of therapy (n = 15, 24%). Guideline and recommendation quality varied widely. Twenty (71%) guidelines incorporated at least one concept relating to antimicrobial stewardship. Including infectious diseases expertise on the guideline panel (OR 9.44, 97.5% CI: 1.09-81.59) and AGREE-REX score (OR 3.26, 97.5% CI: 1.14-9.31 per 10% increase in overall score) were associated with a higher odds of guidelines addressing antimicrobial stewardship. Interpretation: There is an opportunity to improve antibiotic prescribing guidelines in terms of both quality and incorporation of antimicrobial stewardship principles. These findings can help guideline developers better address antibiotic stewardship in future recommendations beyond COVID-19. Funding: This project was funded by Michael G. DeGroote Cochrane Canada and McMaster GRADE centres.
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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 | 0.019 | 0.097 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.007 | 0.014 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".