Perioperative management of patients on glucagon-like peptide-1 receptor agonists
Bibliographic record
Abstract
PURPOSE OF REVIEW: To summarize the mechanism of action, clinical outcomes, and perioperative implications of glucagon-like peptide-1 receptor agonists (GLP-1-RAs). Specifically, this review focuses on the available literature surrounding complications (primarily, bronchoaspiration) and current recommendations, as well as knowledge gaps and future research directions on the perioperative management of GLP-1-RAs. RECENT FINDINGS: GLP-1-RAs are known to delay gastric emptying. Accordingly, recent case reports and retrospective observational studies, while anecdotal, suggest that the perioperative use of GLP-1-RAs may increase the risk of bronchoaspiration despite fasting intervals that comply with (and often exceed) current guidelines. As a result, guidelines and safety bulletins have been published by several Anesthesiology Societies. SUMMARY: While rapidly emerging evidence suggests that perioperative GLP-1-RAs use is associated with delayed gastric emptying and increased risk of bronchoaspiration (particularly in patients undergoing general anesthesia and/or deep sedation), high-quality studies are needed to provide definitive answers with respect to the safety and duration of preoperative drug cessation, and optimal fasting intervals according to the specific GLP-1-RA agent, the dose/duration of administration, and patient-specific factors. Meanwhile, clinicians must be aware of the potential risks associated with the perioperative use of GLP-1-RAs and follow the recommendations put forth by their respective Anesthesiology Societies.
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 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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".