Preoperative Sedative-hypnotic Medication Use and Adverse Postoperative Outcomes
Bibliographic record
Abstract
OBJECTIVE: To determine the association between preoperative benzodiazepine and nonbenzodiazepine receptor agonist ("Z-drugs") use and adverse outcomes after surgery. BACKGROUND: Prescriptions for benzodiazepines and Z-drugs have increased over the past decade. Despite this, the association of preoperative benzodiazepines and Z-drug receipt with adverse outcomes after surgery is unknown. METHODS: Using the Optum Clinformatics Datamart, we performed a retrospective cohort study of adults 18 years or older who underwent any of 10 common surgical procedures between 2010 and 2015. The principal exposure was one or more filled prescriptions for a benzodiazepine or Z-drug in the 90 days before surgery. The primary outcome was any emergency department visit or hospital admission for either (1) a drug related adverse medical event or overdose or (2) a traumatic injury in the 30 days after surgery. RESULTS: Of 785,346 patients meeting inclusion criteria, 94,887 (12.1%) filled a preoperative prescription for a benzodiazepine or Z-drug. From multivariable logistic regression, benzodiazepine or Z-drug use was associated with an increased odds of an adverse postoperative event [odds ratio 1.13; 95% confidence interval: 1.08-1.18). In a separate regression, coprescription of benzodiazepines or Z-drugs with opioids was associated with a 1.45 odds of an adverse postoperative event (95% confidence interval: 1.37-1.53). CONCLUSIONS: Preoperative benzodiazepines and Z-drug use is common and associated with increased odds of adverse outcomes after surgery, particularly when coprescribed with opioids. Counseling on appropriate benzodiazepine and Z-drug use in advance of elective surgery may potentially increase the safety of surgical care.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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".