Postoperative Respiratory Depression Associated with Pregabalin: A Case Series and a Preoperative Decision Algorithm
Bibliographic record
Abstract
Pregabalin is gaining popularity in the perioperative period for its usefulness in treating neuropathic pain and its apparent opioid- sparing effect. The present report describes the perioperative course of three patients who received pregabalin and experienced significant respiratory depression in the postoperative period. All three patients consented to the report and publication of the present case series. The first patient was elderly with borderline renal dysfunction. She experienced respiratory arrest in the immediate postoperative period following a craniotomy for tumour excision. The second patient presented with severe respiratory depression 12 h after receiving a spinal anesthetic for joint replacement, and was later found to have clinically significant obstructive sleep apnea. The third patient, who was an otherwise healthy elderly individual on benzodiazepines for anxiety, experienced respiratory arrest in the postanesthesia care unit after an uneventful anesthesia for lumbar spine decompression. All of these patients were treated successfully with standard resuscitation measures. Although other causes of respiratory depression in these patients were considered, there appears to be an association between pregabalin and this complication. The present article briefly reviews the evidence regarding the perioperative use of pregabalin. Based on the authors' experience and the available evidence, they believe that pregabalin may be useful in the management of acute pain in carefully selected patients undergoing certain surgeries. A clinical algorithm has been developed to guide the perioperative use of pregabalin. This algorithm may be helpful in increasing the safety of perioperative pregabalin use.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.005 | 0.003 |
| 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".