Neuraxial Anesthesia and Intraoperative Bilevel Positive Airway Pressure in a Patient With Severe Chronic Obstructive Pulmonary Disease and Obstructive Sleep Apnea Undergoing Elective Sigmoid Resection
Bibliographic record
Abstract
OBJECTIVE: This case report describes the anesthetic management of a patient with severe chronic obstructive pulmonary disease (COPD) and obstructive sleep apnea (OSA) who underwent elective sigmoid resection under combined spinal-epidural anesthesia and bilevel positive airway pressure (BiPAP). CASE REPORT: A 63-year-old man with diverticular disease presented for a sigmoid resection. His medical history included coronary artery bypass grafting, diabetes mellitus, gastroesophageal reflux, chronic renal failure, COPD, a paralyzed left hemidiaphragm, and OSA treated with nighttime BiPAP and oxygen. Sigmoid resection was performed under combined lumbar spinal-thoracic epidural anesthesia without general anesthesia and/or endotracheal intubation (intrathecal 3.5 mL isobaric bupivacaine 0.5% with 100 microg epinephrine and 200 microg morphine [Epimorph], epidural 60 mg bupivacaine, and 200 mg lidocaine). Intravenous ketamine was administered at a rate between 30 and 50 mg/h. Intraoperative BiPAP was applied using a setting of 12.5/8mm Hg with a backup ventilation rate of 10 breaths/min and an oxygen flow of 4 L/min. After surgery, epidural bupivacaine (0.1%) was infused over 3 days at 10 ml/hr supplemented with oral acetaminophen, resulting in excellent pain relief. Postoperatively, the patient continued to use BiPAP when sleeping, and no adverse respiratory events were observed. The patient was discharged home 5 days after surgery. CONCLUSION: Combined spinal-epidural anesthesia was successfully used in a patient with COPD and OSA undergoing sigmoid resection. Perioperative administration of BiPAP, excellent pain control by continuous epidural infusion of local anesthetic, and the avoidance of endotracheal intubation may have contributed to this patient's uncomplicated postoperative course.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| 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".