Opioid Use Disorders and the Risk of Postoperative Pulmonary Complications
Bibliographic record
Abstract
BACKGROUND: As the rate of opioid use disorders continues to rise, perioperative physicians are increasingly faced with the challenge of providing analgesia to these patients after surgery. Due to the likelihood of opioid dose escalation in the perioperative period, we hypothesized that opioid-dependent patients would be at increased risk for postoperative pulmonary complications. METHODS: A retrospective cross-sectional analysis of patients undergoing 6 representative elective surgical procedures was performed using the Nationwide Inpatient Sample from 2002 to 2011. The primary outcome was a composite including prolonged mechanical ventilation, reintubation, and acute respiratory failure. Secondary outcomes were length of stay, in-hospital mortality, and total hospital costs. Both multivariable logistic regression and propensity score matching were used to determine the impact of opioid use disorder on outcomes. RESULTS: The total sample-weighted cohort consisted of 7,533,050 patients. Patients with opioid use disorders were more likely to suffer pulmonary complications, with a frequency of 4.2% compared to 1.6% in the nonopioid-dependent group (P < .001), and had a 1.62 times higher odds (95% confidence interval [CI], 1.16-2.27) in multivariable regression analysis. In a secondary subgroup analysis, only patients undergoing a colectomy had a greater odds of suffering pulmonary complications (odds ratio, 2.64; 95% CI, 1.42-4.91; P = .0021). Additionally, patients with an opioid use disorder had a longer length of stay (0.84 days [95% CI, 0.52-1.16; P < .001]) and greater costs ($1816 [95% CI, 935-2698; P < .001]). CONCLUSIONS: This study demonstrates that patients with opioid use disorders are at increased risk for postoperative pulmonary complications, and have prolonged length of stay and resource utilization. Further research is needed regarding interventions to reduce the risk of complications in this subset of patients.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".