Predictors of In-hospital Postoperative Opioid Overdose After Major Elective Operations
Bibliographic record
Abstract
OBJECTIVE: The aim of this study was to describe national trends and outcomes of in-hospital postoperative opioid overdose (OD) and identify predictors of postoperative OD. SUMMARY OF BACKGROUND DATA: In 2000, the Joint Commission recommended making pain the 5th vital sign, increasing the focus on postoperative pain control. However, the benefits of pain management must be weighed against the potentially lethal risk of opioid OD. METHODS: This is a retrospective multi-institutional cohort study of patients undergoing 1 of 6 major elective inpatient operation from 2002 to 2011 using the Nationwide Inpatient Sample, an approximately 20% representative sample of all United States hospital admissions. Patients with postoperative OD were identified using ICD-9 codes for poisoning from opioids or adverse effects from opioids. Multivariate logistic regression was used to identify independent predictors. RESULTS: Among 11,317,958 patients, 9458 (0.1%) had a postoperative OD; this frequency doubled over the study period from 0.6 to 1.1 overdoses per 1000 cases. Patients with postoperative OD died more frequently during their hospitalization (1.7% vs 0.4%, P < 0.001). Substance abuse history was the strongest predictor of OD (odds ratio = 14.8; 95% confidence interval: 12.7-17.2). Gender, age, income, geographic location, operation type, and certain comorbid diseases also predicted OD (P < 0.05). Hospital variables, including teaching status, size, and urban/rural location, did not predict postoperative OD. CONCLUSIONS: Postoperative OD is a rare, but potentially lethal complication, with increasing incidence. Postoperative monitoring and treatment safety interventions should be thoughtfully employed to target high-risk patients and avoid this potentially fatal complication.
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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 | 0.000 |
| 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".