Rates and risk factors for prolonged opioid use after major surgery: population based cohort study
Bibliographic record
Abstract
OBJECTIVE: To describe rates and risk factors for prolonged postoperative use of opioids in patients who had not previously used opioids and undergoing major elective surgery. DESIGN: Population based retrospective cohort study. SETTING: Acute care hospitals in Ontario, Canada, between 1 April 2003 and 31 March 2010. PARTICIPANTS: 39,140 opioid naïve patients aged 66 years or older who had major elective surgery, including cardiac, intrathoracic, intra-abdominal, and pelvic procedures. MAIN OUTCOME MEASURE: Prolonged opioid use after discharge, as defined by ongoing outpatient prescriptions for opioids for more than 90 days after surgery. RESULTS: Of the 39,140 patients in the entire cohort, 49.2% (n=19,256) were discharged from hospital with an opioid prescription, and 3.1% (n=1229) continued to receive opioids for more than 90 days after surgery. Following risk adjustment with multivariable logistic regression modelling, patient related factors associated with significantly higher risks of prolonged opioid use included younger age, lower household income, specific comorbidities (diabetes, heart failure, pulmonary disease), and use of specific drugs preoperatively (benzodiazepines, selective serotonin reuptake inhibitors, angiotensin converting enzyme inhibitors). The type of surgical procedure was also highly associated with prolonged opioid use. Compared with open radical prostatectomies, both open and minimally invasive thoracic procedures were associated with significantly higher risks (odds ratio 2.58, 95% confidence interval 2.03 to 3.28 and 1.95 1.36 to 2.78, respectively). Conversely, open and minimally invasive major gynaecological procedures were associated with significantly lower risks (0.73, 0.55 to 0.98 and 0.45, 0.33 to 0.62, respectively). CONCLUSIONS: Approximately 3% of previously opioid naïve patients continued to use opioids for more than 90 days after major elective surgery. Specific patient and surgical characteristics were associated with the development of prolonged postoperative use of opioids. Our findings can help better inform understanding about the long term risks of opioid treatment for acute postoperative pain and define patient subgroups that warrant interventions to prevent progression to prolonged postoperative opioid 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".