EPR19-069: Opioid Use Among Cancer Patients Undergoing Surgery and Their Associated Risk of Re-admissions and Emergency Department Visits in the 1-Year Postsurgical Period
Bibliographic record
Abstract
Background: Prescription opioid use and overdose has steadily increased over the past years, resulting in a dramatic increase in opioid-related emergency department (ED) visits and hospitalizations. Methods: This study used a prospective cohort of cancer patients having undergone surgery in Montreal (Quebec) to describe their post-discharge opioid use and identify potential patterns of unplanned health service use (ED visits, hospitalizations). Provincial health administrative claims were used to measure opioid dispensation as well as hospital re-admissions and ED visits. The hospital warehouse, patient chart and patient interview will be used to further describe patient’s medical profile. Marginal structural models will be used to model the association between use of opioids and risk of ED visits and hospitalizations. Inverse probability of treatment and censoring weights will be constructed to properly adjust for confounders that may be unbalanced between the opioid and non–opioid users as well as to account for competing risk due to mortality. Reasons for the re-admissions will also be presented as part of the analyses. Covariates will include patient comorbidities, medication history, and healthcare system characteristics such as nurse-to-patient and attending physician-to-patient ratios. Results (interim): A total of 821 were included in the study; of these, 73% (n=597) were admitted for a cancer procedure. At postoperative discharge, 605 (74%) of patients had at least one opioid dispensation, of which the majority (67%) were oxycodone with hydromorphone being the second most prescribed (28%). Among those who filled a prescription, mean age was 66 (13.4), 68% had no previous history of opioid use, and 10% have had 3 or more dispensing pharmacies in the year prior to admission, compared to less than 1% for the non–opioid users. Overall, 343 people refilled their opioid prescription at least once and 128 at least twice during the 1-year postoperative period. Among cancer patients who were opioid users, 214 ED visits occurred in the 1 year after surgery compared to only 40 for the non-cancer opioid users. Conclusion: This study will help to identify the risk profile of cancer patients who are most likely to continue using opioids for prolonged periods following surgical procedures as well as quantify the impact of opioid use and its associated burden on the healthcare system in order to identify areas for possible interventions.
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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 | 0.001 |
| 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".