Abstract 5769: Opioid prescription characteristics associated with frequent emergency department use among hospitalized cancer patients: a comparative cohort study
Bibliographic record
Abstract
Abstract Background: Opioid use is associated with greater health resource utilization, namely unplanned emergency department (ED) visits. The purpose of this study was to characterize ED visit patterns by patients hospitalized for cancer based on their use of prescription opioids in the community. Design & Methods: A prospective cohort study of cancer patients discharged from medical and surgical units at an academic health center in Montreal (Canada) between October 2014 and November 2016 was assembled. The main outcome was frequent ED use (≥4 ED visits) in the year following hospital discharge. Clinical information linked to health administrative claims from the provincial universal health care program (RAMQ) was used in multivariable logistic regression to model patient and opioid prescription characteristics, comparing frequent ED users to non-frequent ED users. Potential predictors included history of chronic pain condition, mental health diagnoses, type of cancer, receipt of radiotherapy/chemotherapy, medication history (previous use of opioids, history of antidepressant use, benzodiazepines), receipt of surgery during the hospitalization as well as characteristics of the discharge prescription (e.g: receipt of an opioid, presence of a multi-modal pain regimen). Results: A cohort of 1253 cancer patients discharged from the medical and surgical units was assembled. The mean age for these patients was 70.9 (11.8) and the most frequent cancers included 488 (38.9%) respiratory and 309 (24.6%) upper digestive cancer. Overall, 54% of cancer patients (n =654) had at least one ED visit in the year post-discharge. Of these, all had filled at least one opioid prescription during the follow-up period. Of patients with at least one ED visit in the one year post-discharge, 29% (n = 188) became frequent ED users. In adjusted multivariable logistic model, the strongest associations of frequent ED use were receipt of chemotherapy one year before their index hospitalization (odds ratio (OR) 1.67; 95% CI: 1.08 - 2.61) and respiratory cancer diagnoses (OR 1.69; 95% CI: 1.07 - 2.67). With respect to opioid prescribing, most the dispensations were for oxycodone (51.4%) and hydromorphone (33.6%). Patients receiving a daily dose >90 MME (morphine milligram equivalents) had an odds ratio of 2.24 (95% CI: 1.14- 4.40) of becoming frequent ED users. Those who filled more than one type of opioid during the follow-up were 1.81 times more likely to become repeated ED users (95% CI: 1.23 - 2.70). Conclusions: Cancer patients with higher opioid use after hospital discharge and active use of more than two different type of opioids within one year time of discharge are at higher risk of encoring unplanned health visits in the ER. A new approach to care planning and coordination is recommended to better monitor opioid prescribing practices and improve outcomes. Citation Format: Siyana Kurteva, Robyn Tamblyn, Ari Meguerditchian. Opioid prescription characteristics associated with frequent emergency department use among hospitalized cancer patients: a comparative cohort study [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 5769.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".