Hospital Initiated Injectable Opioid Agonist Therapy for the Treatment of Severe Opioid Use Disorder: A Case Series
Bibliographic record
Abstract
BACKGROUND: Across North America, there is an unprecedented opioid overdose epidemic. Approximately 15% of individuals with severe opioid use disorder (OUD) do not benefit from opioid agonist therapy (OAT) such as buprenorphine/naloxone or methadone and are considered treatment refractory. Of those who inject, injectable OAT (iOAT), with hydromorphone or diacetylmorphine, offered in community settings has demonstrated improved retention to treatment and decreased nonprescription opioid use. This case series seeks to describe iOAT initiation and titration in a hospital setting for treatment refractory individuals with OUD and examine impacts of iOAT on leaving hospital against medical advice (AMA). METHODS: A retrospective chart review of 4 patients initiated on iOAT during hospitalization at St. Paul's Hospital in Vancouver, BC was completed between July 2017 to May 2018. Outcomes of interest included: (1) dose titration schedules of hydromorphone; and (2) reports of leaving hospital AMA; and (3) continuation of iOAT in community postdischarge. RESULTS: Of the 4 participants, 2 were female and the mean age was 42 years. Despite a history of AMA, all participants stayed until the recommended the discharge after iOAT initiation. The average total doses of intravenous hydromorphone used during titration were: day 1: 100 mg and days 2 to 3: 200 mg. All continued iOAT in the community and one participant was readmitted within 30 days postdischarge. INTERPRETATION: This case series describes a novel approach to the management of treatment refractory individuals with severe OUD during hospitalization. Prescribing iOAT in acute care settings is feasible and may reduce rates of leaving hospital AMA.
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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) | 0.001 | 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".