Opioid Poisoning and Opioid Use Disorder in Older Trauma Patients
Bibliographic record
Abstract
Raoul Daoust,1,2 Jean Paquet,1 Lynne Moore,3,4 Alexis Cournoyer,1,2 Marcel Émond,5 Sophie Gosselin,6,7 Gilles J Lavigne,8,9 Aline Boulanger,10,11 Jean-Marc Mac-Thiong,2,12 Jean-Marc Chauny1,2 1Centre d’Étude en Médecine d’Urgence, Hôpital du Sacré-Coeur de Montréal, Montréal, Quebec, Canada; 2Département Médecine Familiale et Médecine d’Urgence, Faculté de Médecine, Université de Montréal, Montréal, Quebec, Canada; 3Département de médecine sociale et préventive, Faculté de médecine, Université Laval, Quebec, Quebec, Canada; 4Axe de recherche en traumatologie-urgence-soins intensifs du Centre de recherche FRQS du CHU-Québec, Quebec, Quebec, Canada; 5Département de médecine familiale et de médecine d’urgence, Faculté DE médecine, Université Laval, Quebec, Quebec, Canada; 6Department of Emergency Medicine, McGill University Health Centre, McGill University, Montréal, Quebec, Canada; 7Département de médecine d’urgence, CISSS-Montérégie-Centre, Greenfield Park, Québec, Canada; 8Faculties of Dental Medicine and Medicine, Université de Montréal, Montréal, Quebec, Canada; 9Center for Advanced Research in Sleep Medicine, Hôpital du Sacré-Coeur de Montréal (CIUSSS du Nord de-l’Île-de-Montréal), Montréal, Quebec, Canada; 10Centre de recherche du Centre Hospitalier de l’Université de Montréal (CRCHUM), Montréal, Quebec, Canada; 11Département d’anesthésiologie, Faculté de médecine, Université de Montréal, Montréal, Quebec, Canada; 12Research Centre, Hôpital du Sacré-Coeur (CIUSSS du Nord de-l’île-de-Montréal), Montréal, Quebec, CanadaCorrespondence: Raoul Daoust Email raoul.daoust@videotron.caBackground: Patients hospitalized following a traumatic injury will be frequently treated with opioids during their stay and after discharge. We examined the relationship between acute phase (< 3 months) opioid use after discharge and the risk of opioid poisoning or use disorder in older trauma patients.Methods: In a retrospective multicenter cohort study conducted on registry data, we included all patients ≥ 65 years admitted (hospital stay > 2 days) for injury in 57 trauma centers in the province of Quebec (Canada) between 2004 and 2014. We searched for opioid poisoning and opioid use disorder from ICD-9 to ICD-10 code diagnosis after their initial injury. Patients that filled an opioid prescription within a 3-month period after sustaining the trauma were compared to those who did not, using Cox proportional hazards regressions.Results: A total of 70,314 admissions were retained for analysis; median age was 82 years (IQR: 75– 87), 68% were women, and 34% of the patients filled an opioid prescription within 3 months of the initial trauma. During a median follow-up of 2.6 years (IQR: 1– 5), 192 participants (0.27%; 95% CI: 0.23%-0.31%) were hospitalized for opioid poisoning and 73 (0.10%; 95% CI: 0.08%-0.13%) were diagnosed with opioid use disorder. Having filled an opioid prescription within 3 months of injury was associated with an increased hazard ratio of opioid poisoning (2.8; 95% CI: 2.1– 3.8) and opioid use disorder (4.2; 95% CI: 2.4– 7.4) after the injury. However, history of opioid poisoning (2.6; 95% CI: 1.1– 5.8), of substance use disorder (4.3; 95% CI: 2.4– 7.7), or of the opioid prescription filled (2.8; 95% CI: 2.2– 3.6) before the trauma, was also related to opioid poisoning or opioid use disorder after the injury.Conclusion: Opioid poisoning and opioid use disorder are rare events after hospitalization for trauma in older patients. However, opioids should be used cautiously in patients with a history of substance use disorder, opioid poisoning or opioid use.Keywords: prescription opioids, opioid poisoning, opioid use disorder, trauma, injury, older adults
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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 | 0.000 | 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 | 0.001 | 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) | 0.003 | 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".