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
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».