Abstract 5769: Opioid prescription characteristics associated with frequent emergency department use among hospitalized cancer patients: a comparative cohort study
Notice bibliographique
Résumé
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.
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 machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,001 | 0,002 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 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 source (Gemma direct ou Codex distillé), 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 ».