Evaluation of Interventions to Reduce Opioid Prescribing for Patients Discharged From the Emergency Department
Notice bibliographique
Résumé
Importance: Limiting opioid overprescribing in the emergency department (ED) may be associated with decreases in diversion and misuse. Objective: To review and analyze interventions designed to reduce the rate of opioid prescriptions or the quantity prescribed for pain in adults discharged from the ED. Data Sources: MEDLINE, Embase, CINAHL, PsycINFO, and Cochrane Controlled Register of Trials databases and the gray literature were searched from inception to May 15, 2020, with an updated search performed March 6, 2021. Study Selection: Intervention studies aimed at reducing opioid prescribing at ED discharge were first screened using titles and abstracts. The full text of the remaining citations was then evaluated against inclusion and exclusion criteria by 2 independent reviewers. Data Extraction and Synthesis: Data were extracted independently by 2 reviewers who also assessed the risk of bias. Authors were contacted for missing data. The main meta-analysis was accompanied by intervention category subgroup analyses. All meta-analyses used random-effects models, and heterogeneity was quantified using I2 values. Main Outcomes and Measures: The primary outcome was the variation in opioid prescription rate and/or prescribed quantity associated with the interventions. Effect sizes were computed separately for interrupted time series (ITS) studies. Results: Sixty-three unique studies were included in the review, and 45 studies had sufficient data to be included in the meta-analysis. A statistically significant reduction in the opioid prescription rate was observed for both ITS (6-month step change, -22.61%; 95% CI, -30.70% to -14.52%) and other (odds ratio, 0.56; 95% CI, 0.45-0.70) study designs. No statistically significant reduction in prescribed opioid quantities was observed for ITS studies (6-month step change, -8.64%; 95% CI, -17.48% to 0.20%), but a small, statistically significant reduction was observed for other study designs (standardized mean difference, -0.30; 95% CI, -0.51 to -0.09). For ITS studies, education, policies, and guideline interventions (6-month step change, -33.31%; 95% CI, -39.67% to -26.94%) were better at reducing the opioid prescription rate compared with prescription drug monitoring programs and laws (6-month step change, -11.18%; 95% CI, -22.34% to -0.03%). Most intervention categories did not reduce prescribed opioid quantities. Insufficient data were available on patient-centered outcomes such as pain relief or patients' satisfaction. Conclusions and Relevance: This systematic review and meta-analysis found that most interventions reduced the opioid prescription rate but not the prescribed opioid quantity for ED-discharged patients. More studies on patient-centered outcomes and using novel approaches to reduce the opioid quantity per prescription are needed. Trial Registration: PROSPERO Identifier: CRD42020187251.
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 | 0,002 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,000 |
| 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 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 ».