Efficacy and Safety of a Digital Tapering Intervention for Patients Prescribed Opioids After Surgery: Protocol for a Prospective Exploratory Cohort Study
Notice bibliographique
Résumé
BACKGROUND: More than 300 million surgical procedures are performed worldwide each year, and opioids remain a primary approach for managing acute postoperative pain. Studies have demonstrated that a significant number of patients do not discontinue opioid treatment and continue to use opioids for months or even years after surgery. Tapering and management of prescription opioids is a well-known practice and is a part of the current clinical guidelines on safe prescribing. Every patient should receive thorough monitoring, education, and a tapering plan when prescribed opioids or receiving refills after a prolonged treatment. There are challenges associated with tapering, including close follow-up, patient education, clinician time, and withdrawal safety. The evolution of smartphone app use for follow-up has shown promising results in some fields of medicine, and patients are increasingly interested in this approach. OBJECTIVE: The objective of this study is to investigate the efficacy and safety of Prescriby services, comprising clinician management augmented with digital support, as a tapering intervention in patients after knee or hip replacement surgery. METHODS: Efficacy will be measured in tapers successfully completed, doses successfully lowered during tapering, number of active users, satisfaction with the intervention, and patients successfully remaining off opioid medication at 6 and 12 months after the intervention. Participant safety will be monitored by assessing adverse effects during tapering using the numeric pain rating scale to assess the severity of pain. Participants are recruited via referrals from orthopedic clinics and orthopedic departments in the hospital after surgery during the 6-month study period to the Prescriby clinic, where they will receive a personalized tapering treatment and follow-up with a clinical pharmacist. Despite the existence of numerous clinical guidelines on tapering off dependence-inducing medications, there is limited knowledge of the outcomes of such tapering. RESULTS: This study received funding in February 2024. Data collection for secondary outcomes started in November 2024 and ended in July 2025. Data collection for primary outcomes will start in June 2026 and finish in July 2026. As of manuscript submission in August 2025, 75 patients had been recruited into the study. Analysis of secondary data started in August 2025, and results will be published in the fourth quarter of 2025. Analysis of primary data will start in July 2026, and results will be published in the fourth quarter of 2026. CONCLUSIONS: We anticipate that persistent opioid use in standard treatment could be somewhere in the range of 10% to 15%. We hypothesize that the intervention group will have a lower rate of persistent opioid use. With regard to the secondary outcomes, we estimate that more than 85% of the participants in intervention group will be able to taper completely off their opioid medication and anticipate a high degree of satisfaction with the intervention. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/72317.
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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 | 0,025 | 0,022 |
| Méta-épidémiologie (sens strict) | 0,003 | 0,001 |
| Méta-épidémiologie (sens large) | 0,005 | 0,005 |
| Bibliométrie | 0,002 | 0,002 |
| Études des sciences et des technologies | 0,003 | 0,002 |
| Communication savante | 0,003 | 0,003 |
| Science ouverte | 0,002 | 0,002 |
| Intégrité de la recherche | 0,004 | 0,004 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,029 | 0,006 |
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 ».