Electronic Decision Support for Deprescribing in Older Adults Living in Long-Term Care
Notice bibliographique
Résumé
Importance: Potentially inappropriate prescribing (PIP) is common, costly, and harmful. Deprescribing potentially inappropriate medications (PIMs) is a priority for improving the health outcomes of older adults. PIP is especially common in long-term care homes, with up to 88% of residents affected. Objective: To assess the efficacy of electronic decision support for deprescribing in long-term care. Design, Setting, and Participants: This stepped-wedge cluster randomized trial took place from August 1, 2021, to October 31, 2022, during the COVID-19 pandemic. The study assessed older adults residing in 1 of 5 long-term care homes in New Brunswick, Canada, at the start of the study who were prescribed 1 or more PIMs. The 5 long-term care homes were divided into 3 clusters. All clusters spent at least 3 months in a control phase; every 3 months a cluster was randomized to enter the intervention phase. Data analysis was performed from October 15, 2023, to March 24, 2025. Interventions: Electronically generated, individualized reports that contained prioritized opportunities for deprescribing in older adults were paired with preexisting quarterly medication reviews. Deprescribing reports were accessed through a secure viewer. Main Outcomes and Measures: The primary outcome was the proportion of residents with 1 or more PIMs deprescribed in the control phase vs intervention measured every 3 months after a medication review. For the primary outcome, an adjusted odds ratio (AOR) was calculated using a generalized linear model with a logit link, controlling for the effect of the intervention and adjusted for the number of PIMs, age, sex, language, and period as fixed effects and participants nested within sites as random effects. Results: A total of 725 residents participated in the study (median [IQR] age, 84 [76-90] years; 478 [65.9%] female). The median (IQR) number of medications was 10 (7-13), and the median (IQR) number of PIMs was 3 (2-4). In the control phase, the proportion of residents with 1 or more PIMs deprescribed was 92 of 725 (12.7%) compared with 226 of 621 (36.4%) during the intervention (AOR, 1.58; 95% CI, 1.07-2.34), in favor of the intervention. Conclusions and Relevance: This study found that electronic decision support paired with the usual workflow could render the deprescribing process scalable and effective. These results suggest that medication reviews should incorporate deprescribing as part of usual care. Trial Registration: ClinicalTrials.gov Identifier: NCT04762303.
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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 | 0,001 | 0,000 |
| 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 ».