Interventions to Address Potentially Inappropriate Prescribing for Older Primary Care Patients
Notice bibliographique
Résumé
Importance: Prescriptions for potentially inappropriate medications are common and, by definition, may carry risks that outweigh benefits. Objective: To determine whether interventions to address potentially inappropriate prescribing for older primary care patients are associated with changes in the number of medications prescribed, drug-related harms, hospitalizations, and mortality. Data Sources: MEDLINE, Embase, and the Cochrane Central Register of Controlled Trials were searched from inception to September 6, 2024. Study Selection: Randomized clinical trials of interventions to address potentially inappropriate prescribing for older primary care patients (aged ≥65 years) residing in the community or in long-term care facilities, such as nursing homes or assisted-living facilities, were included. Data Extraction and Synthesis: Two researchers independently screened the records and abstracted data using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) reporting guideline. Data were pooled using random-effects models. Main Outcomes and Measures: The planned outcomes were the number of medications, nonserious adverse drug reactions, injurious falls, quality of life, medical visits, emergency department visits, hospitalizations, and all-cause mortality. Random-effects meta-analyses were performed using the inverse variance method for similar studies, reporting risk ratios (RRs) or standardized mean differences (SMDs). Heterogeneity was assessed with I2 values, and publication bias was assessed with funnel plots and the Egger regression test. Results: Of the 14 649 records identified, 118 randomized clinical trials (comprising 417 412 patients) were included in this review. Interventions to address potentially inappropriate prescribing were associated with a reduction in the number of medications prescribed (SMD, -0.25 [95% CI, -0.38 to -0.13]), equivalent to approximately 0.5 fewer medications per patient. However, there were no substantial differences in the other outcomes, including nonserious adverse drug reactions (RR, 0.92 [95% CI, 0.58-1.46]), injurious falls (SMD, 0.01 [95% CI, -0.12 to 0.14]), quality of life (SMD, 0.09 [95% CI, -0.04 to 0.23]), medical visits (SMD, 0.02 [95% CI, -0.02 to 0.07]), emergency department admissions (RR, 1.02 [95% CI, 0.96-1.08]), hospitalizations (RR, 0.95 [95% CI, 0.89-1.02]), or all-cause mortality (RR, 0.94 [95% CI, 0.85-1.04]). Conclusions and Relevance: In this systematic review and meta-analysis, interventions to address potentially inappropriate prescribing were associated with reductions in the number of medications prescribed, with no substantial change in other outcomes. These findings suggest that inappropriate prescribing interventions may be implemented to safely reduce the number of medications prescribed to older adults in the primary care setting. Future studies should continue to evaluate these interventions using standardized criteria and consistently report potential harms to support data synthesis and capture key outcomes such as quality of life, hospitalization, and mortality.
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 | 0,010 | 0,061 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,005 | 0,007 |
| Bibliométrie | 0,003 | 0,002 |
| Études des sciences et des technologies | 0,000 | 0,001 |
| Communication savante | 0,002 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,003 | 0,002 |
| 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 ».