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Enregistrement W1990321253 · doi:10.1177/1715163513499530

Reducing polypharmacy in the elderly

2013· article· en· W1990321253 sur OpenAlexaffvenueabout
Barbara Farrell, Salima Shamji, Anne Monahan, Véronique French Merkley

Notice bibliographique

RevueCanadian Pharmacists Journal / Revue des Pharmaciens du Canada · 2013
Typearticle
Langueen
DomaineMedicine
ThématiquePharmaceutical Practices and Patient Outcomes
Établissements canadiensBruyèreUniversity of Ottawa
Organismes subventionnairesnon disponible
Mots-clésPolypharmacyMedicineIntensive care medicine

Résumé

récupéré en direct d'OpenAlex

Why is it so hard to stop medications? Pharmacists see it all the time—elderly patients taking 15, 20, sometimes 25 medications. Pill burden can be as high as 60 doses per day. Patients are unsure of the reasons for medications, take them haphazardly and often have other medications added to treat side effects. They tell us they hate taking these drugs. Family doctors express frustration, sometimes inheriting an elderly patient with little medication history and struggling to keep up with changes made during multiple hospital admissions or specialist visits. They feel pressured by various clinical guidelines to start medications, aren’t sure what a patient is actually taking, may have difficulty distinguishing symptoms from side effects, but don’t have a ready-made solution to the problem.1 Polypharmacy is variously defined as high numbers of medications (e.g., more than 5-10), use of more drugs than clinically indicated or use of inappropriate medications. In 2009, 63% of Canadian seniors were taking more than 5 medications, and 30% of those older than 85 years were taking more than 10. The prevalence is even higher for those living in long-term care.2 The impact of polypharmacy on our elderly population is significant. It is associated with poor adherence, drug-drug interactions, medication errors and adverse drug reactions—including falls, hip fractures, confusion and delirium—accounting for a significant percentage of potentially preventable emergency room visits and hospitalization.3,4 But interventions to reduce medication use are not consistently used. There are consensus-developed screening criteria for inappropriate medications, as well as algorithms and acronyms to help health care professionals conduct medication reviews to identify drug-related problems.5 However, there is little guidance with regard to how to implement suggested changes to reduce medication use. Many prescribers are reluctant to make such changes, often fearing adverse drug withdrawal events,6 and say they do not want to “rock the boat.” In the Geriatric Day Hospital of Bruyere Continuing Care, our elderly patients often have multiple issues such as cognitive impairment, falls, pain and deconditioning. They attend twice weekly for functional assessment and rehabilitation with an interprofessional team. Patients referred for medication review are typically taking 15 medications per day and have an average of 9 drug-related problems.7 We see prescribing cascades (in which a medication has been started to treat a side effect of another medication) and adverse effects of drugs on multiple systems, including hypotension, impaired cognition and balance problems. One by one, over a 10- to 12-week admission, we taper and stop those medications for which we have little evidence for continued use and those that might be contributing to adverse effects. We focus on reducing pill burden, facilitating independent medication management, furthering knowledge and understanding of medication use and communicating changes clearly. The outcome can be amazing. We’ve seen patients demonstrate significant improvement in symptoms with reduction in medication. Frustrated with the never-ending supply of patients suffering with polypharmacy, and the lack of broadly implemented clinical guidance to reduce polypharmacy, we have developed a series of case reports about polypharmacy in the elderly that demonstrate the strategies we employ to reduce medication use in our patients. Identifying drug-related problems, prioritizing them, using tapering approaches, monitoring for adverse drug-withdrawal events, reducing pill burden and appropriately using compliance strategies are demonstrated throughout these cases. As an interprofessional team, each member plays a role in helping to reduce medication use and monitor the effects. We have therefore designed the case reports so they can be used for interprofessional education in geriatric pharmacotherapy. The series is planned for the Canadian Pharmacists Journal, Canadian Family Physician and the Canadian Medical Association Journal. Each case will link to online resources to facilitate interprofessional discussion. We hope pharmacists and other health care professionals will find the strategies in this series supportive of their own efforts to reduce polypharmacy. With recent regulatory and remuneration changes, pharmacists are even better placed to actively participate in optimizing therapy in the elderly.8 We hope those who develop clinical guidelines will take note and consider how they might include “deprescribing” guidelines to further support tapering and stopping of medications when evidence is limited or when pharmacokinetic and pharmacodynamic parameters affecting medication distribution and effectiveness change with age.8 We’d like to thank the Bruyere Academic Medical Organization for its support, our colleagues in the Geriatric Day Hospital of Bruyere Continuing Care for their collaboration and our patients who willingly work with us to taper their medications, particularly the many patients who enthusiastically agreed to share their stories. We hope you enjoy the series and welcome your comments and feedback. ■

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 enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,001
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesMéta-épidémiologie (sens strict), Charge utile insuffisante (le modèle a refusé de juger)
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Étude de cas · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,590
Score d'incertitude au seuil1,000

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0010,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,001
Études des sciences et des technologies0,0010,000
Communication savante0,0000,001
Science ouverte0,0010,000
Intégrité de la recherche0,0000,002
Charge utile insuffisante (le modèle a refusé de juger)0,0040,000

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.

Tête enseignante Opus0,095
Tête enseignante GPT0,344
Écart entre enseignants0,249 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Devis d'étudeÉtude de cas
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations29
Publié2013
Routes d'admission3
Résumé présentoui

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