Routinely implementing safe deprescribing in primary care: a scoping review
Notice bibliographique
Résumé
Abstract Introduction Polypharmacy, the use of ≥5 medicines, continues to increase worldwide and can become problematic potentially leading to adverse drug reactions (ADRs) and poor patient outcomes. Deprescribing - discontinuing medicines where harms outweigh benefits (1) - can help minimise problematic polypharmacy. Although deprescribing is effective in stopping medicines it has not been routinely incorporated into practice, with a lack of evidence on how deprescribing can be implemented in primary care. The support and education patients require and the training that clinicians need, must be established to effectively implement routine safe deprescribing in primary care. This scoping review was conducted to identify research gaps and determine the value of undertaking a systematic review on the barriers and facilitators of implementing deprescribing in primary care. Aim Methods The Arksey and O’Malley framework for scoping reviews was used (2). The Cochrane Library, PubMed, Embase, MEDLINE, Web of Science and International Pharmaceutical Abstracts were searched from 1996 – February 2020. Additional references were identified from reference lists of included articles. Abstracts and titles were screened and potentially relevant articles received full text screening. Inclusion criteria were quantitative, qualitative and mixed-methods literature on deprescribing in primary care. The exclusion criteria were conference papers, non-English language papers and literature on palliative care/life-limiting illness, patient self-discontinuation, withdrawal of medicine due to ADR, substance misuse, and long-term care facilities. For intervention studies; country, study design, aims, population, intervention, education used, follow-up used and findings were collected. For non-interventional studies; study design, aims, population and themes identified were collected. For the deprescribing trials, barriers and facilitators to implementing the intervention were identified. Results 4612 articles were identified and 72 articles included (32 intervention studies; 40 non-intervention). The majority of articles were from the Netherlands (15), Canada (13), USA (10), and UK (seven). Study designs included 12 RCTs, 11 quasi-experimental studies, six follow-up papers, three protocols, 19 surveys studies, eight interview studies, two observational studies, a meta-ethnography, a Q-methodology study, a process evaluation and eight narrative reviews. Five studies documented providing patient support post-deprescribing with little description of what this consisted of. The provision of patient education or clinician training was used in 14 studies. Six of seven studies incorporating patient education into their intervention were able to safely deprescribe for a significant proportion of patients. Research on the barriers and facilitators to implementing deprescribing into primary care was not routinely reported with a greater focus on the process of deprescribing, rather than implementation. Conclusion There is a paucity of research on the fundamental characteristics required for deprescribing to be routinely and safely implemented in primary care. There is a lack of description on what type of education and support patients need, and training that clinicians require, for routine safe deprescribing. Future research is needed to identify and address these factors for the benefits of deprescribing to be realised. References (1) Scott IA, Hilmer SN, Reeve E, Potter K, Le Couteur D, Rigby D, et al. Reducing Inappropriate Polypharmacy: The Process of Deprescribing. JAMA Internal Medicine. 2015;175(5):827-34. (2) Arksey H, O’Malley L. Scoping studies: towards a methodological framework. International Journal of Social Research Methodology. 2005;8(1):19-32.
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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 | 0,037 | 0,134 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,002 |
| Méta-épidémiologie (sens large) | 0,008 | 0,006 |
| Bibliométrie | 0,022 | 0,023 |
| Études des sciences et des technologies | 0,002 | 0,002 |
| Communication savante | 0,006 | 0,006 |
| Science ouverte | 0,003 | 0,004 |
| Intégrité de la recherche | 0,004 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 0,001 |
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 ».