Deprescribing medicines in older people living with multimorbidity and polypharmacy: the TAILOR evidence synthesis
Notice bibliographique
Résumé
BACKGROUND: Tackling problematic polypharmacy requires tailoring the use of medicines to individual needs and circumstances. This may involve stopping medicines (deprescribing) but patients and clinicians report uncertainty on how best to do this. The TAILOR medication synthesis sought to help understand how best to support deprescribing in older people living with multimorbidity and polypharmacy. OBJECTIVES: We identified two research questions: (1) what evidence exists to support the safe, effective and acceptable stopping of medication in this patient group, and (2) how, for whom and in what contexts can safe and effective tailoring of clinical decisions related to medication use work to produce desired outcomes? We thus described three objectives: (1) to undertake a robust scoping review of the literature on stopping medicines in this group to describe what is being done, where and for what effect; (2) to undertake a realist synthesis review to construct a programme theory that describes 'best practice' and helps explain the heterogeneity of deprescribing approaches; and (3) to translate findings into resources to support tailored prescribing in clinical practice. DATA SOURCES: Experienced information specialists conducted comprehensive searches in MEDLINE, Cumulative Index to Nursing and Allied Health Literature, Web of Science, EMBASE, The Cochrane Library (Cochrane Database of Systematic Reviews, Cochrane Central Register of Controlled Trials), Joanna Briggs Institute Database of Systematic Reviews and Implementation Reports, Google (Google Inc., Mountain View, CA, USA) and Google Scholar (targeted searches). REVIEW METHODS: The scoping review followed the five steps described by the Joanna Briggs Institute methodology for conducting a scoping review. The realist review followed the methodological and publication standards for realist reviews described by the Realist And Meta-narrative Evidence Syntheses: Evolving Standards (RAMESES) group. Patient and public involvement partners ensured that our analysis retained a patient-centred focus. RESULTS: Our scoping review identified 9528 abstracts: 8847 were removed at screening and 662 were removed at full-text review. This left 20 studies (published between 2009 and 2020) that examined the effectiveness, safety and acceptability of deprescribing in adults (aged ≥ 50 years) with polypharmacy (five or more prescribed medications) and multimorbidity (two or more conditions). Our analysis revealed that deprescribing under research conditions mapped well to expert guidance on the steps needed for good clinical practice. Our findings offer evidence-informed support to clinicians regarding the safety, clinician acceptability and potential effectiveness of clinical decision-making that demonstrates a structured approach to deprescribing decisions. Our realist review identified 2602 studies with 119 included in the final analysis. The analysis outlined 34 context-mechanism-outcome configurations describing the knowledge work of tailored prescribing under eight headings related to organisational, health-care professional and patient factors, and interventions to improve deprescribing. We conclude that robust tailored deprescribing requires attention to providing an enabling infrastructure, access to data, tailored explanations and trust. LIMITATIONS: Strict application of our definition of multimorbidity during the scoping review may have had an impact on the relevance of the review to clinical practice. The realist review was limited by the data (evidence) available. CONCLUSIONS: Our combined reviews recognise deprescribing as a complex intervention and provide support for the safety of structured approaches to deprescribing, but also highlight the need to integrate patient-centred and contextual factors into best practice models. FUTURE WORK: The TAILOR study has informed new funded research tackling deprescribing in sleep management, and professional education. Further research is being developed to implement tailored prescribing into routine primary care practice. STUDY REGISTRATION: This study is registered as PROSPERO CRD42018107544 and PROSPERO CRD42018104176. FUNDING: ; Vol. 26, No. 32. See the NIHR Journals Library website for further project information.
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 | 0,096 | 0,312 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,002 |
| Méta-épidémiologie (sens large) | 0,009 | 0,013 |
| Bibliométrie | 0,014 | 0,011 |
| Études des sciences et des technologies | 0,001 | 0,002 |
| Communication savante | 0,010 | 0,007 |
| Science ouverte | 0,004 | 0,005 |
| Intégrité de la recherche | 0,005 | 0,004 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,009 | 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 ».