Exploring the potential impact of collaborative medicines optimisation involving pharmacogenomic testing and general practice pharmacists in primary care in Ireland
Notice bibliographique
Résumé
Introduction People are living longer, but not necessarily in better health. The ageing population coupled with the rise in chronic conditions has led to many older people being prescribed polypharmacy, which may not always be appropriate. Medicines optimisation, a patient-centred process, strives for the best clinical outcomes by ensuring the safe and effective use of medicines through medication reviews. However, this necessitates greater collaboration among healthcare professional to individualise care, monitor outcomes closely, review medications more frequently and support patients when needed. Current medical practice often applies the same dose to all individuals based on population studies, overlooking individual differences. Precision medicine, in contrast, seeks to customise pharmacotherapy to individual patients, acknowledging variability in drug response. The study of genetics has been widely applied in precision medicine, and one of the emerging applications is pharmacogenomic-informed pharmacotherapy, which tailors drug selection and dosing based on a patient's genetic profile. Methods A systematic review was conducted to determine the effectiveness of pharmacogenomic interventions in improving outcomes derived from consensus-based core outcome sets in adult patients with multimorbidity and prescribed polypharmacy in all healthcare settings to inform the implementation of pharmacogenomic-guided therapy in clinical practice. A cross-sectional questionnaire study was performed to identify the potential opportunities and challenges of implementing pharmacogenomic testing in Ireland based on the previously unexplored views of those with multimorbidity and polypharmacy, those with a single chronic disease, and those without existing medical conditions. A retrospective study was undertaken using data from the STOP-HF cohort (comprised of Irish patients over the age of 40, with one or more risk factors for developing heart failure) to investigate the potential impact of pharmacogenomic testing. Finally, a qualitative semi-structured interview study was caried out with stakeholders involved in a medicines optimisation service, developed as part of the project to integrate a pharmacist into general practice, to establish their views of the service and the incorporation of pharmacogenomics. Results The results of the systematic review indicated that once the scope extends beyond single drug-gene interactions, there is limited available evidence. Nevertheless, pharmaco-genomic testing was shown to be an effective intervention for improving outcomes in this vulnerable patient cohort, reducing the incidence of adverse drug reactions, reducing healthcare utilisation and costs, and improving clinical decision-making. The questionnaire study provided a comprehensive account of the views of the Irish public regarding pharmacogenomic testing. It was shown that respondents with a chronic disease were more than twice as likely to value pharmacogenomic service availability than those without existing medical conditions. In the retrospective longitudinal study of patients with cardiovascular risk factors, multimorbidity, and polypharmacy, a high level of exposure to medications with potential for drug-gene interactions was observed. Statins accounted for the majority of these interactions; patients receiving a statin were genotyped for SLCO1B1 and ABCG2 no function alleles. A significant association between poor statin transporter function phenotype and progression of heart failure was found, highlighting an opportunity for routine genotyping to reduce the risk of heart failure. The qualitative interview study determined stakeholders' perceptions towards pharmacists working in general practice to provide a telepharmacy collaborative medicines optimisation service in Irish primary care. Barriers and facilitators to the service and considerations around the incorporation of pharmacogenomics into the service were identified. This study demonstrated the feasibility and acceptability of a general practice pharmacist working remotely. Conclusions The culmination of research presented in this thesis provides a comprehensive exploration of the impact pharmacogenomic interventions could have for patients with multimorbidity and prescribed polypharmacy in the context of medicines optimisation. Notably, prior to this research, no published studies had examined the implementation of pharmacogenomic testing in Ireland. Furthermore, the development of a telepharmacy collaborative medicines optimisation service involving a general practice pharmacist in primary care constituted a pioneering and substantial contribution to practice-based research. Rather than attempting to address all aspects of pharmacogenomic testing in a single research project, this thesis identifies opportunities for future research.
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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 | 0,032 | 0,085 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,003 |
| Bibliométrie | 0,002 | 0,002 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,003 | 0,002 |
| Science ouverte | 0,002 | 0,005 |
| Intégrité de la recherche | 0,002 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 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 ».