Collaboration Is Needed to Translate Pharmacology Data Into Better Health Outcomes in Chronic Liver Disease
Notice bibliographique
Résumé
TO THE EDITOR: We thank Ferreira et al. for their response to our article.(1) The authors express their concern regarding the lack of data on pharmacological (pharmacokinetic [PK] and pharmacodynamic [PD]) changes of drugs in patients with chronic liver disease (CLD) and the subsequent insufficient support for prescribing. We share this concern and would like to share our views on this issue in this reply. The lack of pharmacology data and information for prescribing in CLD is a well-known problem. This is especially true for older drugs,(2) which were marketed before guidance from regulatory agencies recommended PK studies in patients with hepatic impairment before drug approval. Initiatives from Canada and the Netherlands have demonstrated how these pharmacology data can be translated into practical guidance for safe drug use in cirrhosis. Ferreira and colleagues invite other researchers to develop similar initiatives. However, development of such guidance is a complex and time-consuming process requiring contributors with knowledge of pharmacology, hepatology, and medical informatics to retrieve relevant articles, summarize and discuss the findings, and formulate evidence-based advice.(3) Rather than repeating all the work already undertaken, we recommend collaboration between different research groups to combine our expertise and take the next step forward as a network. Together we can strengthen our capabilities and formulate a practical agenda to improve availability of pharmacology data for translation into practical prescribing recommendations in CLD. For example, to address the large gap in knowledge of PKPD changes, there should be a list compiled of medicines with missing pharmacology data. As this is probably a long list, it would be necessary to prioritize pharmacological studies based on clinical need for information (i.e., prevalence of medication use or perceived risk of harm in patients with CLD). This will formulate a useful strategy for pharmacology researchers to expand the available evidence base from which the current prescribing recommendations can be refined. We can further work with clinical pharmacy and hepatology colleagues to design clinical research needed to test the validity of the recommendations and their implementation in clinical practice. Collaboration with experts in medical epidemiology will be valuable to measure the impact of prescribing recommendations on patient outcomes over time, particularly with regard to “high-risk” drugs for medication-related harm. To conclude, we agree with Ferreira and colleagues that joint efforts are needed to improve available information for prescribing in patients with CLD. These efforts should focus on collaboration between international experts to provide a research agenda for pharmacology data, to discuss and strengthen current recommendations, and to validate these in clinical practice. We invite other researchers to join this initiative. Author names in bold designate shared co-first authorship.
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 enseignantsNi 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.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,004 | 0,002 |
| Intégrité de la recherche | 0,002 | 0,007 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 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; les deux têtes enseignantes s’accordent sur ce qui est montré ici.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».