Pharmacogenetics of vitamin K antagonists and bleeding risk prediction in atrial fibrillation

Oral Anticoagulation in Patients With Advanced Chronic Kidney Disease and Atrial Fibrillation: Beyond Anticoagulation

The optimal approach to prevent stroke and systemic embolism in patients with advanced chronic kidney disease (CKD) and atrial fibrillation remains unresolved. We conducted a narrative review to explore areas of uncertainty and opportunities for future research. First, the relationship between atrial fibrillation and stroke is more complex in patients with advanced CKD than in the general population. The currently employed risk stratification tools do not adequately discriminate between patients deriving a net benefit and those suffering a net harm from oral anticoagulation. Anticoagulation initiation should probably be more restrictive than is currently advocated by official guidelines. Recent evidence reveals that the superior benefit-risk profile of non-vitamin K antagonist oral anticoagulants (NOACs) vs vitamin K antagonists (VKAs) observed in the general population and in moderate CKD can be extended to advanced CKD. The NOACs yield better protection against stroke, cause less major bleeding, are associated with less acute kidney injury and a slower decline of CKD, and are associated with a lower incidence of cardiovascular events than VKAs. The VKAs may be harmful in CKD patients, in particular in patients with a high bleeding risk and labile international normalized ratio. The better safety and efficacy of NOACs as opposed to VKAs may be particularly evident in advanced CKD as a result of better on-target anticoagulation with NOACs, harmful off-target vascular effects of VKAs, and beneficial off-target vascular effects of NOACs. The intrinsic vasculoprotective effects of NOACs are supported by animal experimental evidence as well as by findings of large clinical trials and may result in use of NOACs beyond their anticoagulant properties.

A Systematic Review of Polygenic Models for Predicting Drug Outcomes

Polygenic models have emerged as promising prediction tools for the prediction of complex traits. Currently, the majority of polygenic models are developed in the context of predicting disease risk, but polygenic models may also prove useful in predicting drug outcomes. This study sought to understand how polygenic models incorporating pharmacogenetic variants are being used in the prediction of drug outcomes. A systematic review was conducted with the aim of gaining insights into the methods used to construct polygenic models, as well as their performance in drug outcome prediction. The search uncovered 89 papers that incorporated pharmacogenetic variants in the development of polygenic models. It was found that the most common polygenic models were constructed for drug dosing predictions in anticoagulant therapies (n = 27). While nearly all studies found a significant association with their polygenic model and the investigated drug outcome (93.3%), less than half (47.2%) compared the performance of the polygenic model against clinical predictors, and even fewer (40.4%) sought to validate model predictions in an independent cohort. Additionally, the heterogeneity of reported performance measures makes the comparison of models across studies challenging. These findings highlight key considerations for future work in developing polygenic models in pharmacogenomic research.