Oral anticoagulants and risk of acute liver injury in patients with nonvalvular atrial fibrillation: a propensity-weighted nationwide cohort study.
Géric MauraMarc BardouCécile BillionnetAlain WeillJérôme DrouinAnke NeumannPublished in: Scientific reports (2020)
Insufficient real-world data on acute liver injury (ALI) risk associated with oral anticoagulants (OACs) exist in patients with nonvalvular atrial fibrillation (NVAF). Using the French national healthcare databases, a propensity-weighted nationwide cohort study was performed in NVAF patients initiating OACs from 2011 to 2016, considering separately those (1) with no prior liver disease (PLD) as main population, (2) with PLD, (3) with a history of chronic alcoholism. A Cox proportional hazards model was used to estimate the hazard ratio with 95% confidence interval (HR [95% CI]) of serious ALI (hospitalised ALI or liver transplantation) during the first year of treatment, for each non-vitamin K antagonist (VKA) oral anticoagulant (NOAC: dabigatran, rivaroxaban, apixaban) versus VKA. In patients with no PLD (N = 434,015), only rivaroxaban new users were at increased risk of serious ALI compared to VKA initiation (adjusted HR: 1.41 [1.05-1.91]). In patients with chronic alcoholism history (N = 13,173), only those initiating dabigatran were at increased risk of serious ALI compared to VKA (2.88 [1.74-4.76]) but an ancillary outcome suggested that differential clinical follow-up between groups might partly explain this association. In conclusion, this study does not suggest an increase of the 1-year risk of ALI in NOAC versus VKA patients with AF.
Keyphrases
- atrial fibrillation
- oral anticoagulants
- drug induced
- liver injury
- left atrial
- catheter ablation
- left atrial appendage
- direct oral anticoagulants
- healthcare
- heart failure
- end stage renal disease
- magnetic resonance
- percutaneous coronary intervention
- liver failure
- chronic kidney disease
- newly diagnosed
- contrast enhanced
- computed tomography
- venous thromboembolism
- network analysis
- magnetic resonance imaging
- prognostic factors
- quality improvement
- intensive care unit
- machine learning
- social media
- patient reported outcomes
- coronary artery disease
- mitral valve
- deep learning
- health information