Patients with a common heart rhythm disorder called atrial fibrillation are often prescribed oral anticoagulants to prevent strokes. All oral anticoagulants greatly reduce an individual's chances of experiencing a stroke, while increasing one's chances of having a serious bleeding complication. Prior research suggests that difficulty weighing the risks and benefits of treatment may result in underutilization of oral anticoagulants: Less than one-half of patients at high risk of stroke receive oral anticoagulants. Physicians identify patients who are most likely to benefit from using these medicines by using an individual's characteristics to estimate stroke risk without treatment and the risk of major bleeding with treatment. However, the existing risk scoring systems have a number of limitations. For example, they do not differentiate bleeding events that have different risks of death (i.e., bleeding in vs. outside the brain). The goal of this study is to develop new scoring systems that will enable physicians to estimate the probabilities of a patient first experiencing death, stroke, bleeding in the brain, and bleeding outside of the brain in the year after atrial fibrillation diagnosis. The new scoring systems may be useful to physicians and patients when making treatment decisions.
Treatment decisions in atrial fibrillation (AF) are often guided by risk stratification schemes for stroke and warfarin-associated bleeding. However, these schemes have limited utility in the era of target-specific oral anticoagulants (TSOAC). The objectives of this study are to develop and validate new prediction models for the one-year competing risks of a first event among death, stroke, intracranial hemorrhage, and extracranial hemorrhage while also accounting for treatment change as a competing event. To address these objectives, we will estimate the associations between previously-identified predictors and the four clinical events in a cohort of patients with incident non-valvular AF for whom oral anticoagulants may be appropriate. The index date will be 90 days after AF diagnosis, a landmark chosen based on previous work. The dependent variables include the times from index date to all-cause death, ischemic stroke, intracranial hemorrhage, extracranial hemorrhage, and change in treatment status. Independent variables include treatment at index (none, warfarin, dabigatran, rivaroxaban, and apixaban), predictors of stroke, predictors of major bleeding, and concurrent medications. Fine-Gray regression models will be used with a variable selection procedure to estimate the associations. The validity (fit) of the models will be assessed using concordance statistics. Illustrative individual predictions will be reported.
Jason Fine - Chief Investigator - University of North Carolina at Chapel Hill
Todd Durham - Corresponding Applicant - IQVIA - USA (Headquarters)
Anthony Viera - Collaborator - University of North Carolina at Chapel Hill
Jayanti Mukherjee - Collaborator - Bristol-Myers Squibb - USA ( BMS )
Kristen Hasmiller Lich - Collaborator - University of North Carolina at Chapel Hill
Stacie Dusetzina - Collaborator - University of North Carolina at Chapel Hill
HES Admitted Patient Care;ONS Death Registration Data