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Oral Anticoagulant Adherence Trajectories in Atrial Fibrillation
Quick Takes
- In this study of nearly 20,000 patients followed for 5 years using an administrative database in Canada, group-based trajectory modeling identified four distinct oral anticoagulant adherence trajectories: “consistent adherence” (74% of the cohort), “rapid decline and discontinuation” (12%), “rapid decline and partial recovery” (10%), and “slow decline and discontinuation” (4%).
- Very few patient variables were found to be associated with specific adherence trajectories.
Study Questions:
What are long-term oral anticoagulant adherence trajectories and the factors associated with them in patients with atrial fibrillation (AF)?
Methods:
Adults with AF were retrospectively identified using an administrative health database in British Columbia, Canada. Group-based trajectory modeling was used to model patients’ 90-day proportions of days covered over time to identify distinct 5-year adherence trajectories. Regression analysis was used to assess the effect of various demographic and clinical factors on exhibiting each adherence trajectory.
Results:
The study cohort included 19,749 patients with AF (mean age, 70.6 years), 56% male, mean CHA2DS2-VASc stroke risk score, 2.8. Group-based trajectory modeling identified four distinct oral anticoagulant adherence trajectories: “consistent adherence” (n = 14,631, 74% of the cohort), “rapid decline and discontinuation” (n = 2,327, 12%), “rapid decline and partial recovery” (n = 1,973, 10%), and “slow decline and discontinuation” (n = 819, 4%). Very few patient variables were found to be associated with specific adherence trajectories.
Conclusions:
The authors were unable to identify clinical and demographic characteristics to predict patients’ adherence to anticoagulants.
Perspective:
The authors used the group-based trajectory modeling, as well as the proportion of days covered method to assess compliance with oral anticoagulants over a 5-year period to better understand the dynamic process of noncompliance, and they found the group-based trajectory modeling to be more informative over the oversimplified binary classification. The noncompliant patients were grouped into the following categories: rapid decline and discontinuation, rapid decline and partial recovery, and slow decline and discontinuation. Different adherence interventions would be expected to improve compliance depending on the patient’s specific compliance trajectory. Unfortunately, no clinical or demographic characteristics were found to presage the trajectory. Further study of the psychosocial determinants of anticoagulation nonadherence based on the above trajectories is very much needed.
Clinical Topics: Anticoagulation Management, Arrhythmias and Clinical EP, Geriatric Cardiology, Prevention, Anticoagulation Management and Atrial Fibrillation, Implantable Devices, SCD/Ventricular Arrhythmias, Atrial Fibrillation/Supraventricular Arrhythmias
Keywords: Anticoagulants, Arrhythmias, Cardiac, Atrial Fibrillation, Blood Coagulation, Data Management, Geriatrics, Patient Compliance, Risk Factors, Secondary Prevention, Stroke
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