Shahrzad Salmasi1, Mary A De Vera2, Abdollah Safari3, Larry D Lynd4, Mieke Koehoorn5, Arden R Barry6, Jason G Andrade7, Marc W Deyell8, Kathy Rush9, Yinshan Zhao10, Peter Loewen11. 1. Collaboration for Outcomes Research & Evaluation (CORE), University of British Columbia, Vancouver, British Columbia, Canada; Faculty of Pharmaceutical Sciences, University of British Columbia, Vancouver, British Columbia, Canada. 2. Collaboration for Outcomes Research & Evaluation (CORE), University of British Columbia, Vancouver, British Columbia, Canada; Faculty of Pharmaceutical Sciences, University of British Columbia, Vancouver, British Columbia, Canada; Centre for Health Evaluation & Outcome Sciences, Providence Health Care Research Institute, Vancouver, British Columbia, Canada. 3. Faculty of Pharmaceutical Sciences, University of British Columbia, Vancouver, British Columbia, Canada; Data Analytics, Statistics and Informatics, Faculty of Pharmaceutical Sciences, University of British Columbia, Vancouver, British Columbia, Canada. 4. Collaboration for Outcomes Research & Evaluation (CORE), University of British Columbia, Vancouver, British Columbia, Canada; Faculty of Pharmaceutical Sciences, University of British Columbia, Vancouver, British Columbia, Canada; Centre for Health Evaluation & Outcome Sciences, Providence Health Care Research Institute, Vancouver, British Columbia, Canada; School of Population and Public Health, University of British Columbia, Vancouver, British Columbia, Canada. 5. School of Population and Public Health, University of British Columbia, Vancouver, British Columbia, Canada. 6. Faculty of Pharmaceutical Sciences, University of British Columbia, Vancouver, British Columbia, Canada. 7. Division of Cardiology, Faculty of Medicine, University of British Columbia, Vancouver, British Columbia, Canada; Atrial Fibrillation Clinic, Vancouver General Hospital, Vancouver, British Columbia, Canada. 8. Faculty of Medicine, University of British Columbia, Vancouver, British Columbia, Canada; UBC Center for Cardiovascular Innovation, Vancouver, British Columbia, Canada. 9. School of Nursing, Faculty of Health and Social Development, University of British Columbia Okanagan, Kelowna, British Columbia, Canada. 10. Faculty of Medicine, University of British Columbia, Vancouver, British Columbia, Canada. 11. Collaboration for Outcomes Research & Evaluation (CORE), University of British Columbia, Vancouver, British Columbia, Canada; Faculty of Pharmaceutical Sciences, University of British Columbia, Vancouver, British Columbia, Canada; UBC Center for Cardiovascular Innovation, Vancouver, British Columbia, Canada. Electronic address: peter.loewen@ubc.ca.
Abstract
BACKGROUND: Conventional adherence summary measures do not capture the dynamic nature of adherence. OBJECTIVES: This study aims to characterize distinct long-term oral anticoagulant adherence trajectories and the factors associated with them in patients with atrial fibrillation. METHODS: Adults with incident atrial fibrillation were identified using linked population-based administrative health data in British Columbia, Canada (1996-2019). Group-based trajectory modeling was used to model patients' 90-day proportions of days covered over time to identify distinct 5-year adherence trajectories. Multinomial 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 ± 10.6 years), 56% male, mean CHA2DS2-VASc stroke risk score 2.8 ± 1.4. Group-based trajectory modeling identified 4 distinct oral anticoagulants 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: There is heterogeneity among nonadherent patients in the rate and timing of decline in their medication taking. Clinical and demographic characteristics were found to be inadequate to predict patients' adherence trajectories. Insights from this study could be used to inform the design and timing of adherence interventions, and qualitative studies may be needed to better understand the psychosocial determinants and reasons for the behaviors reflected in the identified trajectories.
BACKGROUND: Conventional adherence summary measures do not capture the dynamic nature of adherence. OBJECTIVES: This study aims to characterize distinct long-term oral anticoagulant adherence trajectories and the factors associated with them in patients with atrial fibrillation. METHODS: Adults with incident atrial fibrillation were identified using linked population-based administrative health data in British Columbia, Canada (1996-2019). Group-based trajectory modeling was used to model patients' 90-day proportions of days covered over time to identify distinct 5-year adherence trajectories. Multinomial 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 ± 10.6 years), 56% male, mean CHA2DS2-VASc stroke risk score 2.8 ± 1.4. Group-based trajectory modeling identified 4 distinct oral anticoagulants 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: There is heterogeneity among nonadherent patients in the rate and timing of decline in their medication taking. Clinical and demographic characteristics were found to be inadequate to predict patients' adherence trajectories. Insights from this study could be used to inform the design and timing of adherence interventions, and qualitative studies may be needed to better understand the psychosocial determinants and reasons for the behaviors reflected in the identified trajectories.
Authors: Shaobo Shi; Qingyan Zhao; Tao Liu; Shujuan Zhang; Jinjun Liang; Yanhong Tang; Bo Yang; He Huang; Congxin Huang Journal: Front Cardiovasc Med Date: 2022-05-02