Using group-based trajectory modeling to characterize the association of past ACEIs/ARBs adherence with subsequent statin adherence patterns among new statin users

Despite well-documented benefits, statin adherence remains suboptimal. Studies have suggested that previous adherence to other chronic medications is a strong predictor of future adherence to newly initiated statins. Group-based trajectory modeling (GBTM) has been applied as a method to longitudinally depict the dynamic nature of adherence.This study aimed to examine the association between patients' adherence patterns to newly initiated statins and previous adherence trajectories of angiotensin-converting enzyme inhibitors (ACEIs) or angiotensin receptor blockers (ARBs) using GBTM.This retrospective cohort study was conducted among continuously enrolled statin initiators using claims data. Patients were included if they had ACEI/ARB use within 1 year before statin initiation (preindex period). Monthly adherence to ACEIs/ARBs was calculated during the preindex period and monthly adherence to statins was assessed 1 year after statin initiation using proportion of days covered (PDC). The monthly PDCs were modeled as a longitudinal response in a logistic GBTM to provide distinct patterns of adherence for ACEIs/ARBs and statins, separately. A multinomial logistic regression was conducted to determine an association between ACEI/ARB adherence trajectories and future statin trajectories, controlling for patient characteristics.A total of 1078 patients were categorized into 4 distinct statin adherence trajectories: adherent (40.8%), gradual decline (37.4%), gaps in adherence (13.9%), and rapid decline (7.9%). Patients were further categorized into 4 groups on the basis of their distinct past ACEIs/ARBs trajectories: adherent (43%), gaps in adherence (29%), delayed nonadherence (15.2%), and gradual decline (12.8%). In the multinomial logistic regression, patients in the gaps in adherence or gradual decline groups were more likely to follow similar trajectories for future statin use than the adherent trajectory.Previous adherence trajectories of ACEIs/ARBs may predict future adherence patterns for newly initiated statins. Knowledge of past medication-taking behavior could provide valuable information for developing tailored interventions to improve adherence.