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Medication Adherence Patterns after Hospitalization for Coronary Heart Disease. A Population-Based Study Using Electronic Records and Group-Based Trajectory Models

OBJECTIVE: To identify adherence patterns over time and their predictors for evidence-based medications used after hospitalization for coronary heart disease (CHD). PATIENTS AND METHODS: We built a population-based retrospective cohort of all patients discharged after hospitalization for CHD from pu...

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Detalles Bibliográficos
Autores principales: Librero, Julián, Sanfélix-Gimeno, Gabriel, Peiró, Salvador
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2016
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4995009/
https://www.ncbi.nlm.nih.gov/pubmed/27551748
http://dx.doi.org/10.1371/journal.pone.0161381
Descripción
Sumario:OBJECTIVE: To identify adherence patterns over time and their predictors for evidence-based medications used after hospitalization for coronary heart disease (CHD). PATIENTS AND METHODS: We built a population-based retrospective cohort of all patients discharged after hospitalization for CHD from public hospitals in the Valencia region (Spain) during 2008 (n = 7462). From this initial cohort, we created 4 subcohorts with at least one prescription (filled or not) from each therapeutic group (antiplatelet, beta-blockers, ACEI/ARB, statins) within the first 3 months after discharge. Monthly adherence was defined as having ≥24 days covered out of 30, leading to a repeated binary outcome measure. We assessed the membership to trajectory groups of adherence using group-based trajectory models. We also analyzed predictors of the different adherence patterns using multinomial logistic regression. RESULTS: We identified a maximum of 5 different adherence patterns: 1) Nearly-always adherent patients; 2) An early gap in adherence with a later recovery; 3) Brief gaps in medication use or occasional users; 4) A slow decline in adherence; and 5) A fast decline. These patterns represented variable proportions of patients, the descending trajectories being more frequent for the beta-blocker and ACEI/ARB cohorts (16% and 17%, respectively) than the antiplatelet and statin cohorts (10% and 8%, respectively). Predictors of poor or intermediate adherence patterns were having a main diagnosis of unstable angina or other forms of CHD vs. AMI in the index hospitalization, being born outside Spain, requiring copayment or being older. CONCLUSION: Distinct adherence patterns over time and their predictors were identified. This may be a useful approach for targeting improvement interventions in patients with poor adherence patterns.