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

Abstract

ObjectiveTo identify adherence patterns over time and their predictors for evidence-based medications used after hospitalization for coronary heart disease (CHD).Patients and MethodsWe 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.ResultsWe 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.ConclusionDistinct 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.

Opening the black box of the group‐based trajectory modeling process to analyze medication adherence patterns: An example using real‐world statin adherence data

“…As Nagin recommended, we first selected the appropriate number of groups before choosing the optimal functional form for each group. We modeled adherence using 2–7 groups with all quadratic polynomials since we had <12 time‐points . The Bayesian information criterion (BIC) and Bayes Factor were used to select the optimal number of groups .…”

“…The OCC measures predictive capacity of group assignment from the maximum PP rule (OCC=1 suggests group assignment no better than random). We additionally calculated relative entropy, a measure of uncertainty in group classification bound between 0 and 1 (higher certainty as value approaches 1) …”

“…We modeled adherence using 2-7 groups with all quadratic polynomials since we had <12 time-points. 8 The Bayesian information criterion (BIC) and Bayes Factor were used to select the optimal number of groups. 2 If these indices continued to improve with additional groups, 3,5,9 we used clinical meaningfulness to select the optimal number of groups: e.g., if adding a new group (6 th group) resulted in 2 groups with similar adherence patterns, the previous model (5 groups) was selected.…”

Clinician — Treats, and Patient — Being Treated? Recent View on the Treatment Adherence in Coronary Heart Disease Setting

“…Нехватка мотивирующих факторов (социальная поддержка, видимость пользы/изменений) или наличие демоти-вирующих (усилия, неудобство, негативная оценка извне) ведут к возврату на уровень отсутствия дан-ной деятельности [28]. В исследовании Librero, et al (2016) произведена попытка выделить паттерны низкой приверженно-сти. В Испании была создана когорта больных, выпи-санных после госпитализации по поводу ишемиче-ской болезни сердца (n=7462).…”

“…Паттерны с постепен-ным снижением были более характерны для бета-блокаторов и блокаторов ренин-ангиотензиновой системы, нежели статинов и антитромбоцитарных. Предиктором низкой приверженности был диагноз хронической ИБС или нестабильной стенокардии по сравнению с диагнозом ИМ [29].…”