Assessing Concurrent Adherence to Combined Essential Medication and Clinical Outcomes in Patients With Acute Coronary Syndrome. A Population-Based, Real-World Study Using Group-Based Trajectory Models.

acute coronary syndrome clinical outcomes concomitant medications concurrent adherence group-based trajectory models population-based cohort real-world data

Journal

Frontiers in cardiovascular medicine
ISSN: 2297-055X
Titre abrégé: Front Cardiovasc Med
Pays: Switzerland
ID NLM: 101653388

Informations de publication

Date de publication:
2022
Historique:
received: 27 01 2022
accepted: 21 03 2022
entrez: 13 6 2022
pubmed: 14 6 2022
medline: 14 6 2022
Statut: epublish

Résumé

Adherence to multiple medications recommended for secondary prevention of cardiovascular conditions represents a challenge. We aimed to identify patterns of concurrent adherence to combined therapy and assess their impact on clinical outcomes in a cohort of patients with acute coronary syndrome (ACS). Population-based retrospective cohort of all patients discharged after hospitalization for ACS (2009-2011), prescribed ≥3 therapeutic groups within the first month. We assessed monthly concurrent adherence (≥24 days of medication out of 30) to ≥3 medications during the first year, and patterns were identified through group-based trajectory models. A composite clinical outcome during the second year was constructed. The association between adherence patterns and traditional refill adherence metrics [e.g., the proportion of days covered (PDC)], and outcomes were assessed through a multivariable Cox proportional hazards model. Among 15,797 patients discharged alive, 12,057 (76.32%) initiated treatment with ≥3 therapeutic groups after discharge. We identified seven adherence trajectories to ≥3 medications: Adherent (52.94% of patients); Early Gap (6.64%); Middle Gap (5.67%); Late Decline (10.93%); Occasional Users (5.45%); Early Decline (8.79%); Non-Adherent (9.58%). Compared to the Adherent group, patients belonging to Early Gap (HR:1.30, 95%CI 1.07;1.60), Late decline (hazards ratio (HR): 1.31, 95% CI 1.1; 1.56), and Non-Adherent trajectories (HR: 1.36, 95% CI 1.14; 1.63) had a greater risk of adverse clinical outcomes, which was also different to the risk ascertained through concurrent PDC < 80 (HR: 1.13, 95% CI 1.01; 1.27). Overall, seven adherence trajectories to ≥3 drugs were identified, with three distinct adherence patterns being at higher risk of adverse outcomes. The identification of patterns of concurrent adherence, a more comprehensive approach than traditional measurements, may be useful to target interventions to improve adherence to multiple medications.

Identifiants

pubmed: 35694663
doi: 10.3389/fcvm.2022.863876
pmc: PMC9174582
doi:

Types de publication

Journal Article

Langues

eng

Pagination

863876

Informations de copyright

Copyright © 2022 Rodríguez-Bernal, Sánchez-Saez, Bejarano-Quisoboni, Hurtado, García-Sempere, Peiró and Sanfélix-Gimeno.

Déclaration de conflit d'intérêts

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

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Auteurs

Clara L Rodríguez-Bernal (CL)

Health Services Research Unit, The Foundation for the Promotion of Health and Biomedical Research of Valencia Region (FISABIO), Valencia, Spain.
Red de Investigación en Servicios de Salud en Enfermedades Crónicas (REDISSEC), Valencia, Spain.

Francisco Sánchez-Saez (F)

Health Services Research Unit, The Foundation for the Promotion of Health and Biomedical Research of Valencia Region (FISABIO), Valencia, Spain.
Red de Investigación en Servicios de Salud en Enfermedades Crónicas (REDISSEC), Valencia, Spain.

Daniel Bejarano-Quisoboni (D)

Health Services Research Unit, The Foundation for the Promotion of Health and Biomedical Research of Valencia Region (FISABIO), Valencia, Spain.

Isabel Hurtado (I)

Health Services Research Unit, The Foundation for the Promotion of Health and Biomedical Research of Valencia Region (FISABIO), Valencia, Spain.
Red de Investigación en Servicios de Salud en Enfermedades Crónicas (REDISSEC), Valencia, Spain.

Anibal García-Sempere (A)

Health Services Research Unit, The Foundation for the Promotion of Health and Biomedical Research of Valencia Region (FISABIO), Valencia, Spain.
Red de Investigación en Servicios de Salud en Enfermedades Crónicas (REDISSEC), Valencia, Spain.

Salvador Peiró (S)

Health Services Research Unit, The Foundation for the Promotion of Health and Biomedical Research of Valencia Region (FISABIO), Valencia, Spain.
Red de Investigación en Servicios de Salud en Enfermedades Crónicas (REDISSEC), Valencia, Spain.

Gabriel Sanfélix-Gimeno (G)

Health Services Research Unit, The Foundation for the Promotion of Health and Biomedical Research of Valencia Region (FISABIO), Valencia, Spain.
Red de Investigación en Servicios de Salud en Enfermedades Crónicas (REDISSEC), Valencia, Spain.

Classifications MeSH