| Literature DB >> 28436725 |
Liping Du1, Zhongwei Cheng2, Yuxuan Zhang1, Ying Li1, Dan Mei1.
Abstract
Background Long-term use of evidence-based medications is recommended by international guidelines for the management of stable coronary artery disease, however, non-adherence to medications is common. This meta-analysis aims to systematically evaluate the impact of medication adherence on clinical outcomes in patients with stable coronary artery disease. Methods Articles from January 1960-December 2015 were retrieved from the MEDLINE and EMBASE databases without any language restriction. A meta-analysis was performed to investigate the risk ratios of all-cause mortality, cardiovascular mortality, and myocardial infarction/hospitalization between groups with good medication adherence and poor medication adherence. Studies were independently reviewed by two investigators. Data from eligible studies were extracted, and the meta-analysis was performed using R Version 3.1.0 software. Results A total of 10 studies were included in the analysis, with a total of 106,002 coronary artery disease patients. The results showed that good adherence to evidence-based medication regimens, including β-blockers, angiotensin-converting enzyme inhibitors/angiotensin receptor blockers, antiplatelet drugs, and statins, was related to a lower risk of all-cause mortality(risk ratio 0.56; 95% confidence interval: 0.45-0.69), cardiovascular mortality(risk ratio 0.66; 95% confidence interval: 0.51-0.87), and cardiovascular hospitalization/myocardial infarction(risk ratio 0.61; 95% confidence interval: 0.45-0.82). Conclusions This meta-analysis confirms the significant impact of good medication adherence on clinical outcomes in patients with stable coronary artery disease. More strategy and planning are needed to improve medication adherence.Entities:
Keywords: Medication adherence; coronary artery disease; mortality; secondary prevention
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Year: 2017 PMID: 28436725 DOI: 10.1177/2047487317695628
Source DB: PubMed Journal: Eur J Prev Cardiol ISSN: 2047-4873 Impact factor: 7.804