Interventions to improve medication adherence in coronary disease patients: A systematic review and meta-analysis of randomised controlled trials

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Abstract

Background:

Adherence to multiple cardiovascular (CV) medications is a cornerstone of coronary heart disease (CHD) management and prevention, but it is sub-optimal worldwide. This review aimed to examine whether interventions improve adherence to multiple CV medications in a CHD population.

Design:

This study was based on a systematic review and meta-analysis according to Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) guidelines.

Methods:

Randomised controlled trials were identified by searching multiple databases and reference lists. Studies were selected if they evaluated interventions aiming to improve adherence to multiple CV medications targeting a CHD population and if they provided an appropriate measure of adherence. Interventions were classified as complex or simple interventions. Odds ratios (ORs) were calculated and pooled for a meta-analysis. Risk of bias, heterogeneity and publication bias were also assessed.

Results:

Sixteen studies (10,706 patients) were included. The mean age was 62 years (standard deviation (SD) 3.6) and 72% were male. In a pooled analysis, the interventions significantly improved medication adherence (OR 1.52; 95% confidence interval (CI) 1.25-1.86; p < 0.001) and there were no significant differences based on intervention type (complex vs simple), components categories and adherence method. There was moderate heterogeneity (I2 = 61%) across the studies. After adjusting for publication bias, the effect size was attenuated but remained significant (OR 1.35; 95% CI 1.09-1.68).

Conclusion:

Interventions to improve adherence to multiple CV medication in a CHD population significantly improved the odds of being adherent. Simple one-component interventions might be a promising way to improve medication adherence in a CHD population, as they would be easier to replicate in different settings and on a large scale.

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