Prediction of high risk of non-adherence to antiplatelet treatment

Aldona Kubica, Karolina Obońska, Michał Kasprzak

  • 1Department of Principles of Clinical Medicine, Collegium Medicum, Nicolaus Copernicus University, Bydgoszcz, Poland. gosiawikta@o2.pl.

Kardiologia Polska
|June 24, 2015
PubMed

Insights

Identifying patients at high risk of clopidogrel non-adherence after myocardial infarction (MI) is crucial. A new objective method using adenosine diphosphate-induced platelet aggregation (ADP-PA) can help predict non-adherence, improving secondary prevention strategies.

Area of Science:

  • Cardiology
  • Pharmacology
  • Clinical Medicine

Background:

  • Dual antiplatelet therapy (DAPT) with aspirin (ASA) and clopidogrel is standard for secondary prevention post-myocardial infarction (MI).
  • Premature clopidogrel discontinuation increases risks of MI, death, and healthcare costs.

Purpose of the Study:

  • To develop an objective method for identifying patients at high risk of clopidogrel non-adherence after MI.
  • To assess the utility of adenosine diphosphate-induced platelet aggregation (ADP-PA) in predicting medication adherence.

Main Methods:

  • Prospective, observational study of 189 patients receiving DAPT (clopidogrel + ASA) post-MI.
  • Adenosine diphosphate-induced platelet aggregation (ADP-PA) measured at baseline and 3, 6, and 9 months post-discharge.
  • Medication adherence defined by drug availability (>80% proportion).

Main Results:

  • An optimal ADP-PA cut-off of 4 U was identified to predict non-adherence (p < 0.0001).
  • The method differentiated adherence probability: 50.8% true adherence in the high-risk group vs. 32.4% in the low-risk group (p = 0.01).
  • ADP-PA was higher in non-adherent patients compared to baseline measurements.

Conclusions:

  • A novel, easily applicable, and cost-effective method for objectively identifying patients at high risk of clopidogrel non-adherence post-MI was developed.
  • Despite moderate sensitivity and specificity, the method effectively differentiates patients regarding clinical outcomes during follow-up.
  • This tool aids in optimizing secondary prevention strategies by identifying patients needing adherence support.
Abstract

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