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Several clinical and genetic variables are associated with influencing high on treatment platelet reactivity (HTPR). The aim of the study was to propose a path model explaining a concurrent impact among variables influencing HTPR and ischemic events. In this prospective cohort study polymorphisms of CYP2C19*2, CYP2C19*17, ABCB1, PON1 alleles and platelet function assessed by Multiple Electrode Aggregometry were assessed in 416 patients undergoing percutaneous coronary intervention treated with clopidogrel and aspirin. The rates of major adverse cardiac events (MACE) were recorded during a 12-month follow up. The path model was calculated by a structural equation modelling. Paths from two clinical characteristics (diabetes mellitus and acute coronary syndrome (ACS)) and two genetic variants (CYP2C19*2 and CYP2C19*17) independently predicted HTPR (path coefficients: 0.11 0.10, 0.17, and -0.10, respectively; p<0.05 for all). By use of those four variables a novel score for prediction of HTPR was built: in a factor-weighted model the risk for HTPR was calculated with an OR of 3.8 (95%CI: 3.1–6.8, p<0.001) for a score level of ≥1 compared with a score of <1. While MACE was independently predicted by HTPR and age in the multivariate model (path coefficient: 0.14 and 0.13, respectively; p<0.05), the coexistence of HTPR and age ≥75 years emerged as the strongest predictor of MACE. Our study suggests a pathway, which might explain indirect and direct impact of variables on clinical outcome: ACS, diabetes mellitus, CYP2C19*2 and CYP2C19*17 genetic variants independently predicted HTPR. In turn, age ≥75 years and HTPR were the strongest predictors of MACE.

plos one

Interplay between Genetic and Clinical Variables Affecting Platelet Reactivity and Cardiac Adverse Events in Patients Undergoing Percutaneous Coronary Intervention