Responsiveness to antiplatelet therapy is a clinically important issue and there is a need to develop individual antiplatelet strategies particularly for patients at risk. Many studies have shown that this responsiveness is not a theoretical one.
Further studies are needed to find out whether a personalized antiplatelet therapy can improve platelet inhibition and net clinical outcome in patients identified by non-genetic and genetic risk analysis.
Prof Gawaz and Geisler, from the Tuebingen University Hospital in Germany have developed the PREDICT-score, that allows the probability of a high RPA to be estimated, as well as the risk of short-term thromboischaemic events.
Safeguarding of antiplatelet drug efficacy is important for the optimal treatment of patients with symptomatic coronary artery disease, requiring coronary interventions. This represents a challenge to modern cardiology since there has been cumulative evidence that response to common oral antiplatelet therapy is a highly variable phenomenon underlying various mechanisms.
It is known that particular risk groups exhibit high residual platelet aggregability (RPA) despite conventional dual antiplatelet therapy with acetylic salicylic acid and clopidogrel. There is also a relevant association between high RPA and recurrent ischemic events especially stent thrombosis after percutaneous coronary intervention. Individualization of antiplatelet therapy by dose increase or alternative application of novel P2Y12-receptor antagonists (Prasugrel, Ticagrelor, Cangrelor) might therefore lead to improved cardiovascular outcome in defined risk patients. For this reason, measurement of response by point-of-care platelet function tests is reasonable in particular risk patients.
Some clinical conditions are synonymous with increased residual platelet reactivity. Recently, Profs Gawaz and Geisler, developed a score (Residual Platelet Aggregation after Deployment of Intracoronary Stent (PREDICT)-score) to identify the individual risk for poor responsiveness to clopidogrel by non-genetic factors. 1092 PCI-patients were investigated in this study. Clinical and demographic factors were included in univariate analysis and thus possible influencing factors were identified. These factors were then entered into a multivariate model. Thus, increased age (>65 years), acute coronary syndrome, reduced left ventricular function and presence of diabetes mellitus or renal failure could be identified as independent determinants for a high RPA. By weighing these variables according to their statistical influence (for example weighing factor 1 for acute coronary syndrome, age, 2 for diabetes and renal failure, and 3 for reduced left ventricular function) we developed the PREDICT-score, that allows the probability of a high RPA to be estimated, as well as the risk of short-term thromboischaemic events. This can be seen from easily available clinical data.