GW24-e3522 Establishing of aspirin resistance incident prediction model for the old patients with chronic coronary heart disease

Objectives Coronary heart disease is one of the leading causes of mortality in women and men in the world. Aspirin use for the primary and secondary prevention of coronary heart disease reduces the risk of cardiovascular events. However, it appears that aspirin’s antiplatelet effect may not be uniform in all patients. Clinical aspirin resistance has included patients who, despite being on therapeutic doses of aspirin, experience thrombotic or embolic vascular events. Therefore, it is very important to identify high risk population who are more likely to develop aspirin resistance and then to conduct interventions at early stage. Methods To establish the prediction models, 1130 patients with stable angina who take aspirin (75-100 mg) for more than 2 months were included. Platelet aggregation was measured by light transmission aggregometry (LTA) and thrombelastography platelet mapping assay (TEG). And ROC approach of the interviewees to define the best cut point of the model with its sensitivity and specificity was applied. Results Seven risk factors were included in the model. Risk score was finally set up according to the coefficient B and rank of variables in logistic regression model (logit = exp (B 0 + B1X1 + B2X2 + … + BnXn). Our risk model showed good calibration and discriminative power in which Hosmer-Lemeshow test’s P value were greater than 0.05 and the area under the ROC curve were greater than 0.70. Results in our risk score: serum creatinine>110 umol/l: 1 score, fasting blood glueose>7.0 mmol/L:1 score, hyperlipidemia: 1 score, number of coronary artery lesion (2 branches:2 score, ≥3branches; 4 score), Body mass index: 20-25 kg/m 2 :2 score; >25 kg/m 2 : 4 score). PCI: 2 score, smoking: 3 score. Conclusions High levels of serum creatinine, fasting blood glucose and blood lipid, number of coronary artery lesion (2 branches:2 score, ≥3branches; 4 score),high body mass index and PCI history are risk factors for aspirin resistance. The incidence prediction model of aspirin resistance is effective to identify high risk population.