Establishing a predictive model for aspirin resistance in elderly Chinese patients with chronic cardiovascular disease.

Abstract

BackgroundResistance to anti-platelet therapy is detrimental to patients. Our aim was to establish a predictive model for aspirin resistance to identify high-risk patients and to propose appropriate intervention.MethodsElderly patients (n = 1130) with stable chronic coronary heart disease who were taking aspirin (75 mg) for > 2 months were included. Details of their basic characteristics, laboratory test results, and medications were collected. Logistic regression analysis was performed to establish a predictive model for aspirin resistance. Risk score was finally established according to coefficient B and type of variables in logistic regression. The Hosmer–Lemeshow (HL) test and receiver operating characteristic curves were performed to respectively test the calibration and discrimination of the model.ResultsSeven risk factors were included in our risk score. They were serum creatinine (> 110 μmol/L, score of 1); fasting blood glucose (> 7.0 mmol/L, score of 1); hyperlipidemia (score of 1); number of coronary arteries (2 branches, score of 2; ≥ 3 branches, score of 4); body mass index (20–25 kg/m2, score of 2; > 25 kg/m2, score of 4); percutaneous coronary intervention (score of 2); and smoking (score of 3). The HL test showed P ≥ 0.05 and area under the receiver operating characteristic curve ≥ 0.70.ConclusionsWe explored and quantified the risk factors for aspirin resistance. Our predictive model showed good calibration and discriminative power and therefore a good foundation for the further study of patients undergoing anti-platelet therapy.