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

BACKGROUND: Resistance 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. METHODS: Elderly 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. RESULTS: Seven 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/m(2), score of 2; > 25 kg/m(2), 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. CONCLUSIONS: We 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.