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

Abstract

Objective: 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 medication were collected. Logistic regression analysis was performed to establish a predictive model for aspirin resistance. Risk score was finally established according to the coefficient B and type of variables in logistic regression. The Hosmer-Lemeshow (HL) test and a receiver operating characteristic curve 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 mol/L: score of 1); fasting blood glucose (>7.0 mmol/L: score of 1); hyperlipidaemia (score of 1); number of diseased 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). HL test showed P≥0.05 and area under the receiver operating characteristic curve ≥0.70. Conclusion: We explored and quantified the risk factors for aspirin resistance. Our predictive model showed good calibration and discriminative power. So it would be a good foundation for the further study of patients undergoing anti-platelet therapy.