Assessment of aspirin resistance in patients experiencing ischemic stroke

Purpose. To identify routine laboratory indicators associated with aspirin resistance (AR) in ischemic stroke patients and evaluate their predictive value using thromboelastography (TEG) as the criterion standard. Method. This retrospective cohort study analyzed clinical and laboratory data of 606 ischemic stroke patients (admission and 7 days post-antithrombotic therapy). The inhibition rate of arachidonic acid (AA) was utilized as the dependent variable in linear regression analysis, and a multivariate model was established. Receiver operating characteristic (ROC) curve analysis was used to validate the predictive model, and logistic regression was constructed using significant indicators to further validate the predictive efficacy. Result. Linear regression analysis identified clotting rate (Angle, B=0.30, 95%Cl=0.10~0.51, P=0.003), maximum blood clot strength (MA _AA , B=-1.65, 95%Cl=-1.80~-1.51, P<0.001), platelet average volume / lymphocyte count (MPVLR, B=-1.32, 95%Cl=-2.06~-0.58, P=0.001), Albumin (ALB, B=-0.85, 95%Cl=-1.31~-0.39, P<0.001), and Homocysteine (HCY, B=0.17, 95%Cl=0.03-0.32, P=0.017) as independent predictors of AR. A prognostic index equation was established based on these findings. The logistic regression model indicated that patients with high protein, high cholesterol, and those concurrently having hyperlipidemia, diabetes, coronary heart disease, and coronary artery stenting were more prone to develop aspirin resistance. Conclusion. Angle, MA _AA , MPVLR, ALB, and HCY are independently associated with aspirin resistance in ischemic stroke. A logistic regression model incorporating total protein, total cholesterol, hyperlipidemia, diabetes, coronary heart disease, and post-operative arterial stenting distinguish the aspirin-sensitive group from the aspirin-resistant group among patients with ischemic stroke, supporting personalized antiplatelet therapy strategies in clinical practice.