Construction and evaluation of an aspirin resistance risk prediction model for ischemic stroke

Background Aspirin has become the drug of choice for the prevention and treatment of ischemic stroke, but approximately a quarter of patients may be resistant to its effects and have an increased risk of recurrent ischemic events while also developing aspirin resistance. This study aimed to build a risk prediction model for AR in IS patients, predicts the likelihood of IS patients developing AR . Methods The retrospective research study included the clinical data of patients with ischemic stroke were retrospectively collected from January 2021 to January 2023 at the Affiliated Hospital of Beihua University in the Jilin Province. Univariate and logistic regression analyses were used to construct a risk prediction model. The Hosmer–Lemeshow χ2 test and a receiver operating characteristic (ROC) curve were used to check the differential validity and calibration of the risk prediction model. The AR risk assessment criteria for ischemic stroke were established based on the β values of each risk factor and its variable types in the prediction model. The two evaluation criteria were compared and analyzed to determine the best criteria. Results Seven risk factors were included in the prediction model. Sex (female), age (≥ 60 years), smoking, diabetes, hyperlipidemia, platelets > 350 × 10 ⁹ g/L, and glycosylated hemoglobin > 6.5% were independent influencing factors for the occurrence of AR in ischemic stroke. The area under the ROC curve (AUC) of the risk score model in the training group was 0.834 (0.772–0.896, P < 0.001). The Hosmer–Lemeshow test predicted the model fit effect χ ² = 9.979, P = 0.267 > 0.05. In the validation group, the AUC was 0.819 (0.715–0.922, P < 0.001). Using the β value × 4 partial regression coefficient method, the scores and stratification of the AR risk prediction model were divided into three groups: no risk (0–3 points), low risk (4–15 points), and high risk (16–36 points). Conclusions The AR ischemic stroke risk prediction model has strong prediction and assessment capabilities, enabling the precise identification of patients at risk of AR.