Abstract: Objective To explore the factors associated with major adverse cardiovascular events (MACE) in patients with hyperuricemia and to establish a corresponding risk assessment model. Methods A retrospective analysis was conducted on the data of 441 patients with hyperuricemia treated at Shijingshan Hospital, Beijing. Patients were divided into a modeling cohort (n=309) and a validation cohort (n=132), and further stratified based on the occurrence of MACE. The clinical data of the two groups were compared. Logistic regression analysis was used to analyze the influencing factors of hyperuricemia complicated with MACE. Based on these factors, a nomogram model was constructed and its predictive performance was validated. Results The overall incidence of MACE was 30.61%.The age, body massindex (BMI), suboptimal glycemic control, inadequate lipid management, blood uric acid (BUA), cystatin C (Cys C), and hypersensitive C-reactive protein (hs-CRP) were identified as the influencing factors of hyperuricemia combined with MACE (P＜0.05). The nomogram incorporating these variables demonstrated excellent discrimination, with area under the receiver operating characteristic curve (AUC) values of 0.939 in the modeling cohort and 0.872 in the validation cohort. The calibration curve showed high consistency between the predicted and actual probabilities of hyperuricemia complicated with MACE in both cohort. Furthermore, the Hosmer-Lemeshow test indicated good model fit. Conclusions Age, BMI, poor glycemic control, suboptimal lipid regulation, BUA, Cys C, hs-CRP are the influencing factors of hyperuricemia combined with MACE. The developed nomogram exhibits robust predictive accuracy and may serve as a practical tool for individualized risk assessment.

Key words: hyperuricemia, major adverse cardiovascular events, risk, assessment model