Establishment and validation of prediction model for atherosclerotic cardiovascular disease in patients with hyperuricemia

Objective To construct a risk prediction model for atherosclerotic cardiovascular disease (ASCVD) in patients with hyperuricemia. Methods Data in this study were obtained from the National Health and Nutrition Examination Survey (NHANES) (2007–2010). Participants from Huashan Hospital were included as an external validation. Logistic regression analysis was used to explore the relevant factors of ASCVD in patients with hyperuricemia. The discriminability of the model was evaluated using the area under the curve (AUC) statistic of the receiver operating characteristic curve. Hosmer–Lemeshow test, correction curve and decision curve analysis (DCA) were used to evaluate the model. Results A total of 389 patients collected from the NHANES were included in the final analysis. Logistic regression analysis showed that age, creatinine (Cr), glucose (Glu), serum uric acid (SUA), and history of gout were predictive factors for ASCVD in hyperuricemia (HUA) patients. These predictive factors were used to construct a nomogram. And 157 patients from NHANES were in the internal validation group and 136 patients from Huashan Hospital were in the external validation group. The AUC values of the three groups were 0.943, 0.735, and 0.664. The p values of the Hosmer–Lemeshow test were .568, .600, and .763. The calibration curve showed consistency between the nomogram and the actual observed values. The DCA curve indicated that the model has good clinical practicality. Conclusion This study constructed the ASCVD risk prediction model for HUA patients, which is beneficial for medical staff to detect high‐risk populations of ASCVD in the early stage.