Department of Rheumatology, Huashan Hospital, Fudan University, Shanghai, China.
Department of Epidemiology and Biostatistics, School of Public Health, Anhui Medical University, Hefei, Anhui, China.
Int J Rheum Dis. 2024 Jun;27(6):e15205. doi: 10.1111/1756-185X.15205.
To construct a risk prediction model for atherosclerotic cardiovascular disease (ASCVD) in patients with hyperuricemia.
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.
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.
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.
构建高尿酸血症患者动脉粥样硬化性心血管疾病（ASCVD）的风险预测模型。
本研究数据来源于国家健康与营养调查（NHANES）（2007-2010 年）。华山医院的患者作为外部验证纳入研究。采用 logistic 回归分析探讨高尿酸血症患者 ASCVD 的相关因素。采用受试者工作特征曲线下面积（AUC）评估模型的判别能力。Hosmer-Lemeshow 检验、校正曲线和决策曲线分析（DCA）用于评估模型。
最终纳入 389 例来自 NHANES 的患者进行分析。logistic 回归分析显示，年龄、肌酐（Cr）、葡萄糖（Glu）、血清尿酸（SUA）和痛风史是高尿酸血症（HUA）患者 ASCVD 的预测因素。这些预测因素被用于构建列线图。其中，157 例患者来自 NHANES 用于内部验证，136 例患者来自华山医院用于外部验证。三组的 AUC 值分别为 0.943、0.735 和 0.664。Hosmer-Lemeshow 检验的 p 值分别为 0.568、0.600 和 0.763。校准曲线显示列线图与实际观测值之间具有一致性。DCA 曲线表明该模型具有良好的临床实用性。
本研究构建了 HUA 患者 ASCVD 风险预测模型，有助于医务人员早期发现 ASCVD 的高危人群。
