2型糖尿病患者发生心血管事件的危险因素分析及列线图预测模型构建-东南大学学报医学版

2型糖尿病患者发生心血管事件的危险因素分析及列线图预测模型构建

单位：石家庄市第二医院内分泌科, 河北石家庄 050000

分类号：R587.1

出版年·卷·期（页码）：2026·45·第一期（78-85）

摘要：

目的:构建2型糖尿病(T2DM)患者发生心血管事件(CVD)的列线图预测模型。方法:收集2017年1月至2019年1月石家庄市第二医院1 168例T2DM患者的完整资料,对患者均进行为期5年的随访,依据患者在随访期内是否新发生CVD分为CVD组和N-CVD组。对完成筛选导入的患者资料进行总结,多因素Logistic回归模型分析影响因素;构建预测T2DM患者发生CVD风险的列线图模型。CVD预测模型的区分度和一致性用受试者工作特征(ROC)曲线、校准曲线评估,绘制决策曲线分析(DCA)曲线,对模型临床净收益进行评估。结果:CVD组诊断年龄、病程10年及以上、吸烟、高血压、糖化血红蛋白(HbA1C)、空腹血糖、总胆固醇(TC)、血尿酸、血肌酐、尿素氮、每周运动时间与N-CVD组相比,差异均有统计学意义(P<0.05)。Logistic回归分析结果显示,诊断年龄、病程、吸烟、HbA1C、血尿酸均是CVD的影响因素(P<0.05)。将诊断年龄、病程、吸烟、HbA1C、血尿酸作为影响因素,建立预测CVD的列线图模型。区分度和一致性评估表明,模型区分度和一致性均较高。DCA曲线分析显示,当高风险阈值为0.05~0.78时,模型预测T2DM患者CVD风险的净获益率较高。结论:诊断年龄、病程、吸烟、HbA1C、血尿酸均是T2DM患者发生CVD的影响因素,本研究建立的列线图预测模型实用性较高。

Objective: To construct a nomogram prediction model for cardiovascular events(CVD) in patients with type 2 diabetes mellitus(T2DM). Methods: Complete data from 1 168 T2DM patients at Shijiazhuang Second Hospital were collected from January 2017 to January 2019, with all patients followed up for 5 years and divided into the CVD group and N-CVD group according to the occurrence of new-onset CVD during follow-up. Data from eligible patients were summarized, and multivariate Logistic regression was used to analyze influencing factors. A nomogram model for predicting the risk of CVD events in T2DM patients was constructed. The discrimination and calibration of the CVD prediction model were evaluated using receiver operating characteristic(ROC) curves and calibration curves. Clinical net benefit was assessed with decision curve analysis(DCA). Results: Compared with the N-CVD group, the CVD group showed statistically significant differences in age at diagnosis, disease duration ≥10 years, smoking, hypertension, HbA1C, fasting blood glucose, TC, blood uric acid, serum creatinine, urea nitrogen, and weekly exercise duration(P<0.05). Logistic regression analysis revealed that age at diagnosis, disease duration, smoking, HbA1C, and blood uric acid were influencing factors for CVD in T2DM patients(P<0.05). A nomogram prediction model for CVD was constructed using these five factors. Evaluation results indicated that the model had good discrimination and calibration ability. DCA curve analysis showed that the model yielded a relatively high net benefit rate in predicting CVD risk in T2DM patients when the high-risk threshold ranged from 0.05 to 0.78. Conclusion: Age at diagnosis, disease duration, smoking, HbA1C, and blood uric acid are influencing factors for CVD in T2DM patients. The nomogram prediction model developed in this study demonstrates high practical value.

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