在T2DM中构建基于糖尿病病风险的Nomogram预测模型
Xian Wang1, Xiaming Liu1, Jun Zhao1
1Graduate School of Chengde Medical College, Chengde, Hebei, People's Republic of China.
Diabetes, metabolic syndrome and obesity : targets and therapy
|January 17, 2024
概括
在2型糖尿病 (T2DM) 中糖尿病病 (DKD) 风险可以使用9个因素预测,包括糖尿病视网膜病变和高血压. 一种名ogram模型准确地评估DKD风险,帮助临床决策.
科学领域:
- 腎臟病學 (nephrology) 是一種醫學.
- 内分泌学 在内分泌学.
- 糖尿病学 糖尿病学
背景情况:
- 糖尿病病 (DKD) 是2型糖尿病 (T2DM) 的一个主要并发症.
- 预测DKD风险对于及时干预和管理至关重要.
- 现有的预测模型可能需要针对不同患者群体进行改进.
研究的目的:
- 在T2DM患者中确定DKD的关键预测因子.
- 开发和验证一个用于预测DKD风险的诺莫格拉姆模型.
- 评估开发的预测模型的临床实用性.
主要方法:
- 从T2DM患者的临床数据的回顾性分析.
- 单变量和多变量分析用于选DKD预测因子.
- 使用引导,ROC曲线和DCA构建和验证一个名ogram.
主要成果:
- 确定了九种DKD的显著预测因素:糖尿病视网膜病变,高血压,痛风史,吸烟,胰岛素使用,BMI,TG,Cys-C升高,以及25...OH) D降低.
- 诺米图表表现出良好的区分能力 (AUC=0.868) 和校准.
- 决策分析曲线表明,风险预测值在0.10和0.70之间具有临床可行性.
结论:
- 在T2DM患者中,使用临床和生化因素的组合,可以准确预测DKD风险.
- 开发的名录图为T2DM的风险分层和个性化管理提供了有价值的工具.
- 早期识别高风险个体可以促进主动策略,以防止或延迟DKD的进展.
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