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一种诺莫格拉姆模型用于预测中国成年人糖尿病前期的5年风险

Yanhua Hu¹, Yong Han^2,3, Yufei Liu^4,5

¹College of Information Science and Engineering, Liuzhou Institute of Technology, Liuzhou, 545616, Guangxi Zhuang Autonomous Region, China.

Scientific reports

|December 19, 2023

概括

一种新的诺米图准确地预测了中国成年人使用年龄,BMI,FBG,TG,SBP和SCR的5年糖尿病前期风险. 这种工具有助于早期识别和干预患有糖尿病前期发展高风险的个体.

科学领域:

内分泌学在内分泌学.
预防医学预防医学
生物统计学生物统计学

背景情况:

早期发现糖尿病前期是有效干预的关键.
中国成年人代表了一大批面临糖尿病前期风险的人口.
现有的风险预测模型可能缺乏个性化和准确性.

研究的目的:

开发和验证一个个性化的名谱,用于预测中国成年人5年糖尿病前期风险.
为了比较各种预测模型的性能,包括机器学习和传统的统计方法.
为早期风险评估提供一种临床适用工具.

主要方法:

一个回顾性队列研究,涉及184,188名中国成年人,在基线没有糖尿病前期.
随机分配到培训 (92,177) 和验证 (92,011) 队列中.
五种预测模型的比较:完整的Cox,步进的Cox,多变量分数多项式 (MFP),机器学习和最小绝对收缩和选择运算符 (LASSO).
通过使用名图 (nomogram) 验证所选择的 LASSO 模型,并通过 ROC 曲线,决策曲线分析和校准进行评估.

主要成果:

糖尿病前期的5年发病率在两个队列中大约为10.70%.
开发了一种基于LASSO的名录,包括年龄,BMI,FBG,TG,SBP和Scr.
该名录显示出良好的区分能力,AUC为0.7341 (训练) 和0.7336 (验证).
通过决策曲线和校准分析证实了优良的校准和临床效用.

结论:

一个经过验证,个性化的名图有效地预测了中国成年人的5年糖尿病前期风险.
随时可用的参数使用的nomogram为临床医生提供了一个实用的工具.
早期识别高风险个体可以促进及时干预,以防止糖尿病前期的进展.

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