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开发和验证非酒精性脂肪性肝病 (NAFLD) 基于名录的风险预测模型:在身体检查人口中进行后勤回归分析

Jing Lan¹, Ting Que², Hong Lan³

¹Department of Health Examination Center, Maternal and Child Health Hospital of Guangxi Zhuang Autonomous Region, Nanning, China.

BMC gastroenterology

|July 24, 2025

概括

一种针对非酒精性脂肪性肝病 (NAFLD) 的新查模型整合了代谢和非代谢因素. 这种强大的工具有助于在例行体检期间在不同人群中进行早期检测.

科学领域:

肝病学肝病学是一种肝病学.
公共卫生公共卫生
生物统计学生物统计学

背景情况:

全球非酒精性脂肪肝 (NAFLD) 患病率为25%,需要改进查.
目前的NAFLD预测模型由于依赖西方代谢数据而缺乏通用性.
为不同人群开发了一种整合代谢和非代谢因素的新型模型.

研究的目的:

开发和验证一个强大的查模型来检测NAFLD.
为了更广泛的适用性,将代谢和非代谢指标纳入.
为了在接受常规体检的个体中促进NAFLD早期诊断.

主要方法:

6,461名参与者的回顾性队列被用于模型构建和内部验证.
多变量逻辑回归从20个候选变量中确定了显著预测因素 (p<0.05).
用曲线下的面积 (AUC),校准和决策曲线分析 (DCA) 来评估模型性能,并进行外部验证.

主要成果:

最终的模型包括12个预测因素:年龄,性别,BMI,UA,HDL-C,RBC,Cr,FT4,AST,ALT,AFP和TyG指数.
该模型实现了高分辨率,AUC为0.909 (培训) 和0.905 (内部验证).
通过内部,交叉验证和外部验证证实了优秀的校准和实质性的净收益.

结论:

开发了一种强大的NAFLD预测模型,包括各种风险因素.
该模型适用于接受例行体检的人群的查.
这个工具支持临床医生在早期NAFLD检测和干预.

关键词:

后勤回归的逻辑回归这是NAFLD.身体检查人口的身体检查预测模型的预测模型.培训套件培训套件验证集是一个验证集.

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