使用6年UBT数据对Helicobacter pylori感染和风险分层进行回顾性队列研究
Yan Chen1, Miaojuan Wang1, Jianfeng Wang2,3
1Department of General Practice, The First Affiliated Hospital of Zhejiang Chinese Medical University, Zhejiang Provincial Hospital of Traditional Chinese Medicine, Hangzhou, China.
Frontiers in public health
|June 10, 2025
概括
机器学习使用代谢数据准确预测Helicobacter pylori感染风险. 这种方法可以识别具有更高风险的个体,使H. pylori相关健康问题的早期干预成为可能.
科学领域:
- 代谢健康 代谢健康
- 传染性疾病 传染性疾病
- 机器学习在医学中的应用
背景情况:
- 杆菌感染是一个全球性健康问题,与胃癌和代谢障碍有关.
- 对H. pylori感染的准确风险分层仍然是一个重大挑战.
- 了解与慢性H. pylori感染相关的代谢变化至关重要.
研究的目的:
- 开发一种基于机器学习 (ML) 的H.pylori感染风险预测模型.
- 使用纵向尿素呼吸试验 (UBT) 数据识别与慢性H. pylori感染相关的代谢变化.
- 评估不同ML模型的预测性能.
主要方法:
- 对3,409名个人 (2016-2021) 的回顾性队列研究.
- 根据纵向UBT结果,分为H. pylori阳性和阴性组的分层.
- 对代谢标记物 (HbA1c,LDL-C,BMI,WBC) 的分析和逻辑回归,随机森林和XGBoost模型的比较.
主要成果:
- 队列中20.5%的人患有慢性H. pylori感染.
- 受感染的个体显示HbA1c,LDL-C和WBC水平较高,白蛋白水平较低.
- XGBoost模型实现了最高的预测性能 (AUC=0.6809,准确度=81.13%),确定了一个高风险子组.
结论:
- 慢性H. pylori感染与代谢功能障碍有关.
- XGBoost模型可靠地使用可访问的临床标志物对H. pylori感染风险进行分层.
- 将其整合到查协议中可以改善早期检测和个性化干预.
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