在35,206名成年人中,使用基于组的轨迹建模和机器学习进行9年风险分层和Helicobacter pylori感染的预测
Heping Zhao1, Sainan Liu2, Manzhen Wei2
1Department of Gastroenterology, The First Hospital of Shanxi Medical University, Taiyuan, Shanxi, China.
Frontiers in public health
|December 1, 2025
概括
一个新的风险预测工具可以准确地识别出患有Helicobacter pylori感染的高风险个体. 这种工具有助于精确查和早期干预H. pylori,这是胃肠道问题的普遍原因.
科学领域:
- 胃肠病学和肝病学
- 流行病学 流行病学
- 生物统计学和生物信息学
背景情况:
- Helicobacter pylori (H. pylori) 感染是一个广泛的问题,特别是在像中国山西这样的地区,导致显著的胃肠道疾病.
- 有效的查和早期干预策略对于管理H. pylori的流行及其相关的健康风险至关重要.
研究的目的:
- 开发和验证一种可解释的H. pylori感染风险预测工具.
- 为了确定与高风险H. pylori感染轨迹相关的关键预测因素和风险因素.
主要方法:
- 一个追溯的纵向队列研究,涉及35206名成年人进行重复的H. pylori测试.
- 基于组的轨迹建模 (GBTM) 来识别风险子组和用于预测分析的多变量逻辑回归.
- 机器学习模型 (LightGBM,XGBoost,物流回归) 经过训练和验证,使用SHAP进行解释性和R Shiny应用程序进行部署.
主要成果:
- GBTM确定了不同的高风险 (14.63%) 和低风险 (85.37%) 群体.
- 关键的危险因素包括肥胖,蓝领工作和酒精消费,而女性性别和未婚状态是保护性的.
- 酒精消费显著改变了与其他因素相关的风险,特别是婚姻状况. 轻GBM实现了高预测准确度 (AUC高达0.863).
结论:
- 已经开发了一个准确的,可解释的在线风险预测工具,用于H. pylori感染.
- 该工具支持精确查,使H. pylori感染风险较高的个体能够进行有针对性的干预.
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