在喘儿童中开发持续空气流限制的预测模型.
Shiqiu Xiong1,2, Xinyu Jia1, Wei Chen1
1Department of Allergy, Center for Asthma Prevention and Lung Function Laboratory, Capital Center for Children's Health, Capital Medical University, Beijing, China.
Journal of thoracic disease
|November 13, 2025
概括
通过使用临床数据,可以预测喘儿童的持续气流限制 (PAL). 机器学习模型,如随机森林和后勤回归,可以准确地识别患PAL风险的儿童.
科学领域:
- 儿科肺病学 儿科肺病学
- 临床信息学是一种临床信息学.
- 生物统计学 生物统计学
背景情况:
- 一部分患有喘的儿童会出现持续的空气流限制 (PAL).
- 识别患有PAL风险的儿童对于及时干预至关重要.
- 预测模型可以帮助早期风险分层.
研究的目的:
- 开发和验证预测模型,以识别患有PAL风险的喘儿童.
- 为了比较经典和机器学习方法在预测PAL方面的性能.
主要方法:
- 利用1671名喘儿童的人口和临床数据进行培训和内部验证.
- 包括401名喘儿童进行时间验证.
- 开发了使用后勤回归 (LR),随机森林 (RF) 和极端梯度提升 (XGBoost) 的预测模型.
- 使用曲线下的面积 (AUC),精度,灵敏度,特异性,校准曲线,布里尔得分和决策曲线分析评估模型性能.
主要成果:
- 在内部验证中,RF (AUC=0.857),LR (AUC=0.849) 和XGBoost (AUC=0.835) 显示出强大的预测能力.
- 时间验证证实了不同模型的可比性能:RF (AUC=0.853),LR (AUC=0.836) 和XGBoost (AUC=0.848).
- 所有模型都表现出良好的健康状况和临床实用性.
结论:
- 在儿童中,与喘相关的持续空气流量限制可以以显著的准确性预测.
- 常规收集的临床数据足以开发有效的预测模型.
- 雷射,射频和XGBoost模型在识别高风险儿科喘患者方面提供了可比的性能.
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