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开发一种可解释的预测模型,用于儿童中度至严重阻塞性睡眠呼吸暂停的风险

Fuzhi Lin¹, Yufei Peng², Xiaowei Chen¹

¹Department of Otorhinolaryngology-Head and Neck Surgery, Children's Hospital of Zhejiang University School of Medicine, No. 3333 Binsheng Road, Binjiang District, Hangzhou, 310051, Zhejiang Province, China.

European journal of pediatrics

|January 13, 2026

概括

一个新的名图有助于通过使用临床和炎症标记来识别中度至严重的阻塞性睡眠呼吸暂停 (OSA) 的儿童. 这种工具有助于早期诊断和干预,当多睡眠学 (PSG) 无法使用时.

科学领域:

儿科睡眠医学儿科睡眠医学
生物标志物发现发现
临床预测建模临床预测建模

背景情况:

多睡眠学 (PSG) 是诊断儿科阻塞性睡眠呼吸暂停 (OSA) 的黄金标准,但其有限的可访问性阻碍了早期识别严重病例.
准确和及时的诊断对于中度至重度OSA儿童的有效干预至关重要.

研究的目的:

开发一种可解释的临床预测模型,用于早期识别中度至重度儿科OSA.
使用可访问的临床和炎症生物标志物,区分中度至严重的儿科OSA与轻度OSA.

主要方法:

对164名通过PSG诊断患有OSA的儿童进行了回顾性研究.
最小绝对收缩和选择运算符 (LASSO) 回归来选择预测特征.
多变量逻辑回归模型呈现为可解释的名图,通过引导分析验证.

主要成果:

确定了八个核心预测因素:女性性别,桃体大小 (3-4年级),腺和鼻比 (A/N比),IgE,IL-4,IL-6和IL-10.
最终的模型实现了0.763的AUC (95% CI 0.690-0.836) 的启动纠正.
决策曲线分析证实了该模型对风险评估的临床实用性.

结论:

开发了一个可解释的名图,将上呼吸道解剖学,过敏状态,性别和炎症性细胞因子 (IL-4,IL-6,IL-10) 整合起来.
该工具提供了一种实用的,非侵入性的方法,用于对儿童中度至重度OSA的个性化风险评估.
促进儿童中度至重度OSA的优先诊断和干预.

关键词:

炎症生物标志物炎症生物标志物机器学习机器学习这个名字是名ogramogram.儿童阻塞性睡眠呼吸暂停症预测模型的预测模型.

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