基于炎症生物标志物的机器学习模型的开发和验证,用于基于炎症生物标志物的慢性阻塞性肺病急性恶化预测
Ye Zhu1, Meng Wang1, Xin-Nan Gu1
1Department of Respiratory and Critical Care Medicine, Yixing People's Hospital, Yixing, Jiangsu Province, China.
Frontiers in medicine
|August 18, 2025
概括
机器学习模型可以使用常规血液检查中的炎症生物标志物预测慢性阻塞性肺病 (AECOPD) 的急性恶化. 梯度增强机器模型显示出高精度,有助于COPD患者的早期干预.
科学领域:
- 肺部医学 肺部医学
- 生物标志物 生物标志物
- 机器学习 机器学习
背景情况:
- 慢性阻塞性肺病 (AECOPD) 的急性恶化是COPD患者住院和死亡的主要原因.
- 目前的预测依赖于主观的临床症状和医生的经验,缺乏客观的工具.
研究的目的:
- 通过整合多个炎症生物标志物来开发和比较用于预测AECOPD的机器学习模型.
- 为COPD管理提供客观和精确的早期干预工具.
主要方法:
- 追溯研究763名COPD患者,分为培训和验证队列.
- 收集的人口统计数据,并发症和炎症指数,如中性粒细胞与淋巴细胞的比率 (NLR) 和单细胞与淋巴细胞的比率 (MLR).
- 构建后勤回归和机器学习模型 (随机森林,GBM,SVM),使用ROC,校准和决策曲线分析进行评估.
主要成果:
- 梯度增强机 (GBM) 模型在验证队列中取得了卓越的性能 (AUC=0.900,精度=0.948).
- 沙普利添加式扩展 (SHAP) 确定了MLR,NLR和肺性心脏病作为关键预测因素.
- 较高的MLR和NLR与AECOPD风险增加相关,而乙素与淋巴细胞比率 (ELR) 显示出负相关性.
结论:
- GBM模型有效地使用易于获得的炎症生物标志物预测AECOPD.
- 这种模型适用于资源有限的环境,可用于早期识别和个性化治疗,以改善COPD患者的治疗结果.
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