基于血小板轨迹潜伏类的传染性内心炎患者动态预测模型的开发和验证:从多中心数据库进行纵向回顾性研究
Kexiao Zheng1, Yanglin Hao, Chao Guo
1Department of Cardiovascular Surgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
International journal of surgery (London, England)
|November 10, 2025
概括
在感染性内心炎 (IE) 患者中分析血小板 (PLT) 轨迹揭示了不同的临床特征,并使动态风险预测成为可能. 这种方法有助于更好地分层患者,并预测IE的不良结果.
科学领域:
- 心脏病学 心脏病学
- 传染性疾病 传染性疾病
- 数据科学数据科学数据科学
背景情况:
- 血小板 (PLT) 在传染性内心炎 (IE) 中至关重要,IE是一种复杂的心脏感染.
- IE在病原和结果中表现出异质性,受感染,免疫力和脆弱性的影响.
- 了解IE治疗期间的PLT行为对于患者管理至关重要.
研究的目的:
- 在感染性内心炎 (IE) 治疗期间探索血小板 (PLT) 轨迹.
- 识别与不同PLT轨迹相关的不同的临床特征.
- 开发一个动态预测模型,用于IE患者的不良结果.
主要方法:
- 来自eICU和MIMIC数据库的纵向数据的回顾性分析.
- 隐性类增长混合建模 (LCGMM) 来识别PLT轨迹和集群.
- 用于动态预测的贝叶斯联合模型,由AUROC验证.
主要成果:
- 在IE患者中确定了四种不同的PLT轨迹的潜在类别.
- 一个集成感染状态和PLT的动态预测模型显示出强大的性能 (AUC:0.71).
- 该模型实现了高预测准确度 (尤登:0.96,F1得分:0.95).
结论:
- 纵向PLT轨迹与基线数据相结合,改善了IE中的风险分层.
- 动态预测模型增强了IE患者不良结果的预测.
- 这种方法为个性化的IE管理提供了宝贵的见解.
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