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[用于预测患有深静脉血栓症的败血症患者在医院内死亡率的诺姆图和机器学习模型]

Hongwei Duan¹, Huaizheng Liu², Chuanzheng Sun³

¹Department of Emergency, Third Xiangya Hospital, Central South University, Changsha 410013, China. 228311092@csu.edu.cn.

Zhong nan da xue xue bao. Yi xue ban = Journal of Central South University. Medical sciences

|September 27, 2025

概括

开发了一个名图和机器学习模型,以预测重症监护室 (ICU) 浸血症患者深静脉血栓症 (DVT) 的死亡率. 诺米图表显示出卓越的概括性和临床可用性,用于识别高风险患者.

科学领域:

关键护理医学关键护理医学
心血管研究研究心血管研究
医疗信息学医疗信息学

背景情况:

在重症监护室 (ICU) 的败血症患者有很大的风险 (20-30%) 患深静脉血栓症 (DVT) 由于凝血病.
这种并发症与高死亡率 (25-40%) 相关,突显了改善预后工具的需要.
目前对患有DVT的败血症患者的预后工具有显著的局限性.

研究的目的:

开发和验证一个名录模型,用于预测患有DVT的败血症患者的住院死亡率.
为同一个目的开发和验证机器学习模型 (XGBoost).
评估两个预测模型的临床适用性和通用性.

主要方法:

一项多中心的回顾性研究利用了来自MIMIC-IV,eICU-CRD和CSU-XYS-ICU数据库的数据.
用LASSO回归和BIC选择了名图的预测器;还构建了一个XGBoost模型.
模型性能使用一致性指数 (C指数),校准曲线,布里尔分数,决策曲线分析 (DCA) 和净重新分类改进 (NRI) 来评估.

主要成果:

一个名图识别了年龄,最小和最大激活的部分血栓形成蛋白 (APTT),最大的乳酸和最大的血清肌素作为关键预测因素.
在内部和外部验证 (C指数从0.779到0.845) 中,名图表表现出了强大的表现.
与名图相比,XGBoost模型在训练集中表现高,但在外部验证中概括性略低.

结论:

无论是nomogram还是XGBoost模型都有效地预测了患有DVT的败血症患者的住院死亡率.
诺莫格拉姆模型表现出卓越的概括性和临床可用性.
诺米图作为一种有价值的定量工具,用于识别高风险患者,指导个性化干预.

关键词:

深静脉血栓形成的原因是深静脉血栓形成.极端的梯度增强了极端的梯度.在医院的死亡率.机器学习模型机器学习模型命名图形模型的模型.有关风险因素的风险因素.败血症是什么败血症是什么

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