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相关概念视频

Factors Affecting the Risk of Infection01:26

Factors Affecting the Risk of Infection

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The hosts' susceptibility to infection depends on several factors. The integrity of the skin and mucous membranes helps protect the body against microbial attacks. When the skin is altered, the chance of infection, limb loss, and even death increases.
The integrity and count of the white blood cells help the body resist pathogens and fight infection. When impaired, it reduces the body's resistance to pathogens. The acidic pH levels of the gastrointestinal, genitourinary tracts, and skin...
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相关实验视频

Updated: Jan 11, 2026

Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index
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基于可解释的机器学习,对血液感染的并发病特异性死亡风险评分.

Chen Cui1, Jinyi Zhao1, Fei Mu1

  • 1Department of Pharmacy, Xijing Hospital, Fourth Military Medical University, Xi'an 710032, China.

The Journal of infection
|November 17, 2025
PubMed
概括

一个新的血流感染异质性得分 (BHScore) 准确地预测了败血症患者的28天死亡率. 这一分数整合了早期临床数据和患者的并发症,以改善风险分层和及时干预.

关键词:
血液感染 血液感染伴随性疾病 伴随性疾病机器学习 机器学习风险因素 风险因素

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科学领域:

  • 临床医学 临床医学
  • 传染性疾病 传染性疾病
  • 医疗信息学 医疗信息学

背景情况:

  • 血流感染 (BSI) 是败血症的主要原因,需要早期风险评估才能有效治疗.
  • 现有的风险分层工具可能无法充分捕捉BSI患者的复杂性.

研究的目的:

  • 开发和验证BSI患者28天死亡率的预测模型.
  • 整合纵向临床数据,并在风险预测中考虑伴随性疾病驱动的异质性.
  • 改善早期风险分层,以便及时干预BSI相关的败血症.

主要方法:

  • 在2524名BSI患者的纵向临床数据 (前7天) 上使用机器学习开发了BSI异质性得分 (BHScore).
  • 采用可解释的方法来确定病,肝病和转移性恶性瘤的并发症分层值.
  • 利用西医院的数据进行模型开发和验证.

主要成果:

  • 与SOFA得分相比,BHScore表现出优异的歧视性表现 (AUC:0.81-0.91) 和时间稳定性,提高了预测能力的10-25%.
  • 确定了特定小组的预后指标:凝血生物标志物 (恶性病),炎症值 (肝病) 和尿素水平 (脏病).
  • 为支持BHScore的临床应用,开发了一个免费访问的网络工具.

结论:

  • 通过简单的临床指标,BHScore有效地识别了具有各种并发症的高风险BSI患者.
  • 促进早期和有针对性的干预,可能降低败血症患者的死亡率.
  • 强调在BSI风险预测中考虑并发症异质性的重要性.