开发和验证一个营养综合的诺米克图,用于预测败血症患者的28天死亡率
Yong-Ling Yang1, Yi-Sheng Huang1, Yu-Long Bai2
1Department of Critical Care Medicine, Tenth Affiliated Hospital of Guangxi Medical University, Qinzhou, China.
Frontiers in nutrition
|February 5, 2026
概括
这项研究开发了一种使用机器学习的预测性名录,以识别高风险28天死亡的败血症患者. 该模型结合了像PNI这样的营养生物标志物,显示了早期败血症风险分层的强大预测性能.
科学领域:
- 生物医学科学 生物医学科学
- 临床医学 临床医学
- 数据科学数据科学数据科学
背景情况:
- 败血症是一种严重的疾病,死亡率很高.
- 早期风险分层对于有效的败血症管理至关重要.
- 机器学习为识别败血症死亡率预测因素提供了潜力.
研究的目的:
- 开发和验证预测性血病患者28天死亡率的诺姆图.
- 使用机器学习识别用于预测败血症死亡率的关键生物标志物.
- 为了加强风险评估,将选定的生物标志物与临床评分相结合.
主要方法:
- 追溯注册1350名败血症患者进入培训和测试组.
- 应用LASSO和随机森林算法用于生物标志物选择.
- 构建一个物流回归模型和名图,通过外部验证.
主要成果:
- 已经确定了六个关键生物标志物:PCT,PNI,RBC,PLT,ALT和IBIL.
- 诺米图表显示出强大的预测歧视 (AUC在培训中为0.841,在测试中为0.808,在外部验证中为0.921).
- 该模型显示了良好的校准和临床实用性,跨越各种值.
结论:
- 为预测28天败血症死亡率而开发并验证了一种临床上有用的诺米克图.
- 与营养相关的生物标志物,特别是PNI,在败血症结果中发挥着重要作用.
- 诺米图为败血症患者早期风险分层提供了一个强大的工具.
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