除了SOFA和APACHE II之外,在重症监护中使用易于获得的生物标志物进行新型风险分层模型
Jihyuk Chung1, Joonghyun Ahn2, Jeong-Am Ryu3,4
1Department of Thoracic and Cardiovascular Surgery, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul 06351, Republic of Korea.
Diagnostics (Basel, Switzerland)
|May 14, 2025
概括
使用乳酸与白蛋白比率 (LAR) 和中性粒细胞百分比与白蛋白比率 (NPAR) 的简化ICU死亡预测模型显示高准确度. 这些新型模型与传统的评分系统竞争,提供更快的临床决策和潜在的患者改善结果.
科学领域:
- 关键护理医学 关键护理医学
- 生物标志物发现发现
- 医疗信息学 医疗信息学
背景情况:
- 目前的重症监护室 (ICU) 严重性评分系统复杂且耗时.
- 这种复杂性限制了它们在重症监护机构快速临床决策中的实用性.
- 需要简化,准确的工具来评估住院死亡风险.
研究的目的:
- 开发和验证ICU患者医院死亡风险的简化预测模型.
- 利用易于获得的生物标志物来提高预测准确性和临床实用性.
- 将新型模型的性能与已建立的评分系统 (如SOFA和APACHE II) 进行比较.
主要方法:
- 对19720名成年ICU患者的回顾性分析.
- 三种预测模型的开发:基本模型 (LAR,NPAR) 和两个增强模型 (包括通风和置换治疗数据).
- 使用机器学习的性能评估,与顺序器官衰竭评估 (SOFA) 和急性生理和慢性健康评估 (APACHE) II分数进行比较.
主要成果:
- 改进后的Model 3的AUC为0.929,相当于SOFA (AUC为0.931) 的AUC,并且优于APACHE II (AUC为0.900).
- 模型2 (AUC 0.913) 的表现与APACHE II相当,而模型1 (AUC 0.898) 仅使用LAR和NPAR表现出强的表现.
- 在子组分析中,所有模型在各种ICU类型中都显示出一致的性能.
结论:
- 新型,简化预测模型的准确性与传统的ICU评分系统相美.
- 这些模型提供了显著更快的实施,促进有效的风险评估.
- 这些发现支持了早期临床干预的潜力,并改善了重症监护中的患者结果.
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