开发和评估一个预测模型,用于成人ICU出血,仅使用连续的心肺呼吸数据
Andrew Barros1, Brynne Sullivan2, Matthew T Clark3
1Department of Medicine, University of Virginia, 1215 Lee Street, Charlottesville, 22903-1738, United States.
Physiological measurement
|February 12, 2026
概括
一个新的模型使用心肺呼吸数据准确地预测了重症监护室 (ICU) 患者的出血. 这种工具对更早的出血检测和改善患者的结果有希望,尽管性能在人口统计学上有所不同.
科学领域:
- 关键护理医学 关键护理医学
- 生物医学工程 生物医学工程
- 医疗信息学 医疗信息学
背景情况:
- 在重症监护室 (ICU) 住院的患者,由于严重疾病而面临出血的高风险.
- 早期识别出血对于改善重症监护机构患者的治疗结果至关重要.
- 现有的出血检测方法可能缺乏及时性或通用性.
研究的目的:
- 利用心肺呼吸系统数据,开发和评估ICU患者出血的预测模型.
- 评估不同ICU队列中出血检测模型的通用性.
- 为了比较开发的出血模型与冲击指数的性能.
主要方法:
- 收集了来自四个ICU队列 (一个发展,三个评估) 的心肺呼吸系统监测和包装红血细胞管理数据.
- 定义出血为在24小时内输血三次或更多次.
- 训练了一种处罚后勤回归模型,在8小时内预测出血,并对其性能进行外部验证.
主要成果:
- 该模型在开发队列中实现了0.706的交叉验证AUC,在评估队列中达到0.712.
- 该模型表现出良好的校准 (斜率1.041) 并在临床识别前几个小时预测出血风险增加.
- 老年患者 (>75岁) 的表现较低,在特定机构 (Pitt) 和黑人患者和女性中,尽管出血得分优于冲击指数.
结论:
- 使用连续心肺呼吸数据的风险模型可以预测ICU患者的出血,并具有临床相关的准确性.
- 该模型证明了在不同的ICU设置,监测设备和电子健康记录系统中具有普遍性.
- 虽然该模型通常有效,但患者子组之间的性能差异需要进一步调查和潜在的改进.
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