动态敏度评分的前性评估,用于定期评估重症患者的死亡风险
Andrew A Kramer1, James F Krinsley2, Matthew Lissauer3
1Prescient Healthcare Consulting, LLC, Charlottesville, VA.
Critical care medicine
|May 29, 2023
概括
维西格评分有效地使用实时数据预测重症监护室 (ICU) 死亡率,将患者分为低风险,中等风险和高风险组. 这种前性验证证实了其对临床应用的稳定性.
科学领域:
- 临界护理医学 临界护理医学
- 医疗信息学 医疗信息学
- 预测分析是一种预测分析.
背景情况:
- 重症监护室 (ICU) 的预测模型通常依赖于回顾性数据,限制了它们在现实世界中的适用性.
- 在利用实时临床数据来准确预测死亡率方面存在挑战.
- 之前已经开发了ViSIG评分,但需要使用前性数据进行验证.
研究的目的:
- 使用前性收集的近实时临床数据来确定ViSIG评分的稳定性.
- 为了验证先前开发的ICU死亡率滚动预测器.
主要方法:
- 从六个成年ICU收集的前性数据被汇总起来.
- 计算了ViSIG评分,包括生命体征和Visensia指数.
- 分析了患者数据,以比较ViSIG分数分布与ICU死亡率.
- 该模型的准确性通过使用排放处置数据进行评估,并在新入场时得到验证.
主要成果:
- 通过ViSIG评分,成功地将患者分为低 (1.7%的死亡率),中等 (12.0%的死亡率) 和高 (39.8%的死亡率) 风险组 (p < 0.001).
- 该模型在预测高风险组的死亡率方面显示了51%的灵敏度和91%的特异性.
- 在验证数据集上,表现仍然很高,在各个风险组的逗留时间,成本和再接收方面都有类似的趋势.
结论:
- 通过前性数据验证的ViSIG评分有效地根据死亡风险对ICU患者进行分层,具有良好的灵敏度和出色的特异性.
- 进一步的研究将探索临床医生对ViSIG评分的可见性对患者结果的影响.
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