在医院行政数据中识别败血症的代码的裁决由专家共识
Allan Garland1, Na Li2, Wendy Sligl3
1Department of Medicine, University of Manitoba, Winnipeg, MB, Canada.
Critical care medicine
|December 5, 2024
概括
一个新的算法,AlgorithmL,通过包括更多的感染和器官功能障碍代码来完善住院患者的败血症病例识别. 这种方法可以更好地捕捉出毒症严重程度的全谱,导致较高的发病率和较低的死亡率,相比现有方法.
科学领域:
- 医疗信息学 医疗信息学
- 公共卫生监督 公共卫生监督
- 临床流行病学临床流行病学
背景情况:
- 准确的行政数据定义对于败血症监测至关重要.
- 现有的算法可能无法完全捕捉出败血症严重程度的范围,特别是不那么严重的病例.
研究的目的:
- 完善住院患者中败血症的行政数据定义.
- 开发和验证一种新的算法 (AlgorithmL) 用于使用行政代码识别败血症病例.
- 在行政数据定义中包括不太严重的败血症病例.
主要方法:
- 专家们判断了1928年感染的可能性和108个与败血症有关的器官功能障碍代码.
- 开发了算法L,要求至少有一个感染和一个被认为可能与败血症相关的器官功能障碍代码.
- 使用加拿大医院数据,AlgorithmL与四个现有算法以及CDC的成人败血症事件 (ASE) 定义进行了比较.
主要成果:
- 算法L包含720个感染和50个器官功能障碍代码,在代码包含方面超过了对比算法.
- 与比较者相比,AlgorithmL发现了较高的每年败血症发病率 (91/100,000在ICU,291/100,000在非ICU).
- 通过AlgorithmL识别的败血症死亡率较低 (24%ICU,17%非ICU) 比使用比较算法的死亡率低.
结论:
- AlgorithmL提供了一个更全面的方法来识别行政数据中的败血症病例.
- 算法的更广泛的代码包含可能导致更准确的败血症发病率和死亡率估计.
- 算法mL显示了更好地涵盖住院人群中毒症严重程度的全部范围的潜力.
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