ACS NSQIP风险计算器在运营风险和风险相关特征的多个领域的性能
Mark E Cohen1, Yaoming Liu1, Bruce L Hall1,2
1Division of Research and Optimal Patient Care, American College of Surgeons, Chicago, IL.
Annals of surgery
|May 13, 2025
概括
美国外科医生学院国家外科质量改善计划 (ACS NSQIP) 风险计算器 (RC) 准确预测高风险患者的外科结果. 这种经过验证的工具在不同患者群体中提供了出色的校准和良好的歧视.
科学领域:
- 手术成果研究的研究结果.
- 医疗信息学 医疗信息学
- 机器学习在医学中的应用
背景情况:
- ACS NSQIP风险计算器 (RC) 对于手术决策至关重要,特别是在高风险场景中.
- 之前对RC的评估没有关注高风险子集,也没有考虑其更新的机器学习 (ML) 算法.
- 本研究通过评估RC在复杂患者群体中的表现来解决这些局限性.
研究的目的:
- 评估ACS NSQIP风险计算器 (RC) 在预测高风险手术患者子组的结果中的准确性.
- 将当前ML算法 (XGB) 的性能与潜在的未来算法 (CATB) 进行比较.
主要方法:
- 从ACS-NSQIP数据库 (2021-2024) 中分析了1,085,707名患者.
- 根据风险因素,年龄,ASA类,功能状态和手术紧急性/类型,患者被分为21个子组.
- 评估了对死亡率和发病率结果的校准 (绝对百分比误差;APE) 和歧视 (曲线下的面积;AUC).
主要成果:
- 观察到的死亡率从0.04%到60.01%不等,病率从3.33%到59.17%不等.
- 无论是XGB还是CATB的算法都表现出了很好的校准 (APE<10%).
- 歧视一直很好 (AUC>0.7),只有很小的例外.
结论:
- 该ACS NSQIP风险计算器 (RC) 为所有患者的风险水平提供可靠的预测.
- 该工具具有出色的校准和良好的区分能力,对于手术决策非常有价值.
- 这些发现支持RC在多种不同手术群体的持续使用和潜在增强.
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