内未破裂动脉瘤微手术中不良事件的风险模型
Victor E Staartjes1,2, Alexios A Adamides3, Pasquale Anania4
1Machine Intelligence in Clinical Neuroscience & Microsurgical Neuroanatomy (MICN) Laboratory, Department of Neurosurgery, Clinical Neuroscience Center, University Hospital Zurich, University of Zurich, Zürich, Switzerland.
Neurosurgery
|December 8, 2025
概括
新的机器学习模型预测了微手术治疗未破裂的内动脉瘤 (UIA) 的风险. 这些工具为不良的神经结果和新的缺陷提供了个性化的风险估计,有助于临床决策.
科学领域:
- 神经外科 神经外科
- 医学机器学习 医学机器学习
- 临床预测模型临床预测模型
背景情况:
- 预防性治疗未破裂的内动脉瘤 (UIA) 需要平衡治疗风险与预期的益处.
- 目前对UIA破裂/生长风险的评分缺乏对微手术治疗风险的外部验证.
- 机器学习模型可以根据患者和动脉瘤特征提供针对UIA治疗的个性化风险评估.
研究的目的:
- 开发和外部验证UIAs微手术治疗后不良结果的临床预测模型.
- 评估模型预测神经系统不良结果,新的感觉运动缺陷和医院出院时所有原因的不良事件的能力.
主要方法:
- 利用来自20个国际中心的数据 (3705名患者) 进行"对内未破裂动脉瘤微手术后不良事件的预测"研究.
- 开发并验证了使用机器学习对患者和动脉瘤特征的模型.
- 基于来自7个中心 (824名患者) 的数据进行外部验证的模型.
主要成果:
- 模型表现出良好的校准和歧视,可以预测神经系统的不良结果 (AUC=0.70) 和新的感觉运动缺陷 (AUC=0.69).
- 所有原因不良事件的预测显示了公平的校准,AUC为0.59.
- 整合预测模型的Web应用程序可以在https://neurosurgery.shinyapps.io/PRAEMIUM/.
结论:
- 开发的模型用于预测不良的神经结果和新的感觉运动缺陷,在多国外部验证时显示出良好的校准和公平的歧视.
- 这些模型可能有助于客观地估计UIAs的微手术治疗风险.
- 预测的概率与现实世界的风险保持一致,这表明临床效用.
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