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机器学习预测模型用于在接受内血管腹腔大动脉动脉瘤修复的患者的预后

Ning Zhao¹, Yaming Zhou², Shaobo Cao³

¹Department of Vascular Surgery, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, China; Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.

Annals of vascular surgery

|January 16, 2026

概括

机器学习模型可以预测腹腔大动脉动脉瘤 (AAA) 的内血管动脉瘤修复 (EVAR) 后的生存率. 该K-最近邻居 (KNN) 算法在预测患者结果方面表现出卓越的准确性,确定中风,动脉瘤直径与BMI比率,以及COPD作为关键预测因素.

科学领域:

血管外科血管外科
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:

内血管动脉瘤修复 (EVAR) 在腹腔大动脉瘤 (AAA) 中越来越常见.
预测术后生存对于管理接受EVAR的AAA患者至关重要.
机器学习为开发准确的预后模型提供了一个有希望的方法.

研究的目的:

建立和评估基于机器学习的预测模型,用于在EVAR后的AAA患者的预后.
为了确定影响EVAR后生存率的关键因素.

主要方法:

分析了接受EVAR的163名AAA患者的队列.
数据分为培训 (70%) 和测试 (30%) 组.
使用各种机器学习算法,包括LASSO回归和K-Nearest Neighbors (KNN),并使用ROC曲线进行评估.

主要成果:

该K-最近邻居 (KNN) 算法显示了最高的预测性能,AUC为0.85 (训练) 和0.81 (测试).
通过LASSO回归确定的关键预测因素包括慢性阻塞性肺病 (COPD),动脉瘤直径与体重指数比率 (DBR) 和中风史.
研究队列的平均年龄为72岁,随访期间死亡率为20.2%.

结论:

在AAA患者中,中风,DBR和COPD是EVAR后预后的显著预测因素.
该KNN算法是一种卓越的机器学习方法,用于预测这个患者群体的生存结果.

关键词:

腹腔大动脉动脉瘤腹腔大动脉动脉瘤血管内动脉瘤修复血管内动脉瘤修复机器学习是机器学习.预测模型预测模型

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