使用机器学习对静脉缩手术90天后临床改善的分析
Amel Bakhouche1, Olivier Creton2, Fabien Lareyre3,4
1Epione Team, INRIA Center, University Côte d'Azur, Sophia Antipolis, France.
Angiology
|January 31, 2026
概括
机器学习模型可以预测慢性静脉缺陷 (CVI) 静脉瘤手术后的临床改善. 基线分数和BMI等关键因素有助于识别患有不良结果风险的患者.
科学领域:
- 血管外科 血管外科
- 医疗信息学 医疗信息学
- 生物统计学 生物统计学
背景情况:
- 预测慢性静脉缺陷 (CVI) 干预后的结果仍然具有挑战性.
- 静脉静脉外科手术是CVI的常见治疗方法,但患者的反应各不相同.
- 确定影响手术结果的因素对于患者管理至关重要.
研究的目的:
- 开发和评估机器学习 (ML) 模型,以预测经过静脉缩手术后90天的临床改善.
- 确定与CVI患者治疗结果相关的关键预测因素.
- 为了提高治疗成功的预测,并识别患有低于最佳结果风险较高的个体.
主要方法:
- 对4015名首次进行静脉缩手术的患者进行了回顾性多中心研究 (2014-2024年).
- 使用临床-病因-解剖-病理生理学 (CEAP) 和静脉临床严重性评分 (VCSS) 的分类.
- 训练后勤回归,随机森林和XGBoost模型,并进行交叉验证和低采样.
主要成果:
- 87.6%的患者在90天内实现了临床改善 (降低CEAP阶段).
- 随机森林模型在预测缺乏改进方面表现最好 (准确率为80%,回忆率为75%).
- 预测不良结果的预测因素包括年龄较大,BMI较高,以及基线CEAP和VCSS得分较高.
结论:
- 机器学习模型提供了适度但有价值的预测性能的静脉瘤手术结果.
- 模型有效地突出了患有术后不改善风险的患者.
- 患者的基线特征和疾病严重程度得分是治疗反应的关键预测指标.
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