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机器学习用于预测血栓栓塞事件随着脑内动脉瘤的流量转换器治疗:多中心回顾性研究

Yunpeng Lin¹, Xiaoning Liu¹, Bingcheng Ren¹

¹Department of Neurosurgery, Tianjin Medical University General Hospital, Tianjin, 300052, China.

Neurology and therapy

|August 27, 2025

概括

一个新的机器学习模型预测了脑内动脉瘤流转器治疗后的血栓栓塞事件. 这个工具通过评估个体患者的风险因素来帮助个性化治疗策略.

科学领域:

神经内血管外科
医疗人工智能
脑血管疾病管理

背景情况:

复杂的内动脉瘤最好使用流量转移器 (FD).
手术后的血栓栓塞事件 (TEE) 是常见的并发症.
目前缺乏对TEE的预测工具.

研究的目的:

开发和验证用于预测FD植入后的TEE的机器学习模型.
确定与TEE风险相关的主要临床和动脉瘤特征.

主要方法:

对377名用FD治疗的患者 (451例动脉瘤) 的追溯分析.
使用了机器学习算法,包括拉索回归和SVM.
使用AUC-ROC和AUC-PR评估模型性能;用于解释性的SHAP分析.

主要成果:

一个具有10个变量的机器学习模型以高准确度预测了TEE (AUC-ROC 0.96,AUC-PR 0.88).
主要预测因素包括动脉瘤宽度,LDL,高血压,动脉瘤位置,甘油三和糖尿病.
为临床应用开发了一个基于网络的工具.

结论:

一个经过验证的机器学习模型可以预测FD治疗的患者的TEE风险.
这种工具有助于神经干预专家个性化治疗策略.
该模型有助于风险分层和明智的临床决策.

关键词:

流量转移脑内动脉瘤机器学习预测模型血栓塞栓性事件

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