通讯作者: 周甲丰, 179254021@qq.com

DOI：10.12201/bmr.202509.00035

Random Forest Model for Predicting Rupture of Middle Cerebral Aneurysms in Elderly Patients

• 摘要：目的：通过建立随机森林模型来预测老年患者大脑中动脉（MCA）瘤的破裂风险。方法：对温州医科大学附属第一医院2009年3月至2020年6月收治的MCA动脉瘤老年患者（年龄>60岁）资料进行回顾性分析，随机分为训练组和验证组（n=7:3），采用多因素Logistic回归分析老年患者MCA动脉瘤破裂的独立危险因素，据此建立随机森林模型来预测动脉瘤破裂风险，并通过其他四家医院的数据进行外部验证。采用受试者工作特征曲线（ROC）的曲线下面积（AUC）评估模型的预测效能。结果：共纳入226名患者242例MCA动脉瘤，训练组169例，内部验证组73例。外部验证组48例。多因素logistic回归分析显示，尺寸比、动脉瘤角度、高宽比及不规则形态是老年患者MCA动脉瘤破裂的独立危险因素。随机森林模型对训练组、内部验证组和外部验证组的预测效能分别为AUC=0.916（95%CI，0.878-0.946）、0.925（95%CI，0.874-0.968）和0.834（95%CI，0.725-0.932）。结论：预测老年患者大脑中动脉瘤破裂的随机森林模型具有较好的性能，可以用来辅助临床诊疗决策。

  关键词： 颅内动脉瘤；老年；大脑中动脉；破裂；随机森林; ; ; ; 

   

  Abstract: Objective: To predict therisk of middle cerebral artery (MCA) aneurysm rupture in elderly patients by establishing a random forest model. Methods: A retrospective analysis was conducted on data from elderly patients (age > 60) with MCA aneurysms treated at the First Affiliated Hospital of Wenzhou Medical University between March 2009 and June 2020. The data were randomly divided into a training group and a validation group (n=7:3). Independent risk factors for MCA aneurysms rupture in elderly patients were obtained by unifactorial and multifactorial logistic regression, based on these a random forest model was constructed, which was externally validated using data from four other hospitals. Its predictive performance was evaluated using the area under the curve (AUC) of receiver operating characteristic (ROC). Results: A total of 242 MCA aneurysms from 226 patients were included, with 169 cases in the training group, 73 cases in the internal validation group, and 48 cases in the external validation group. Multifactorial logistic regression analysis showed that the size ratio, aneurysm angle, height-width ratio, and irregular morphology were independent risk factors for MCA aneurysms rupture in elderly patients. The random forest model achieved AUC values of0.916 (95% CI, 0.878–0.946), 0.925 (95% CI, 0.874–0.968), and 0.834 (95% CI, 0.725–0.932) for the training, internal validation, and external validation groups, respectively. Conclusion: The random forest model demonstrated excellent performance in predicting the risk of MCA aneurysm rupture in elderly patients and can be used to assist clinical decision-making.

  Key words: intracranial aneurysm; elderly; middle cerebral artery; rupture; random forest

  提交时间：2025-09-15

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• 序号	提交日期	编号	操作
  1	2025-07-16	bmr.202509.00035V1	下载

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