Lei Chunyu, Fu Anhui, Li Bin, Zhou Shengfu, Liu Jun, Cao Yu, Zhou Bo
Department of Neurosurgery, FuShun County Zigong City People's Hospital, Fushun, China.
Department of Neurosurgery, Nanchong Central Hospital, Nanchong, China.
Front Neurol. 2025 Jan 8;15:1482119. doi: 10.3389/fneur.2024.1482119. eCollection 2024.
To evaluate the clinical utility of improved machine learning models in predicting poor prognosis following endovascular intervention for intracranial aneurysms and to develop a corresponding visualization system.
A total of 303 patients with intracranial aneurysms treated with endovascular intervention at four hospitals (FuShun County Zigong City People's Hospital, Nanchong Central Hospital, The Third People's Hospital of Yibin, The Sixth People's Hospital of Yibin) from January 2022 to September 2023 were selected. These patients were divided into a good prognosis group ( = 207) and a poor prognosis group ( = 96). An improved machine learning model was employed to analyze patient clinical data, aiding in the construction of a prediction model for poor prognosis in intracranial aneurysm endovascular intervention. This model simultaneously performed feature selection and weight determination. Logistic multivariate analysis was used to validate the selected features. Additionally, a visualization system was developed to automatically calculate the risk level of poor prognosis.
In the training set, the improved machine learning model achieved a maximum F1 score of 0.8633 and an area under the curve (AUC) of 0.9118. In the test set, the maximum F1 score was 0.7500, and the AUC was 0.8684. The model identified 10 key variables: age, hypertension, preoperative aneurysm rupture, Hunt-Hess grading, Fisher score, ASA grading, number of aneurysms, intraoperative use of etomidate, intubation upon leaving the operating room, and surgical time. These variables were consistent with the results of logistic multivariate analysis.
The application of improved machine learning models for the analysis of patient clinical data can effectively predict the risk of poor prognosis following endovascular intervention for intracranial aneurysms at an early stage. This approach can assist in formulating intervention plans and ultimately improve patient outcomes.
评估改进的机器学习模型在预测颅内动脉瘤血管内介入治疗后预后不良方面的临床效用，并开发相应的可视化系统。
选取2022年1月至2023年9月在四家医院（自贡市富顺县人民医院、南充市中心医院、宜宾市第三人民医院、宜宾市第六人民医院）接受血管内介入治疗的303例颅内动脉瘤患者。这些患者被分为预后良好组（=207）和预后不良组（=96）。采用改进的机器学习模型分析患者临床数据，辅助构建颅内动脉瘤血管内介入治疗预后不良的预测模型。该模型同时进行特征选择和权重确定。采用逻辑多因素分析验证所选特征。此外，开发了一个可视化系统，以自动计算预后不良的风险水平。
在训练集中，改进的机器学习模型的最大F1分数为0.8633，曲线下面积（AUC）为0.9118。在测试集中，最大F1分数为0.7500，AUC为0.8684。该模型确定了10个关键变量：年龄、高血压、术前动脉瘤破裂、Hunt-Hess分级、Fisher评分、ASA分级、动脉瘤数量、术中使用依托咪酯、离开手术室时插管以及手术时间。这些变量与逻辑多因素分析结果一致。
应用改进的机器学习模型分析患者临床数据可有效早期预测颅内动脉瘤血管内介入治疗后预后不良的风险。这种方法有助于制定干预计划并最终改善患者预后。
