原发性三叉神经痛手术治疗决策辅助预测模型的构建及验证  

Construction and validation of a predictive model for surgical procedures in primary trigeminal neuralgia

作　　者：郭洪彬 马富凯[1] 吴祎炜 张文川 Guo Hongbin;Ma Fukai;Wu Yiwei;Zhang Wenchuan(Department of Neurosurgery,the Ninth People′s Hospital Affiliated to Shanghai Jiao Tong University School of Medicine,Shanghai 200011,China;Department of Neurosurgery,Qian Tang Campus of Sir Run Run Shaw Hospital,School of Medicine,Zhejiang University,Hangzhou 310016,China)

机构地区：[1]上海交通大学医学院附属第九人民医院神经外科,上海200011 [2]浙江大学医学院附属邵逸夫医院钱塘院区神经外科,杭州310016

出　　处：《中华神经外科杂志》2024年第10期1043-1049,共7页Chinese Journal of Neurosurgery

基　　金：国家自然科学基金青年基金(82101432)。

摘　　要：目的基于机器学习的支持向量机算法(SVM)构建原发性三叉神经痛(PTN)手术治疗决策的辅助预测模型并验证其效能。方法共纳入上海交通大学医学院附属第九人民医院神经外科连续性收治的2个数据集的PTN患者,数据集1为回顾性收集2022年6月至2023年2月的167例患者资料,数据集2为前瞻性纳入2023年3—6月的41例患者。将数据集1按照3∶1比例随机分为训练集(125例)和测试集(42例),将手术方式[包括显微血管减压术(MVD)、射频热凝(RFTC)、经皮球囊压迫(PBC)]作为因变量,采用Lasso回归分析,筛选训练集的特征变量后纳入到机器学习,构建手术治疗决策的辅助预测模型,使用测试集对模型进行验证和评估,并使用数据集2(即验证集)进行外部验证,绘制模型的受试者工作特征(ROC)曲线并计算曲线下面积(AUC)评估模型在不同数据集上的预测效能。术前、术后1个月采用视觉模拟评分(VAS)评估PTN患者的疼痛程度,比较数据集1和数据集2患者VAS的差异。同时,观察两个数据集患者手术相关不良反应或并发症情况。结果Lasso回归分析结果显示,筛选出回归系数不为0的特征变量依次为PBC手术史、年龄、MR体层血管成像结果、MVD手术史、其他限制性疾病、疼痛分布、病程、RFTC手术史。预测模型在训练集、测试集和验证集的总体准确率分别为73.6%、69.0%、73.2%,AUC(宏观/微观)分别为0.89/0.89、0.80/0.81、0.85/0.85。数据集1和数据集2患者的年龄、病程等特征变量资料及手术方式的差异均无统计学意义(均P>0.05)。数据集1与数据集2比较,术前、术后1个月VAS的差异均无统计学意义(均P>0.05);与术前比较,数据集1与数据集2术后1个月的VAS均降低,差异均有统计学意义(均P<0.001)。数据集1中2例患者行MVD术后发生皮下积液,数据集2中1例患者MVD术后切口愈合不良,两数据集间并发症发生率的差异无统计学意义[1.2%(2/167)ObjectiveTo develop a predictive model for surgical treatment of primary trigeminal neuralgia(PTN)based on machine learning support vector machine algorithm(SVM)and to verify its effectiveness.MethodsTwo datasets of PTN patients admitted consecutively to the Neurosurgery Department of the Ninth People′s Hospital Affiliated to Shanghai Jiao Tong University School of Medicine were included.Dataset 1 retrospectively collected 167 patients from June 2022 to February 2023,and Dataset 2 prospectively collected 41 patients from March to June 2023.We randomly divided dataset 1 into a training set(125 cases)and a testing set(42 cases)in a 3∶1 ratio.Surgical methods,including microvascular decompression(MVD),radiofrequency thermocoagulation(RFTC)and percutaneous ballon compression(PBC),were used as the dependent variables,Lasso regression analysis was performed to screen feature variables in training set,which were incorporated into machine learning to construct a predictive model for surgical treatment methods.We used the testing set to validate and evaluate the model,and used dataset 2(validation set)for external validation.The receiver operating characteristic(ROC)curve of the model was drawn and the area under the curve(AUC)was calculated to evaluate its predictive performance on different datasets.The visual analog scale(VAS)was used to evaluate the pain level of PTN patients before and one month after surgery,and the difference in VAS between dataset 1 and dataset 2 patients was compared.Meanwhile,we documented the incidence of surgical related adverse reactions or complications in patients from two dataset.ResultsThe Lasso regression analysis showed that the feature variables with non-zero regression coefficients were PBC surgery history,age,magnetic resonance tomography angiography(MRTA)results,MVD surgery history,other restrictive diseases,pain distribution,disease duration,and RFTC surgery history.The overall accuracy of the prediction model in the training set,testing set,and validation set was 73.6%,69.0%,a

关 键 词：机器学习 显微血管减压术 原发性三叉神经痛 球囊压迫 预测模型 

分 类 号：R651.3[医药卫生—外科学]