A Diagnostic Prediction Model of Acute Symptomatic Portal Vein Thrombosis
A Diagnostic Prediction Model of Acute Symptomatic Portal Vein Thrombosis
复制标题
DOI：
10.1016/j.avsg.2019.04.037
复制
发表时间：
2019-11-01
影响因子：
1.5
通讯作者：
Li, Xiaoqiang
中科院分区：
文献类型：
--
作者：
Liu, Kun;Chen, Jun;Li, Xiaoqiang
背景资料：本研究的目的是开发一种诊断预测模型，以提高对急性症状性门静脉血栓形成（PVT）的识别。方法：我们对苏州大学第二附属医院、南京市宿迁人民医院、鼓楼医院集团的47例PVT患者和94名无PVT的对照者进行了检查。我们构建了一个预测模型，使用支持向量机（SVM）分类器耦合最小绝对收缩和选择算子（LASSO）。我们采用了10倍交叉验证，以估计每个model.Results的错误率：本研究表明，急性症状性PVT与11个指标，包括肝硬化，D-二聚体，脾肿大，脾切除术，遗传性血栓形成倾向，腹水，腹部手术史，腹胀，C-反应蛋白（CRP），白蛋白，腹部压痛。结论：建立的LASSO-SVM模型诊断肺静脉血栓的敏感性为91.5%，特异性为100.0%。
Background: The aim of this study was to develop a diagnostic prediction model to improve identification of acute symptomatic portal vein thrombosis (PVT).Methods: We examined 47 patients with PVT and 94 controls without PVT in the Second Affiliated Hospital of Soochow University and Suqian People's Hospital of Nanjing, Gulou Hospital Group. We constructed a prediction model by using a support vector machine (SVM) classifier coupled with a least absolute shrinkage and selection operator (LASSO). We applied a 10-fold cross-validation to estimate the error rate for each model.Results: The present study indicated that acute symptomatic PVT was associated with 11 indicators, including liver cirrhosis, D-Dimer, splenomegaly, splenectomy, inherited thrombophilia, ascetic fluid, history of abdominal surgery, bloating, C-reactive protein (CRP), albumin, and abdominal tenderness. The LASSO-SVM model achieved a sensitivity of 91.5% and a specificity of 100.0%.Conclusions: We developed a LASSO-SVM model to diagnose PVT. We demonstrated that the model achieved a sensitivity of 91.5% and a specificity of 100.0%.