Kun Liu1, Jun Chen2, Kaixin Zhang2, Shuo Wang2, Xiaoqiang Li3. 1. Department of Vascular Surgery, The Affiliated Suqian Hospital of Xuzhou Medical University, Suqian People's Hospital affiliated to Nanjing Drama Tower Hospital Group, Suqian, Jiangsu, China; Department of Vascular Surgery, The Second Affiliated Hospital of Soochow University, Suzhou, Jiangsu, China. 2. Department of Vascular Surgery, The Affiliated Suqian Hospital of Xuzhou Medical University, Suqian People's Hospital affiliated to Nanjing Drama Tower Hospital Group, Suqian, Jiangsu, China. 3. Department of Vascular Surgery, Affiliated Nanjing Drum Tower Hospital of Nanjing University Medical School, Nanjing, China. Electronic address: SuzhouXQL@163.com.
Abstract
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%.
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%.