A Diagnostic Prediction Model of Acute Symptomatic Portal Vein Thrombosis

The aim of this study was to develop a diagnostic prediction model to improve identification of acute symptomatic portal vein thrombosis (PVT).

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.

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%.

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%.