Risk Prediction for Portal Vein Thrombosis in Acute Pancreatitis Using Radial Basis Function.

BACKGROUND: Acute pancreatitis (AP) can induce portosplenomesenteric vein thrombosis (PVT), which may generate higher morbidity and mortality. However current diagnostic modalities for PVT are still controversial. In recent decades, artificial neural networks have been increasingly applied in medical research. The aim of this study is to predict the risk of AP-induced PVT by radial basis function (RBF) artificial neural networks (ANNs) model.

METHODS: A retrospective or consecutive study of 426 individuals with AP at our unit between January 1, 2011 and July 31, 2016 was conducted. All individuals were subjected to RBF ANNs. Variables included age, gender, red blood cell specific volume (Hct), prothrombin time (PT), fasting blood glucose, D-Dimer, concentration of serum calcium ([Ca2+ ]), triglyceride, serum amylase (AMY), acute physiology and chronic health evaluation II score, and Ranson score. All outcomes were derived after subjecting the variables to a statistical analysis.

RESULTS: In the RBF ANNs model, D-dimer, AMY, Hct, and PT were the important factors among all 11 independent variables for PVT. The normalized importance of them was 100%, 96.3%, 71.9%, and 68.2%, respectively. The predict sensitivity, specificity, and accuracy by RBF ANNs model for PVT were 76.2%, 92.0%, and 88.1%, respectively. There were significant differences between the RBF ANNs and logistic regression models in these parameters (95% CI: 110.9% [-0.4 to 15.8%]; 8.4% [-3.3 to 19.2%]; and 12.8% [1.6-20.7%], respectively). In addition, the area under receiver operating characteristic curves value for identifying thrombosis when using the RBF ANNs model was 0.892 ± 0.091 (95% CI: 0.805-0.951), demonstrating better overall performance than the logistic regression model (0.762 ± 0.073; 95% CI: 0.662-0.839).

CONCLUSIONS: The RBF ANNs model was a valuable tool in predicting the risk of PVT following AP. AMY, D-dimer, PT, and Hct were important prediction factors of approval for AP-induced PVT.