Predicting the incidence of portosplenomesenteric vein thrombosis in patients with acute pancreatitis using classification and regression tree algorithm

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Background and objective:The accurate prediction of portosplenomesenteric vein thrombosis (PVT) in patients with acute pancreatitis(AP) is very important but may also be difficult because of our insufficient understanding of the characteristics of AP-induced PVT. The purpose of this study is to design a decision tree model that provides critical factors associated with PVT using an approach that makes use of classification and regression tree (CART) algorithm.Methods:The analysis included 353 patients with AP who were admitted between January 2011 and December 2015. CART model and logistic regression model were each applied to the same 50% of the sample to develop the predictive training models, and these models were tested on the remaining 50%. Statistical indexes were used to evaluate the value of the prediction in the 2 models.Results:The predicted sensitivity, specificity, positive predictive value, negative predictive value, and accuracy by CART for PVT were 78.0%, 87.2%, 64.0%, 93.2%, and 85.2%, respectively. Significant differences could be found between the CART model and the logistic regression model in these parameters. There were significant differences between the CART and logistic regression models in these parameters (P<.05). When the CART model was used to identify PVT, the area under receiver operating characteristic curve was 0.803, which demonstrated better overall properties than the logistic regression model (area under the curve = 0.696) (95% confidence interval, 0.603-0.812).Conclusion:The CART model based on serum amylase, D-dimer, Acute Physiology and Chronic Health Evaluation II, and prothrombin time is more likely to predict the occurrence of PVT induced by AP.HighlightsIt was the first time to establish the successful use of decision tree modeling in prediction the occurrence of PVT followingAP.Decision tree modeling offered an alternative medical modeling to traditional logistic regression model in prediction the incidence of PVT; furthermore, it was more sensitive, specific, and accurate than logistic regression models.APACHE-II score and D-dimer were the important factors among all 11 independent variables for PVT concluded by decision tree algorithm.

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