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Prediction of Severe Acute Pancreatitis Using Classification and Regression Tree Analysis by Wandong Hong; Lemei Dong; Qingke Huang; Wenzhi Wu; Jiansheng Wu; Yumin Wang is a Medicine article available to read on EtoBox.

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Background The available prognostic scoring systems for acute pancreatitis have limitations that restrict their clinical value. Aims To develop a decision model based on classification and regression tree (CART) analysis for the prediction of severe acute pancreatitis (SAP). Methods A total of 420 patients with acute pancreatitis were enrolled. Study participants were randomly assigned to the training sample and test sample in a 2:1 ratio. First, univariate analysis and logistic regression analysis were used to identify predictors associated with SAP in the training sample. Then, CART analysis was carried out to develop a simple tree model for the prediction of SAP. A receiver operating characteristic (ROC) curve was constructed in order to assess the performance of the model. The prediction model was then applied to the test sample. Results Four variables (systemic inflammatory response syndrome [SIRS], pleural effusion, serum calcium, and blood urea nitrogen [BUN]) were identified as important predictors of SAP by logistic regression analysis. A tree model (which consisted of pleural effusion, serum calcium, and BUN) that was developed by CART analysis was able to early identify

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Author

Wandong Hong; Lemei Dong; Qingke Huang; Wenzhi Wu; Jiansheng Wu; Yumin Wang

Publisher

Springer Science and Business Media LLC

Published

2011

Language

EN

Field

Medicine (Health Sciences)