Digestive Diseases and Sciences

, Volume 56, Issue 12, pp 3664–3671

Prediction of Severe Acute Pancreatitis Using Classification and Regression Tree Analysis

  • Wandong Hong
  • Lemei Dong
  • Qingke Huang
  • Wenzhi Wu
  • Jiansheng Wu
  • Yumin Wang
Original Article

DOI: 10.1007/s10620-011-1849-x

Cite this article as:
Hong, W., Dong, L., Huang, Q. et al. Dig Dis Sci (2011) 56: 3664. doi:10.1007/s10620-011-1849-x

Abstract

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 among cohorts at high (79.03%) and low (7.80%) risk of developing SAP. The area under the ROC curve of the tree model was higher than that of the APACHE II score (0.84 vs. 0.68; P < 0.001). The predicted accuracy of the tree model was validated in the test sample with an area under the ROC curve of 0.86.

Conclusions

A decision tree model that consists of pleural effusion, serum calcium, and BUN may be useful for the prediction of SAP.

Keywords

Decision tree Predictors Acute pancreatitis Severity Sensitivity and specificity 

Copyright information

© Springer Science+Business Media, LLC 2011

Authors and Affiliations

  • Wandong Hong
    • 1
  • Lemei Dong
    • 1
  • Qingke Huang
    • 1
  • Wenzhi Wu
    • 1
  • Jiansheng Wu
    • 1
  • Yumin Wang
    • 2
  1. 1.Department of Gastroenterology and HepatologyThe First Affiliated Hospital of Wenzhou Medical CollegeWenzhouPeople’s Republic of China
  2. 2.Department of Laboratory Diagnosis CenterThe First Affiliated Hospital of Wenzhou Medical CollegeWenzhouPeople’s Republic of China

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