Department of Gastroenterology and Hepatology, The First Affiliated Hospital of Wenzhou Medical College, No. 2, Fu Xue Road, 325000 Wenzhou, Zhejiang, People's Republic of China.
Dig Dis Sci. 2011 Dec;56(12):3664-71. doi: 10.1007/s10620-011-1849-x. Epub 2011 Aug 11.
The available prognostic scoring systems for acute pancreatitis have limitations that restrict their clinical value.
To develop a decision model based on classification and regression tree (CART) analysis for the prediction of severe acute pancreatitis (SAP).
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.
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.
A decision tree model that consists of pleural effusion, serum calcium, and BUN may be useful for the prediction of SAP.
现有的用于急性胰腺炎的预后评分系统存在局限性，限制了其临床价值。
基于分类回归树（CART）分析开发用于预测重症急性胰腺炎（SAP）的决策模型。
共纳入 420 例急性胰腺炎患者。研究对象按 2:1 的比例随机分配到训练样本和测试样本中。首先，对训练样本进行单因素分析和逻辑回归分析，以确定与 SAP 相关的预测因子。然后，进行 CART 分析，建立 SAP 预测的简单树模型。构建受试者工作特征（ROC）曲线以评估模型的性能。然后将预测模型应用于测试样本。
逻辑回归分析确定了 4 个变量（全身炎症反应综合征[SIRS]、胸腔积液、血清钙和血尿素氮[BUN]）是 SAP 的重要预测因子。CART 分析建立的树模型（由胸腔积液、血清钙和 BUN 组成）能够早期识别出 SAP 高危（79.03%）和低危（7.80%）队列。树模型的 ROC 曲线下面积高于 APACHE II 评分（0.84 比 0.68；P<0.001）。在测试样本中，树模型的预测准确性通过 ROC 曲线下面积得到验证，为 0.86。
由胸腔积液、血清钙和 BUN 组成的决策树模型可能有助于 SAP 的预测。
