ROC curve of APACHE II score on admission versus APACHE II score on day 1 in predicting POF.

<p>ROC curve of APACHE II score on admission versus APACHE II score on day 1 in predicting POF.</p

ROC analysis of CD4⁺ T cell proportion, CD4⁺ / CD8⁺ ratio and APACHE II scores in diagnosing POF.

<p>AUC: area under the curve; CI: confidence intervals; PPV: positive predictive value; NPV: negative predictive value.</p><p>ROC analysis of CD4⁺ T cell proportion, CD4⁺ / CD8⁺ ratio and APACHE II scores in diagnosing POF.</p

ROC curve of CD4⁺ T cell proportion versus CD4⁺/CD8⁺ ratio in predicting POF.

<p>ROC curve of CD4⁺ T cell proportion versus CD4⁺/CD8⁺ ratio in predicting POF.</p

Baseline characteristics of the AP patients.

<p>Data are presented in either means and standard deviations or frequencies and percentages.</p><p>Baseline characteristics of the AP patients.</p

Prediction of Severe Acute Pancreatitis Using a Decision Tree Model Based on the Revised Atlanta Classification of Acute Pancreatitis

<div><p>Objective</p><p>To develop a model for the early prediction of severe acute pancreatitis based on the revised Atlanta classification of acute pancreatitis.</p><p>Methods</p><p>Clinical data of 1308 patients with acute pancreatitis (AP) were included in the retrospective study. A total of 603 patients who were admitted to the hospital within 36 hours of the onset of the disease were included at last according to the inclusion criteria. The clinical data were collected within 12 hours after admission. All the patients were classified as having mild acute pancreatitis (MAP), moderately severe acute pancreatitis (MSAP) and severe acute pancreatitis (SAP) based on the revised Atlanta classification of acute pancreatitis. All the 603 patients were randomly divided into training group (402 cases) and test group (201 cases). Univariate and multiple regression analyses were used to identify the independent risk factors for the development of SAP in the training group. Then the prediction model was constructed using the decision tree method, and this model was applied to the test group to evaluate its validity.</p><p>Results</p><p>The decision tree model was developed using creatinine, lactate dehydrogenase, and oxygenation index to predict SAP. The diagnostic sensitivity and specificity of SAP in the training group were 80.9% and 90.0%, respectively, and the sensitivity and specificity in the test group were 88.6% and 90.4%, respectively.</p><p>Conclusions</p><p>The decision tree model based on creatinine, lactate dehydrogenase, and oxygenation index is more likely to predict the occurrence of SAP.</p></div

Patients stratified by the tree model in the training and the test groups.

<p>Patients stratified by the tree model in the training and the test groups.</p

Multivariate regression analyses.

<p>Multivariate regression analyses.</p

Univariate analysis of predictive factors of severe acute pancreatitis (SAP) in the training sample.

<p>Univariate analysis of predictive factors of severe acute pancreatitis (SAP) in the training sample.</p

Receiver operating characteristic curves for classification and regression tree (CART model) and Bedside Index for Severity in Acute Pancreatitis(BISAP model).

<p>Receiver operating characteristic curves for classification and regression tree (CART model) and Bedside Index for Severity in Acute Pancreatitis(BISAP model).</p

Validation of the test set of the tree model obtained by CART analysis.

<p>Validation of the test set of the tree model obtained by CART analysis.</p