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

To develop a model for the early prediction of severe acute pancreatitis based on the revised Atlanta classification of acute pancreatitis. ; 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 hospita... Full description

Journal Title: 2015 Vol.10(11), p.e0143486
Main Author: Yang, Zhiyong
Other Authors: Dong, Liming , Zhang, Yushun , Yang, Chong , Gou, Shanmiao , Li, Yongfeng , Xiong, Jiongxin , Wu, Heshui , Wang, Chunyou
Format: Electronic Article Electronic Article
Language: English
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ID: E-ISSN: 1932-6203 ; DOI: 10.1371/journal.pone.0143486
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recordid: plos10.1371/journal.pone.0143486
title: Prediction of Severe Acute Pancreatitis Using a Decision Tree Model Based on the Revised Atlanta Classification of Acute Pancreatitis (Prediction of Severe Acute Pancreatitis Using Decision Tree Model)
format: Article
creator:
  • Yang, Zhiyong
  • Dong, Liming
  • Zhang, Yushun
  • Yang, Chong
  • Gou, Shanmiao
  • Li, Yongfeng
  • Xiong, Jiongxin
  • Wu, Heshui
  • Wang, Chunyou
subjects:
  • Research Article
ispartof: 2015, Vol.10(11), p.e0143486
description: To develop a model for the early prediction of severe acute pancreatitis based on the revised Atlanta classification of acute pancreatitis. ; 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. ; 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. ; The decision tree model based on creatinine, lactate dehydrogenase, and oxygenation index is more likely to predict the occurrence of SAP.
language: eng
source:
identifier: E-ISSN: 1932-6203 ; DOI: 10.1371/journal.pone.0143486
fulltext: fulltext
issn:
  • 1932-6203
  • 19326203
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titlePrediction of Severe Acute Pancreatitis Using a Decision Tree Model Based on the Revised Atlanta Classification of Acute Pancreatitis (Prediction of Severe Acute Pancreatitis Using Decision Tree Model)
creatorYang, Zhiyong ; Dong, Liming ; Zhang, Yushun ; Yang, Chong ; Gou, Shanmiao ; Li, Yongfeng ; Xiong, Jiongxin ; Wu, Heshui ; Wang, Chunyou
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descriptionTo develop a model for the early prediction of severe acute pancreatitis based on the revised Atlanta classification of acute pancreatitis. ; 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. ; 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. ; The decision tree model based on creatinine, lactate dehydrogenase, and oxygenation index is more likely to predict the occurrence of SAP.
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titlePrediction of Severe Acute Pancreatitis Using a Decision Tree Model Based on the Revised Atlanta Classification of Acute Pancreatitis (Prediction of Severe Acute Pancreatitis Using Decision Tree Model)
descriptionTo develop a model for the early prediction of severe acute pancreatitis based on the revised Atlanta classification of acute pancreatitis. ; 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. ; 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. ; The decision tree model based on creatinine, lactate dehydrogenase, and oxygenation index is more likely to predict the occurrence of SAP.
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abstractTo develop a model for the early prediction of severe acute pancreatitis based on the revised Atlanta classification of acute pancreatitis. ; 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. ; 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. ; The decision tree model based on creatinine, lactate dehydrogenase, and oxygenation index is more likely to predict the occurrence of SAP.
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pubPublic Library of Science
doi10.1371/journal.pone.0143486
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date2015-11-18