Prediction of large esophageal varices in cirrhotic patients using classification and regression tree analysis

Detalhes bibliográficos
Autor(a) principal: Hong, Wan-dong
Data de Publicação: 2011
Outros Autores: Dong, Le-mei, Jiang, Zen-cai, Zhu, Qi-huai, Jin, Shu-Qing
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Clinics
Texto Completo: https://www.revistas.usp.br/clinics/article/view/19463
Resumo: OBJECTIVES: Recent guidelines recommend that all cirrhotic patients should undergo endoscopic screening for esophageal varices. That identifying cirrhotic patients with esophageal varices by noninvasive predictors would allow for the restriction of the performance of endoscopy to patients with a high risk of having varices. This study aimed to develop a decision model based on classification and regression tree analysis for the prediction of large esophageal varices in cirrhotic patients. METHODS: 309 cirrhotic patients (training sample, 187 patients; test sample 122 patients) were included. Within the training sample, the classification and regression tree analysis was used to identify predictors and prediction model of large esophageal varices. The prediction model was then further evaluated in the test sample and different Child-Pugh classes. RESULTS: The prevalence of large esophageal varices in cirrhotic patients was 50.8%. A tree model that was consisted of spleen width, portal vein diameter and prothrombin time was developed by classification and regression tree analysis achieved a diagnostic accuracy of 84% for prediction of large esophageal varices. When reconstructed into two groups, the rate of varices was 83.2% for high-risk group and 15.2% for low-risk group. Accuracy of the tree model was maintained in the test sample and different Child-Pugh classes. CONCLUSIONS: A decision tree model that consists of spleen width, portal vein diameter and prothrombin time may be useful for prediction of large esophageal varices in cirrhotic patients
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spelling Prediction of large esophageal varices in cirrhotic patients using classification and regression tree analysis Classification and regression tree analysisTree modelEsophageal varicesPredictorCirrhosis OBJECTIVES: Recent guidelines recommend that all cirrhotic patients should undergo endoscopic screening for esophageal varices. That identifying cirrhotic patients with esophageal varices by noninvasive predictors would allow for the restriction of the performance of endoscopy to patients with a high risk of having varices. This study aimed to develop a decision model based on classification and regression tree analysis for the prediction of large esophageal varices in cirrhotic patients. METHODS: 309 cirrhotic patients (training sample, 187 patients; test sample 122 patients) were included. Within the training sample, the classification and regression tree analysis was used to identify predictors and prediction model of large esophageal varices. The prediction model was then further evaluated in the test sample and different Child-Pugh classes. RESULTS: The prevalence of large esophageal varices in cirrhotic patients was 50.8%. A tree model that was consisted of spleen width, portal vein diameter and prothrombin time was developed by classification and regression tree analysis achieved a diagnostic accuracy of 84% for prediction of large esophageal varices. When reconstructed into two groups, the rate of varices was 83.2% for high-risk group and 15.2% for low-risk group. Accuracy of the tree model was maintained in the test sample and different Child-Pugh classes. CONCLUSIONS: A decision tree model that consists of spleen width, portal vein diameter and prothrombin time may be useful for prediction of large esophageal varices in cirrhotic patients Hospital das Clínicas, Faculdade de Medicina, Universidade de São Paulo2011-01-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfhttps://www.revistas.usp.br/clinics/article/view/1946310.1590/S1807-59322011000100021Clinics; Vol. 66 No. 1 (2011); 119-124 Clinics; v. 66 n. 1 (2011); 119-124 Clinics; Vol. 66 Núm. 1 (2011); 119-124 1980-53221807-5932reponame:Clinicsinstname:Universidade de São Paulo (USP)instacron:USPenghttps://www.revistas.usp.br/clinics/article/view/19463/21526Hong, Wan-dongDong, Le-meiJiang, Zen-caiZhu, Qi-huaiJin, Shu-Qinginfo:eu-repo/semantics/openAccess2012-05-23T16:42:14Zoai:revistas.usp.br:article/19463Revistahttps://www.revistas.usp.br/clinicsPUBhttps://www.revistas.usp.br/clinics/oai||clinics@hc.fm.usp.br1980-53221807-5932opendoar:2012-05-23T16:42:14Clinics - Universidade de São Paulo (USP)false
dc.title.none.fl_str_mv Prediction of large esophageal varices in cirrhotic patients using classification and regression tree analysis
title Prediction of large esophageal varices in cirrhotic patients using classification and regression tree analysis
spellingShingle Prediction of large esophageal varices in cirrhotic patients using classification and regression tree analysis
Hong, Wan-dong
Classification and regression tree analysis
Tree model
Esophageal varices
Predictor
Cirrhosis
title_short Prediction of large esophageal varices in cirrhotic patients using classification and regression tree analysis
title_full Prediction of large esophageal varices in cirrhotic patients using classification and regression tree analysis
title_fullStr Prediction of large esophageal varices in cirrhotic patients using classification and regression tree analysis
title_full_unstemmed Prediction of large esophageal varices in cirrhotic patients using classification and regression tree analysis
title_sort Prediction of large esophageal varices in cirrhotic patients using classification and regression tree analysis
author Hong, Wan-dong
author_facet Hong, Wan-dong
Dong, Le-mei
Jiang, Zen-cai
Zhu, Qi-huai
Jin, Shu-Qing
author_role author
author2 Dong, Le-mei
Jiang, Zen-cai
Zhu, Qi-huai
Jin, Shu-Qing
author2_role author
author
author
author
dc.contributor.author.fl_str_mv Hong, Wan-dong
Dong, Le-mei
Jiang, Zen-cai
Zhu, Qi-huai
Jin, Shu-Qing
dc.subject.por.fl_str_mv Classification and regression tree analysis
Tree model
Esophageal varices
Predictor
Cirrhosis
topic Classification and regression tree analysis
Tree model
Esophageal varices
Predictor
Cirrhosis
description OBJECTIVES: Recent guidelines recommend that all cirrhotic patients should undergo endoscopic screening for esophageal varices. That identifying cirrhotic patients with esophageal varices by noninvasive predictors would allow for the restriction of the performance of endoscopy to patients with a high risk of having varices. This study aimed to develop a decision model based on classification and regression tree analysis for the prediction of large esophageal varices in cirrhotic patients. METHODS: 309 cirrhotic patients (training sample, 187 patients; test sample 122 patients) were included. Within the training sample, the classification and regression tree analysis was used to identify predictors and prediction model of large esophageal varices. The prediction model was then further evaluated in the test sample and different Child-Pugh classes. RESULTS: The prevalence of large esophageal varices in cirrhotic patients was 50.8%. A tree model that was consisted of spleen width, portal vein diameter and prothrombin time was developed by classification and regression tree analysis achieved a diagnostic accuracy of 84% for prediction of large esophageal varices. When reconstructed into two groups, the rate of varices was 83.2% for high-risk group and 15.2% for low-risk group. Accuracy of the tree model was maintained in the test sample and different Child-Pugh classes. CONCLUSIONS: A decision tree model that consists of spleen width, portal vein diameter and prothrombin time may be useful for prediction of large esophageal varices in cirrhotic patients
publishDate 2011
dc.date.none.fl_str_mv 2011-01-01
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv https://www.revistas.usp.br/clinics/article/view/19463
10.1590/S1807-59322011000100021
url https://www.revistas.usp.br/clinics/article/view/19463
identifier_str_mv 10.1590/S1807-59322011000100021
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv https://www.revistas.usp.br/clinics/article/view/19463/21526
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Hospital das Clínicas, Faculdade de Medicina, Universidade de São Paulo
publisher.none.fl_str_mv Hospital das Clínicas, Faculdade de Medicina, Universidade de São Paulo
dc.source.none.fl_str_mv Clinics; Vol. 66 No. 1 (2011); 119-124
Clinics; v. 66 n. 1 (2011); 119-124
Clinics; Vol. 66 Núm. 1 (2011); 119-124
1980-5322
1807-5932
reponame:Clinics
instname:Universidade de São Paulo (USP)
instacron:USP
instname_str Universidade de São Paulo (USP)
instacron_str USP
institution USP
reponame_str Clinics
collection Clinics
repository.name.fl_str_mv Clinics - Universidade de São Paulo (USP)
repository.mail.fl_str_mv ||clinics@hc.fm.usp.br
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