Prediction of large esophageal varices in cirrhotic patients using classification and regression tree analysis
Autor(a) principal: | |
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Data de Publicação: | 2011 |
Outros Autores: | , , , |
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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Clinics |
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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 |
_version_ |
1800222757309382656 |