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Prediction of massive bleeding in pancreatic surgery based on preoperative patient characteristics using a decision tree
Massive intraoperative blood loss (IBL) negatively influence outcomes after surgery for pancreatic ductal adenocarcinoma (PDAC). However, few data or predictive models are available for the identification of patients with a high risk for massive IBL. This study aimed to build a model for massive IBL...
Autores principales: | , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8577735/ https://www.ncbi.nlm.nih.gov/pubmed/34752505 http://dx.doi.org/10.1371/journal.pone.0259682 |
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author | Wakiya, Taiichi Ishido, Keinosuke Kimura, Norihisa Nagase, Hayato Kubota, Shunsuke Fujita, Hiroaki Hagiwara, Yusuke Kanda, Taishu Matsuzaka, Masashi Sasaki, Yoshihiro Hakamada, Kenichi |
author_facet | Wakiya, Taiichi Ishido, Keinosuke Kimura, Norihisa Nagase, Hayato Kubota, Shunsuke Fujita, Hiroaki Hagiwara, Yusuke Kanda, Taishu Matsuzaka, Masashi Sasaki, Yoshihiro Hakamada, Kenichi |
author_sort | Wakiya, Taiichi |
collection | PubMed |
description | Massive intraoperative blood loss (IBL) negatively influence outcomes after surgery for pancreatic ductal adenocarcinoma (PDAC). However, few data or predictive models are available for the identification of patients with a high risk for massive IBL. This study aimed to build a model for massive IBL prediction using a decision tree algorithm, which is one machine learning method. One hundred and seventy-five patients undergoing curative surgery for resectable PDAC at our facility between January 2007 and October 2020 were allocated to training (n = 128) and testing (n = 47) sets. Using the preoperatively available data of the patients (34 variables), we built a decision tree classification algorithm. Of the 175 patients, massive IBL occurred in 88 patients (50.3%). Binary logistic regression analysis indicated that alanine aminotransferase and distal pancreatectomy were significant predictors of massive IBL occurrence with an overall correct prediction rate of 70.3%. Decision tree analysis automatically selected 14 predictive variables. The best predictor was the surgical procedure. Though massive IBL was not common, the outcome of patients with distal pancreatectomy was secondarily split by glutamyl transpeptidase. Among patients who underwent PD (n = 83), diabetes mellitus (DM) was selected as the variable in the second split. Of the 21 patients with DM, massive IBL occurred in 85.7%. Decision tree sensitivity was 98.5% in the training data set and 100% in the testing data set. Our findings suggested that a decision tree can provide a new potential approach to predict massive IBL in surgery for resectable PDAC. |
format | Online Article Text |
id | pubmed-8577735 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-85777352021-11-10 Prediction of massive bleeding in pancreatic surgery based on preoperative patient characteristics using a decision tree Wakiya, Taiichi Ishido, Keinosuke Kimura, Norihisa Nagase, Hayato Kubota, Shunsuke Fujita, Hiroaki Hagiwara, Yusuke Kanda, Taishu Matsuzaka, Masashi Sasaki, Yoshihiro Hakamada, Kenichi PLoS One Research Article Massive intraoperative blood loss (IBL) negatively influence outcomes after surgery for pancreatic ductal adenocarcinoma (PDAC). However, few data or predictive models are available for the identification of patients with a high risk for massive IBL. This study aimed to build a model for massive IBL prediction using a decision tree algorithm, which is one machine learning method. One hundred and seventy-five patients undergoing curative surgery for resectable PDAC at our facility between January 2007 and October 2020 were allocated to training (n = 128) and testing (n = 47) sets. Using the preoperatively available data of the patients (34 variables), we built a decision tree classification algorithm. Of the 175 patients, massive IBL occurred in 88 patients (50.3%). Binary logistic regression analysis indicated that alanine aminotransferase and distal pancreatectomy were significant predictors of massive IBL occurrence with an overall correct prediction rate of 70.3%. Decision tree analysis automatically selected 14 predictive variables. The best predictor was the surgical procedure. Though massive IBL was not common, the outcome of patients with distal pancreatectomy was secondarily split by glutamyl transpeptidase. Among patients who underwent PD (n = 83), diabetes mellitus (DM) was selected as the variable in the second split. Of the 21 patients with DM, massive IBL occurred in 85.7%. Decision tree sensitivity was 98.5% in the training data set and 100% in the testing data set. Our findings suggested that a decision tree can provide a new potential approach to predict massive IBL in surgery for resectable PDAC. Public Library of Science 2021-11-09 /pmc/articles/PMC8577735/ /pubmed/34752505 http://dx.doi.org/10.1371/journal.pone.0259682 Text en © 2021 Wakiya et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article Wakiya, Taiichi Ishido, Keinosuke Kimura, Norihisa Nagase, Hayato Kubota, Shunsuke Fujita, Hiroaki Hagiwara, Yusuke Kanda, Taishu Matsuzaka, Masashi Sasaki, Yoshihiro Hakamada, Kenichi Prediction of massive bleeding in pancreatic surgery based on preoperative patient characteristics using a decision tree |
title | Prediction of massive bleeding in pancreatic surgery based on preoperative patient characteristics using a decision tree |
title_full | Prediction of massive bleeding in pancreatic surgery based on preoperative patient characteristics using a decision tree |
title_fullStr | Prediction of massive bleeding in pancreatic surgery based on preoperative patient characteristics using a decision tree |
title_full_unstemmed | Prediction of massive bleeding in pancreatic surgery based on preoperative patient characteristics using a decision tree |
title_short | Prediction of massive bleeding in pancreatic surgery based on preoperative patient characteristics using a decision tree |
title_sort | prediction of massive bleeding in pancreatic surgery based on preoperative patient characteristics using a decision tree |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8577735/ https://www.ncbi.nlm.nih.gov/pubmed/34752505 http://dx.doi.org/10.1371/journal.pone.0259682 |
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