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...

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Autores principales:	Taiichi Wakiya, Keinosuke Ishido, Norihisa Kimura, Hayato Nagase, Shunsuke Kubota, Hiroaki Fujita, Yusuke Hagiwara, Taishu Kanda, Masashi Matsuzaka, Yoshihiro Sasaki, Kenichi Hakamada
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/50323a741cea4a9ca7419758d8f209f0
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spelling	oai:doaj.org-article:50323a741cea4a9ca7419758d8f209f02021-11-18T06:34:25ZPrediction of massive bleeding in pancreatic surgery based on preoperative patient characteristics using a decision tree1932-6203https://doaj.org/article/50323a741cea4a9ca7419758d8f209f02021-01-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC8577735/?tool=EBIhttps://doaj.org/toc/1932-6203Massive 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.Taiichi WakiyaKeinosuke IshidoNorihisa KimuraHayato NagaseShunsuke KubotaHiroaki FujitaYusuke HagiwaraTaishu KandaMasashi MatsuzakaYoshihiro SasakiKenichi HakamadaPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 16, Iss 11 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Taiichi Wakiya Keinosuke Ishido Norihisa Kimura Hayato Nagase Shunsuke Kubota Hiroaki Fujita Yusuke Hagiwara Taishu Kanda Masashi Matsuzaka Yoshihiro Sasaki Kenichi Hakamada Prediction of massive bleeding in pancreatic surgery based on preoperative patient characteristics using a decision tree
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	article
author	Taiichi Wakiya Keinosuke Ishido Norihisa Kimura Hayato Nagase Shunsuke Kubota Hiroaki Fujita Yusuke Hagiwara Taishu Kanda Masashi Matsuzaka Yoshihiro Sasaki Kenichi Hakamada
author_facet	Taiichi Wakiya Keinosuke Ishido Norihisa Kimura Hayato Nagase Shunsuke Kubota Hiroaki Fujita Yusuke Hagiwara Taishu Kanda Masashi Matsuzaka Yoshihiro Sasaki Kenichi Hakamada
author_sort	Taiichi Wakiya
title	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_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_sort	prediction of massive bleeding in pancreatic surgery based on preoperative patient characteristics using a decision tree
publisher	Public Library of Science (PLoS)
publishDate	2021
url	https://doaj.org/article/50323a741cea4a9ca7419758d8f209f0
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Prediction of massive bleeding in pancreatic surgery based on preoperative patient characteristics using a decision tree

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