Journal of Clinical and Translational Hepatology

Journal of Clinical and Translational Hepatology

Thursday, 12 / 09 / 2021

Articles

Abstract

ORIGINAL ARTICLE

A Simple and Quick Screening Method for Intrapulmonary Vascular Dilation in Cirrhotic Patients Based on Machine Learning

Yu-Jie Li1,# , Kun-Hua Zhong2,3,4,# , Xue-Hong Bai1 , Xi Tang1 , Peng Li1 , Zhi-Yong Yang1 , Hong-Yu Zhi1 , Xiao-Jun Li1, Yang Chen1 , Peng Deng1 , Xiao-Lin Qin2,3, Jian-Teng Gu1 , Jiao-Lin Ning1, Kai-Zhi Lu1 , Ju Zhang3,4, Zheng-Yuan Xia5 , Yu-Wen Chen2,3,4,* and Bin Yi1,*

1  Department of Anaesthesiology, Southwest Hospital, Third Military Medical University (First Affiliated Hospital of Army Medical University), Chongqing, China
2  Chengdu Institute of Computer Applications, Chinese Academy of Sciences, Chengdu, Sichuan, China
3  University of Chinese Academy of Sciences, Beijing, China
4  Chongqing Institute of Green and Intelligent Technology, Chinese Academy of Science, Chongqing, China
5  Department of Anaesthesiology, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, China
*Yu-Jie Li and Kun-Hua Zhong contributed equally to this study.
*Correspondence to:Bin Yi, Department of Anaesthesiology, Southwest Hospital, Third Military Medical University (First Affiliated Hospital of Army Medical University), Chongqing 400038, China. ORCID: https://orcid.org/0000-0001-5840-2086 . Tel: +86-23-68765366, Fax: +86-23-65463270, E-mail: This email address is being protected from spambots. You need JavaScript enabled to view it. ; Yu-Wen Chen, Chongqing Institute of Green and Intelligent Technology, Chinese Academy of Science, Chongqing 400714, China. ORCID: https://orcid.org/0000-0003-4032-5937 . Tel: +86 23 65935509, Fax: +86-23-65935000, E-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.

Journal of Clinical and Translational Hepatology 2021;9(5):682-689 DOI: 10.14218/JCTH.2020.00184
Received:December 28, 2020 Accepted:April 7, 2021 Published online:April 29, 2021

Abstract

Background and Aims:Screening for hepatopulmonary syndrome in cirrhotic patients is limited due to the need to perform contrast enhanced echocardiography (CEE) and arterial blood gas (ABG) analysis. We aimed to develop a simple and quick method to screen for the presence of intrapulmonary vascular dilation (IPVD) using noninvasive and easily available variables with machine learning (ML) algorithms.

Methods:Cirrhotic patients were enrolled from our hospital. All eligible patients underwent CEE, ABG analysis and physical examination. We developed a two-step model based on three ML algorithms, namely, adaptive boosting (termed AdaBoost), gradient boosting decision tree (termed GBDT) and eXtreme gradient boosting (termed Xgboost). Noninvasive variables were input in the first step (the NI model), and for the second step (the NIBG model), a combination of noninvasive variables and ABG results were used. Model performance was determined by the area under the curve of receiver operating characteristics (AUCROCs), precision, recall, F1-score and accuracy.

Results:A total of 193 cirrhotic patients were ultimately analyzed. The AUCROCs of the NI and NIBG models were 0.850 (0.738–0.962) and 0.867 (0.760–0.973), respectively, and both had an accuracy of 87.2%. For both negative and positive cases, the recall values of the NI and NIBG models were both 0.867 (0.760–0.973) and 0.875 (0.771–0.979), respectively, and the precisions were 0.813 (0.690–0.935) and 0.913 (0.825–1.000), respectively.

Conclusions:We developed a two-step model based on ML using noninvasive variables and ABG results to screen for the presence of IPVD in cirrhotic patients. This model may partly solve the problem of limited access to CEE and ABG by a large numbers of cirrhotic patients.

Keywords

Hepatopulmonary syndrome, Intrapulmonary vascular dilation, Cirrhosis, Screening, Machine learning

Journal of Clinical and Translational Hepatology 2021 vol. 9, 682-689  [ Html  ] [ PDF Full-text ]

© 2021 Authors. This is an Open Access article distributed under the terms of the  Creative Commons Attribution-Noncommercial 4.0 License(CC BY-NC 4.0), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

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