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Development and performance assessment of novel machine learning models to predict pneumonia after liver transplantation

BACKGROUND: Pneumonia is the most frequently encountered postoperative pulmonary complications (PPC) after orthotopic liver transplantation (OLT), which cause high morbidity and mortality rates. We aimed to develop a model to predict postoperative pneumonia in OLT patients using machine learning (ML...

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Autores principales: Chen, Chaojin, Yang, Dong, Gao, Shilong, Zhang, Yihan, Chen, Liubing, Wang, Bohan, Mo, Zihan, Yang, Yang, Hei, Ziqing, Zhou, Shaoli
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8011203/
https://www.ncbi.nlm.nih.gov/pubmed/33789673
http://dx.doi.org/10.1186/s12931-021-01690-3
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author Chen, Chaojin
Yang, Dong
Gao, Shilong
Zhang, Yihan
Chen, Liubing
Wang, Bohan
Mo, Zihan
Yang, Yang
Hei, Ziqing
Zhou, Shaoli
author_facet Chen, Chaojin
Yang, Dong
Gao, Shilong
Zhang, Yihan
Chen, Liubing
Wang, Bohan
Mo, Zihan
Yang, Yang
Hei, Ziqing
Zhou, Shaoli
author_sort Chen, Chaojin
collection PubMed
description BACKGROUND: Pneumonia is the most frequently encountered postoperative pulmonary complications (PPC) after orthotopic liver transplantation (OLT), which cause high morbidity and mortality rates. We aimed to develop a model to predict postoperative pneumonia in OLT patients using machine learning (ML) methods. METHODS: Data of 786 adult patients underwent OLT at the Third Affiliated Hospital of Sun Yat-sen University from January 2015 to September 2019 was retrospectively extracted from electronic medical records and randomly subdivided into a training set and a testing set. With the training set, six ML models including logistic regression (LR), support vector machine (SVM), random forest (RF), adaptive boosting (AdaBoost), extreme gradient boosting (XGBoost) and gradient boosting machine (GBM) were developed. These models were assessed by the area under curve (AUC) of receiver operating characteristic on the testing set. The related risk factors and outcomes of pneumonia were also probed based on the chosen model. RESULTS: 591 OLT patients were eventually included and 253 (42.81%) were diagnosed with postoperative pneumonia, which was associated with increased postoperative hospitalization and mortality (P < 0.05). Among the six ML models, XGBoost model performed best. The AUC of XGBoost model on the testing set was 0.734 (sensitivity: 52.6%; specificity: 77.5%). Pneumonia was notably associated with 14 items features: INR, HCT, PLT, ALB, ALT, FIB, WBC, PT, serum Na(+), TBIL, anesthesia time, preoperative length of stay, total fluid transfusion and operation time. CONCLUSION: Our study firstly demonstrated that the XGBoost model with 14 common variables might predict postoperative pneumonia in OLT patients. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12931-021-01690-3.
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spelling pubmed-80112032021-03-31 Development and performance assessment of novel machine learning models to predict pneumonia after liver transplantation Chen, Chaojin Yang, Dong Gao, Shilong Zhang, Yihan Chen, Liubing Wang, Bohan Mo, Zihan Yang, Yang Hei, Ziqing Zhou, Shaoli Respir Res Research BACKGROUND: Pneumonia is the most frequently encountered postoperative pulmonary complications (PPC) after orthotopic liver transplantation (OLT), which cause high morbidity and mortality rates. We aimed to develop a model to predict postoperative pneumonia in OLT patients using machine learning (ML) methods. METHODS: Data of 786 adult patients underwent OLT at the Third Affiliated Hospital of Sun Yat-sen University from January 2015 to September 2019 was retrospectively extracted from electronic medical records and randomly subdivided into a training set and a testing set. With the training set, six ML models including logistic regression (LR), support vector machine (SVM), random forest (RF), adaptive boosting (AdaBoost), extreme gradient boosting (XGBoost) and gradient boosting machine (GBM) were developed. These models were assessed by the area under curve (AUC) of receiver operating characteristic on the testing set. The related risk factors and outcomes of pneumonia were also probed based on the chosen model. RESULTS: 591 OLT patients were eventually included and 253 (42.81%) were diagnosed with postoperative pneumonia, which was associated with increased postoperative hospitalization and mortality (P < 0.05). Among the six ML models, XGBoost model performed best. The AUC of XGBoost model on the testing set was 0.734 (sensitivity: 52.6%; specificity: 77.5%). Pneumonia was notably associated with 14 items features: INR, HCT, PLT, ALB, ALT, FIB, WBC, PT, serum Na(+), TBIL, anesthesia time, preoperative length of stay, total fluid transfusion and operation time. CONCLUSION: Our study firstly demonstrated that the XGBoost model with 14 common variables might predict postoperative pneumonia in OLT patients. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12931-021-01690-3. BioMed Central 2021-03-31 2021 /pmc/articles/PMC8011203/ /pubmed/33789673 http://dx.doi.org/10.1186/s12931-021-01690-3 Text en © The Author(s) 2021 Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Research
Chen, Chaojin
Yang, Dong
Gao, Shilong
Zhang, Yihan
Chen, Liubing
Wang, Bohan
Mo, Zihan
Yang, Yang
Hei, Ziqing
Zhou, Shaoli
Development and performance assessment of novel machine learning models to predict pneumonia after liver transplantation
title Development and performance assessment of novel machine learning models to predict pneumonia after liver transplantation
title_full Development and performance assessment of novel machine learning models to predict pneumonia after liver transplantation
title_fullStr Development and performance assessment of novel machine learning models to predict pneumonia after liver transplantation
title_full_unstemmed Development and performance assessment of novel machine learning models to predict pneumonia after liver transplantation
title_short Development and performance assessment of novel machine learning models to predict pneumonia after liver transplantation
title_sort development and performance assessment of novel machine learning models to predict pneumonia after liver transplantation
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8011203/
https://www.ncbi.nlm.nih.gov/pubmed/33789673
http://dx.doi.org/10.1186/s12931-021-01690-3
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