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Liver cirrhosis prediction for patients with Wilson disease based on machine learning: a case–control study from southwest China

Wilson disease (WD) is a rare autosomal recessive disease caused by an ATP7B gene mutation. Liver cirrhosis is an important issue that affects the clinical management and prognosis of WD patients. Blood routine examination is a potential biomarker for predicting the occurrence of liver cirrhosis in...

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Autores principales: Chen, Ke, Wan, Yang, Mao, Ju, Lai, Yuqing, Zhuo-ma, Gesang, Hong, Peiwei
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Lippincott Williams And Wilkins 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9439697/
https://www.ncbi.nlm.nih.gov/pubmed/35895997
http://dx.doi.org/10.1097/MEG.0000000000002424
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author Chen, Ke
Wan, Yang
Mao, Ju
Lai, Yuqing
Zhuo-ma, Gesang
Hong, Peiwei
author_facet Chen, Ke
Wan, Yang
Mao, Ju
Lai, Yuqing
Zhuo-ma, Gesang
Hong, Peiwei
author_sort Chen, Ke
collection PubMed
description Wilson disease (WD) is a rare autosomal recessive disease caused by an ATP7B gene mutation. Liver cirrhosis is an important issue that affects the clinical management and prognosis of WD patients. Blood routine examination is a potential biomarker for predicting the occurrence of liver cirrhosis in WD. We aim to construct a predictive model for the occurrence of liver cirrhosis using general clinical information, blood routine examination, urine copper, and serum ceruloplasmin through a machine learning approach. METHODS: Case–control study of WD patients admitted to West China Fourth Hospital between 2005 and 2020. Patients with a score of at least four in scoring system of WD were enrolled. A machine learning model was constructed by EmpowerStats software according to the general clinical data, blood routine examination, 24 h urinary copper, and serum ceruloplasmin. RESULTS: This study analyzed 346 WD patients, of which 246 were without liver cirrhosis. And we found platelet large cell count (P-LCC), red cell distribution width CV (RDW-CV), serum ceruloplasmin, age at diagnosis, and mean corpuscular volume (MCV) were the top five important predictors. Moreover, the model was of high accuracy, with an area under the receiver operating characteristic curve of 0.9998 in the training set and 0.7873 in the testing set. CONCLUSIONS: In conclusion, the predictive model for predicting liver cirrhosis in WD, constructed by machine learning, had a higher accuracy. And the most important indices in the predictive model were P-LCC, RDW-CV, serum ceruloplasmin, age at diagnosis, and MCV.
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spelling pubmed-94396972022-09-06 Liver cirrhosis prediction for patients with Wilson disease based on machine learning: a case–control study from southwest China Chen, Ke Wan, Yang Mao, Ju Lai, Yuqing Zhuo-ma, Gesang Hong, Peiwei Eur J Gastroenterol Hepatol Original Articles: Hepatology Wilson disease (WD) is a rare autosomal recessive disease caused by an ATP7B gene mutation. Liver cirrhosis is an important issue that affects the clinical management and prognosis of WD patients. Blood routine examination is a potential biomarker for predicting the occurrence of liver cirrhosis in WD. We aim to construct a predictive model for the occurrence of liver cirrhosis using general clinical information, blood routine examination, urine copper, and serum ceruloplasmin through a machine learning approach. METHODS: Case–control study of WD patients admitted to West China Fourth Hospital between 2005 and 2020. Patients with a score of at least four in scoring system of WD were enrolled. A machine learning model was constructed by EmpowerStats software according to the general clinical data, blood routine examination, 24 h urinary copper, and serum ceruloplasmin. RESULTS: This study analyzed 346 WD patients, of which 246 were without liver cirrhosis. And we found platelet large cell count (P-LCC), red cell distribution width CV (RDW-CV), serum ceruloplasmin, age at diagnosis, and mean corpuscular volume (MCV) were the top five important predictors. Moreover, the model was of high accuracy, with an area under the receiver operating characteristic curve of 0.9998 in the training set and 0.7873 in the testing set. CONCLUSIONS: In conclusion, the predictive model for predicting liver cirrhosis in WD, constructed by machine learning, had a higher accuracy. And the most important indices in the predictive model were P-LCC, RDW-CV, serum ceruloplasmin, age at diagnosis, and MCV. Lippincott Williams And Wilkins 2022-07-25 2022-10 /pmc/articles/PMC9439697/ /pubmed/35895997 http://dx.doi.org/10.1097/MEG.0000000000002424 Text en Copyright © 2022 The Author(s). Published by Wolters Kluwer Health, Inc. https://creativecommons.org/licenses/by-nc-nd/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution-Non Commercial-No Derivatives License 4.0 (CCBY-NC-ND) (https://creativecommons.org/licenses/by-nc-nd/4.0/) , where it is permissible to download and share the work provided it is properly cited. The work cannot be changed in any way or used commercially without permission from the journal.
spellingShingle Original Articles: Hepatology
Chen, Ke
Wan, Yang
Mao, Ju
Lai, Yuqing
Zhuo-ma, Gesang
Hong, Peiwei
Liver cirrhosis prediction for patients with Wilson disease based on machine learning: a case–control study from southwest China
title Liver cirrhosis prediction for patients with Wilson disease based on machine learning: a case–control study from southwest China
title_full Liver cirrhosis prediction for patients with Wilson disease based on machine learning: a case–control study from southwest China
title_fullStr Liver cirrhosis prediction for patients with Wilson disease based on machine learning: a case–control study from southwest China
title_full_unstemmed Liver cirrhosis prediction for patients with Wilson disease based on machine learning: a case–control study from southwest China
title_short Liver cirrhosis prediction for patients with Wilson disease based on machine learning: a case–control study from southwest China
title_sort liver cirrhosis prediction for patients with wilson disease based on machine learning: a case–control study from southwest china
topic Original Articles: Hepatology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9439697/
https://www.ncbi.nlm.nih.gov/pubmed/35895997
http://dx.doi.org/10.1097/MEG.0000000000002424
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