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Difference between the blood samples of patients with bone and joint tuberculosis and patients with tuberculosis studied using machine learning

BACKGROUND: Tuberculosis (TB) is a chronic infectious disease. Bone and joint TB is a common type of extrapulmonary TB and often occurs secondary to TB infection. In this study, we aimed to find the difference in the blood examination results of patients with bone and joint TB and patients with TB b...

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Autores principales: Ye, Zhen, Zhu, Jichong, Liu, Chong, Lu, Qing, Wu, Shaofeng, Zhou, Chenxing, Liang, Tuo, Jiang, Jie, Li, Hao, Chen, Tianyou, Chen, Jiarui, Deng, Guobing, Yao, Yuanlin, Liao, Shian, Yu, Chaojie, Sun, Xuhua, Chen, Liyi, Guo, Hao, Chen, Wuhua, Jiang, Wenyong, Fan, Binguang, Tao, Xiang, Yang, Zhenwei, Gu, Wenfei, Wang, Yihan, Zhan, Xinli
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9852526/
https://www.ncbi.nlm.nih.gov/pubmed/36684125
http://dx.doi.org/10.3389/fsurg.2022.1031105
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author Ye, Zhen
Zhu, Jichong
Liu, Chong
Lu, Qing
Wu, Shaofeng
Zhou, Chenxing
Liang, Tuo
Jiang, Jie
Li, Hao
Chen, Tianyou
Chen, Jiarui
Deng, Guobing
Yao, Yuanlin
Liao, Shian
Yu, Chaojie
Sun, Xuhua
Chen, Liyi
Guo, Hao
Chen, Wuhua
Jiang, Wenyong
Fan, Binguang
Tao, Xiang
Yang, Zhenwei
Gu, Wenfei
Wang, Yihan
Zhan, Xinli
author_facet Ye, Zhen
Zhu, Jichong
Liu, Chong
Lu, Qing
Wu, Shaofeng
Zhou, Chenxing
Liang, Tuo
Jiang, Jie
Li, Hao
Chen, Tianyou
Chen, Jiarui
Deng, Guobing
Yao, Yuanlin
Liao, Shian
Yu, Chaojie
Sun, Xuhua
Chen, Liyi
Guo, Hao
Chen, Wuhua
Jiang, Wenyong
Fan, Binguang
Tao, Xiang
Yang, Zhenwei
Gu, Wenfei
Wang, Yihan
Zhan, Xinli
author_sort Ye, Zhen
collection PubMed
description BACKGROUND: Tuberculosis (TB) is a chronic infectious disease. Bone and joint TB is a common type of extrapulmonary TB and often occurs secondary to TB infection. In this study, we aimed to find the difference in the blood examination results of patients with bone and joint TB and patients with TB by using machine learning (ML) and establish a diagnostic model to help clinicians better diagnose the disease and allow patients to receive timely treatment. METHODS: A total of 1,667 patients were finally enrolled in the study. Patients were randomly assigned to the training and validation cohorts. The training cohort included 1,268 patients: 158 patients with bone and joint TB and 1,110 patients with TB. The validation cohort included 399 patients: 48 patients with bone and joint TB and 351 patients with TB. We used three ML methods, namely logistic regression, LASSO regression, and random forest, to screen the differential variables, obtained the most representative variables by intersection to construct the prediction model, and verified the performance of the proposed prediction model in the validation group. RESULTS: The results revealed a great difference in the blood examination results of patients with bone and joint TB and those with TB. Infectious markers such as hs-CRP, ESR, WBC, and NEUT were increased in patients with bone and joint TB. Patients with bone and joint TB were found to have higher liver function burden and poorer nutritional status. The factors screened using ML were PDW, LYM, AST/ALT, BUN, and Na, and the nomogram diagnostic model was constructed using these five factors. In the training cohort, the area under the curve (AUC) value of the model was 0.71182, and the C value was 0.712. In the validation cohort, the AUC value of the model was 0.6435779, and the C value was 0.644. CONCLUSION: We used ML methods to screen out the blood-specific factors—PDW, LYM, AST/ALT, BUN, and Na(+)—of bone and joint TB and constructed a diagnostic model to help clinicians better diagnose the disease in the future.
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spelling pubmed-98525262023-01-21 Difference between the blood samples of patients with bone and joint tuberculosis and patients with tuberculosis studied using machine learning Ye, Zhen Zhu, Jichong Liu, Chong Lu, Qing Wu, Shaofeng Zhou, Chenxing Liang, Tuo Jiang, Jie Li, Hao Chen, Tianyou Chen, Jiarui Deng, Guobing Yao, Yuanlin Liao, Shian Yu, Chaojie Sun, Xuhua Chen, Liyi Guo, Hao Chen, Wuhua Jiang, Wenyong Fan, Binguang Tao, Xiang Yang, Zhenwei Gu, Wenfei Wang, Yihan Zhan, Xinli Front Surg Surgery BACKGROUND: Tuberculosis (TB) is a chronic infectious disease. Bone and joint TB is a common type of extrapulmonary TB and often occurs secondary to TB infection. In this study, we aimed to find the difference in the blood examination results of patients with bone and joint TB and patients with TB by using machine learning (ML) and establish a diagnostic model to help clinicians better diagnose the disease and allow patients to receive timely treatment. METHODS: A total of 1,667 patients were finally enrolled in the study. Patients were randomly assigned to the training and validation cohorts. The training cohort included 1,268 patients: 158 patients with bone and joint TB and 1,110 patients with TB. The validation cohort included 399 patients: 48 patients with bone and joint TB and 351 patients with TB. We used three ML methods, namely logistic regression, LASSO regression, and random forest, to screen the differential variables, obtained the most representative variables by intersection to construct the prediction model, and verified the performance of the proposed prediction model in the validation group. RESULTS: The results revealed a great difference in the blood examination results of patients with bone and joint TB and those with TB. Infectious markers such as hs-CRP, ESR, WBC, and NEUT were increased in patients with bone and joint TB. Patients with bone and joint TB were found to have higher liver function burden and poorer nutritional status. The factors screened using ML were PDW, LYM, AST/ALT, BUN, and Na, and the nomogram diagnostic model was constructed using these five factors. In the training cohort, the area under the curve (AUC) value of the model was 0.71182, and the C value was 0.712. In the validation cohort, the AUC value of the model was 0.6435779, and the C value was 0.644. CONCLUSION: We used ML methods to screen out the blood-specific factors—PDW, LYM, AST/ALT, BUN, and Na(+)—of bone and joint TB and constructed a diagnostic model to help clinicians better diagnose the disease in the future. Frontiers Media S.A. 2023-01-06 /pmc/articles/PMC9852526/ /pubmed/36684125 http://dx.doi.org/10.3389/fsurg.2022.1031105 Text en © 2023 Ye, Zhu, Liu, Lu, Wu, Zhou, Liang, Jiang, Li, Chen, Chen, Deng, Yao, Liao, Yu, Sun, Chen, Guo, Chen, Jiang, Fan, Tao, Yang, Gu, Wang and Zhan. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) (https://creativecommons.org/licenses/by/4.0/) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Surgery
Ye, Zhen
Zhu, Jichong
Liu, Chong
Lu, Qing
Wu, Shaofeng
Zhou, Chenxing
Liang, Tuo
Jiang, Jie
Li, Hao
Chen, Tianyou
Chen, Jiarui
Deng, Guobing
Yao, Yuanlin
Liao, Shian
Yu, Chaojie
Sun, Xuhua
Chen, Liyi
Guo, Hao
Chen, Wuhua
Jiang, Wenyong
Fan, Binguang
Tao, Xiang
Yang, Zhenwei
Gu, Wenfei
Wang, Yihan
Zhan, Xinli
Difference between the blood samples of patients with bone and joint tuberculosis and patients with tuberculosis studied using machine learning
title Difference between the blood samples of patients with bone and joint tuberculosis and patients with tuberculosis studied using machine learning
title_full Difference between the blood samples of patients with bone and joint tuberculosis and patients with tuberculosis studied using machine learning
title_fullStr Difference between the blood samples of patients with bone and joint tuberculosis and patients with tuberculosis studied using machine learning
title_full_unstemmed Difference between the blood samples of patients with bone and joint tuberculosis and patients with tuberculosis studied using machine learning
title_short Difference between the blood samples of patients with bone and joint tuberculosis and patients with tuberculosis studied using machine learning
title_sort difference between the blood samples of patients with bone and joint tuberculosis and patients with tuberculosis studied using machine learning
topic Surgery
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9852526/
https://www.ncbi.nlm.nih.gov/pubmed/36684125
http://dx.doi.org/10.3389/fsurg.2022.1031105
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