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A nomogram for predicting lung-related diseases among construction workers in Wuhan, China

OBJECTIVE: To develop a prediction nomogram for the risk of lung-related diseases (LRD) in construction workers. METHODS: Seven hundred and fifty-two construction workers were recruited. A self- designed questionnaire was performed to collected relevant information. Chest X-ray was taken to judge bu...

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Autores principales: Chen, Xuyu, Yin, Wenjun, Wu, Jie, Luo, Yongbin, Wu, Jing, Li, Guangming, Jiang, Jinfeng, Yao, Yong, Wan, Siyu, Yi, Guilin, Tan, Xiaodong
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9792134/
https://www.ncbi.nlm.nih.gov/pubmed/36579057
http://dx.doi.org/10.3389/fpubh.2022.1032188
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author Chen, Xuyu
Yin, Wenjun
Wu, Jie
Luo, Yongbin
Wu, Jing
Li, Guangming
Jiang, Jinfeng
Yao, Yong
Wan, Siyu
Yi, Guilin
Tan, Xiaodong
author_facet Chen, Xuyu
Yin, Wenjun
Wu, Jie
Luo, Yongbin
Wu, Jing
Li, Guangming
Jiang, Jinfeng
Yao, Yong
Wan, Siyu
Yi, Guilin
Tan, Xiaodong
author_sort Chen, Xuyu
collection PubMed
description OBJECTIVE: To develop a prediction nomogram for the risk of lung-related diseases (LRD) in construction workers. METHODS: Seven hundred and fifty-two construction workers were recruited. A self- designed questionnaire was performed to collected relevant information. Chest X-ray was taken to judge builders' lung health. The potential predictors subsets of the risk of LRD were screened by the least absolute shrinkage and selection operator regression and univariate analysis, and determined by using multivariate logistic regression analysis, then were used for developing a prediction nomogram for the risk of LRD. C-index, calibration curve, receiver operating characteristic curve, decision curve analysis (DCA) and clinical impact curve analysis (CICA) were used to evaluation the identification, calibration, predictive ability and clinical effectiveness of the nomogram. RESULTS: Five hundred and twenty-six construction workers were allocated to training group and 226 to validation group. The predictors included in the nomogram were symptoms, years of dust exposure, work in shifts and labor intensity. Our model showed good discrimination ability, with a bootstrap-corrected C index of 0.931 (95% CI = 0.906–0.956), and had well-fitted calibration curves. The area under the curve (AUC) of the nomogram were (95% CI = 0.906–0.956) and 0.945 (95% CI = 0.891–0.999) in the training and validation groups, respectively. The results of DCA and CICA indicated that the nomogram may have clinical usefulness. CONCLUSION: We established and validated a novel nomogram that can provide individual prediction of LRD for construction workers. This practical prediction model may help occupational physicians in decision making and design of occupational health examination.
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spelling pubmed-97921342022-12-27 A nomogram for predicting lung-related diseases among construction workers in Wuhan, China Chen, Xuyu Yin, Wenjun Wu, Jie Luo, Yongbin Wu, Jing Li, Guangming Jiang, Jinfeng Yao, Yong Wan, Siyu Yi, Guilin Tan, Xiaodong Front Public Health Public Health OBJECTIVE: To develop a prediction nomogram for the risk of lung-related diseases (LRD) in construction workers. METHODS: Seven hundred and fifty-two construction workers were recruited. A self- designed questionnaire was performed to collected relevant information. Chest X-ray was taken to judge builders' lung health. The potential predictors subsets of the risk of LRD were screened by the least absolute shrinkage and selection operator regression and univariate analysis, and determined by using multivariate logistic regression analysis, then were used for developing a prediction nomogram for the risk of LRD. C-index, calibration curve, receiver operating characteristic curve, decision curve analysis (DCA) and clinical impact curve analysis (CICA) were used to evaluation the identification, calibration, predictive ability and clinical effectiveness of the nomogram. RESULTS: Five hundred and twenty-six construction workers were allocated to training group and 226 to validation group. The predictors included in the nomogram were symptoms, years of dust exposure, work in shifts and labor intensity. Our model showed good discrimination ability, with a bootstrap-corrected C index of 0.931 (95% CI = 0.906–0.956), and had well-fitted calibration curves. The area under the curve (AUC) of the nomogram were (95% CI = 0.906–0.956) and 0.945 (95% CI = 0.891–0.999) in the training and validation groups, respectively. The results of DCA and CICA indicated that the nomogram may have clinical usefulness. CONCLUSION: We established and validated a novel nomogram that can provide individual prediction of LRD for construction workers. This practical prediction model may help occupational physicians in decision making and design of occupational health examination. Frontiers Media S.A. 2022-12-12 /pmc/articles/PMC9792134/ /pubmed/36579057 http://dx.doi.org/10.3389/fpubh.2022.1032188 Text en Copyright © 2022 Chen, Yin, Wu, Luo, Wu, Li, Jiang, Yao, Wan, Yi and Tan. 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). 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 Public Health
Chen, Xuyu
Yin, Wenjun
Wu, Jie
Luo, Yongbin
Wu, Jing
Li, Guangming
Jiang, Jinfeng
Yao, Yong
Wan, Siyu
Yi, Guilin
Tan, Xiaodong
A nomogram for predicting lung-related diseases among construction workers in Wuhan, China
title A nomogram for predicting lung-related diseases among construction workers in Wuhan, China
title_full A nomogram for predicting lung-related diseases among construction workers in Wuhan, China
title_fullStr A nomogram for predicting lung-related diseases among construction workers in Wuhan, China
title_full_unstemmed A nomogram for predicting lung-related diseases among construction workers in Wuhan, China
title_short A nomogram for predicting lung-related diseases among construction workers in Wuhan, China
title_sort nomogram for predicting lung-related diseases among construction workers in wuhan, china
topic Public Health
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9792134/
https://www.ncbi.nlm.nih.gov/pubmed/36579057
http://dx.doi.org/10.3389/fpubh.2022.1032188
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