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Risk Factors for Granulomatous Mastitis and Establishment and Validation of a Clinical Prediction Model (Nomogram)

BACKGROUND: This study aimed to explore the risk factors and clinical characteristics of granulomatous mastitis (GM) using a case-control study and establish and validate a clinical prediction model (nomogram). METHODS: This retrospective case-control study was conducted in three hospitals in China...

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Autores principales: Zeng, Yifei, Zhang, Dongxiao, Fu, Na, Zhao, Wenjie, Huang, Qiao, Cui, Jianchun, Chen, Yunru, Liu, Zhaolan, Zhang, Xiaojun, Zhang, Shiyun, Mansoor, Khattak Mazher
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Dove 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10596285/
https://www.ncbi.nlm.nih.gov/pubmed/37881167
http://dx.doi.org/10.2147/RMHP.S431228
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author Zeng, Yifei
Zhang, Dongxiao
Fu, Na
Zhao, Wenjie
Huang, Qiao
Cui, Jianchun
Chen, Yunru
Liu, Zhaolan
Zhang, Xiaojun
Zhang, Shiyun
Mansoor, Khattak Mazher
author_facet Zeng, Yifei
Zhang, Dongxiao
Fu, Na
Zhao, Wenjie
Huang, Qiao
Cui, Jianchun
Chen, Yunru
Liu, Zhaolan
Zhang, Xiaojun
Zhang, Shiyun
Mansoor, Khattak Mazher
author_sort Zeng, Yifei
collection PubMed
description BACKGROUND: This study aimed to explore the risk factors and clinical characteristics of granulomatous mastitis (GM) using a case-control study and establish and validate a clinical prediction model (nomogram). METHODS: This retrospective case-control study was conducted in three hospitals in China from June 2017 to December 2021. A total of 1634 GM patients and 186 healthy women during the same period were included and randomly divided into the modeling and validation groups in a 7:3 ratio. To identify the independent risk factors of GM, univariate and multivariate logistic analyses were conducted and used to develop a nomogram. The prediction model was internally and externally validated using the Bootstrap technique and validation cohort. The receiver operating characteristic (ROC) curve and calibration curve were used to evaluate the discrimination and calibration of the prediction model. Decision curve analysis (DCA) and clinical impact curve (CIC) were used to evaluate the clinical significance of the model. RESULTS: The average age of GM patients was 33.14 years (mainly 20–40). The incidence was high within five years from delivery and mainly occurred in the unilateral breast. The majority of the patients exhibited local skin alterations, while some also presented with systemic symptoms. On multivariate logistic analysis, age, high prolactin level, sex hormone intake, breast trauma, nipple discharge or invagination, and depression were independent risk factors for GM. The mean area under the curve (AUC) in the modeling and validation groups were 0.899 and 0.889. The internal and external validation demonstrated the model’s predictive ability and clinical value. CONCLUSION: Lactation-related factors are the main risk factors of GM, leading to milk stasis or increased ductal secretion. Meanwhile, hormone disorders could affect the secretion and expansion of mammary ducts. All these factors can obstruct or injure the duct, inducing inflammatory reactions and immune responses. Additionally, blunt trauma, depressed mood, and diet preference can accelerate the process. The nomogram can effectively predict the risk of GM.
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spelling pubmed-105962852023-10-25 Risk Factors for Granulomatous Mastitis and Establishment and Validation of a Clinical Prediction Model (Nomogram) Zeng, Yifei Zhang, Dongxiao Fu, Na Zhao, Wenjie Huang, Qiao Cui, Jianchun Chen, Yunru Liu, Zhaolan Zhang, Xiaojun Zhang, Shiyun Mansoor, Khattak Mazher Risk Manag Healthc Policy Original Research BACKGROUND: This study aimed to explore the risk factors and clinical characteristics of granulomatous mastitis (GM) using a case-control study and establish and validate a clinical prediction model (nomogram). METHODS: This retrospective case-control study was conducted in three hospitals in China from June 2017 to December 2021. A total of 1634 GM patients and 186 healthy women during the same period were included and randomly divided into the modeling and validation groups in a 7:3 ratio. To identify the independent risk factors of GM, univariate and multivariate logistic analyses were conducted and used to develop a nomogram. The prediction model was internally and externally validated using the Bootstrap technique and validation cohort. The receiver operating characteristic (ROC) curve and calibration curve were used to evaluate the discrimination and calibration of the prediction model. Decision curve analysis (DCA) and clinical impact curve (CIC) were used to evaluate the clinical significance of the model. RESULTS: The average age of GM patients was 33.14 years (mainly 20–40). The incidence was high within five years from delivery and mainly occurred in the unilateral breast. The majority of the patients exhibited local skin alterations, while some also presented with systemic symptoms. On multivariate logistic analysis, age, high prolactin level, sex hormone intake, breast trauma, nipple discharge or invagination, and depression were independent risk factors for GM. The mean area under the curve (AUC) in the modeling and validation groups were 0.899 and 0.889. The internal and external validation demonstrated the model’s predictive ability and clinical value. CONCLUSION: Lactation-related factors are the main risk factors of GM, leading to milk stasis or increased ductal secretion. Meanwhile, hormone disorders could affect the secretion and expansion of mammary ducts. All these factors can obstruct or injure the duct, inducing inflammatory reactions and immune responses. Additionally, blunt trauma, depressed mood, and diet preference can accelerate the process. The nomogram can effectively predict the risk of GM. Dove 2023-10-20 /pmc/articles/PMC10596285/ /pubmed/37881167 http://dx.doi.org/10.2147/RMHP.S431228 Text en © 2023 Zeng et al. https://creativecommons.org/licenses/by-nc/3.0/This work is published and licensed by Dove Medical Press Limited. The full terms of this license are available at https://www.dovepress.com/terms.php and incorporate the Creative Commons Attribution – Non Commercial (unported, v3.0) License (http://creativecommons.org/licenses/by-nc/3.0/ (https://creativecommons.org/licenses/by-nc/3.0/) ). By accessing the work you hereby accept the Terms. Non-commercial uses of the work are permitted without any further permission from Dove Medical Press Limited, provided the work is properly attributed. For permission for commercial use of this work, please see paragraphs 4.2 and 5 of our Terms (https://www.dovepress.com/terms.php).
spellingShingle Original Research
Zeng, Yifei
Zhang, Dongxiao
Fu, Na
Zhao, Wenjie
Huang, Qiao
Cui, Jianchun
Chen, Yunru
Liu, Zhaolan
Zhang, Xiaojun
Zhang, Shiyun
Mansoor, Khattak Mazher
Risk Factors for Granulomatous Mastitis and Establishment and Validation of a Clinical Prediction Model (Nomogram)
title Risk Factors for Granulomatous Mastitis and Establishment and Validation of a Clinical Prediction Model (Nomogram)
title_full Risk Factors for Granulomatous Mastitis and Establishment and Validation of a Clinical Prediction Model (Nomogram)
title_fullStr Risk Factors for Granulomatous Mastitis and Establishment and Validation of a Clinical Prediction Model (Nomogram)
title_full_unstemmed Risk Factors for Granulomatous Mastitis and Establishment and Validation of a Clinical Prediction Model (Nomogram)
title_short Risk Factors for Granulomatous Mastitis and Establishment and Validation of a Clinical Prediction Model (Nomogram)
title_sort risk factors for granulomatous mastitis and establishment and validation of a clinical prediction model (nomogram)
topic Original Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10596285/
https://www.ncbi.nlm.nih.gov/pubmed/37881167
http://dx.doi.org/10.2147/RMHP.S431228
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