Construction of a Multi-Indicator Model for Abscess Prediction in Granulomatous Lobular Mastitis Using Inflammatory Indicators
Nan-Nan Du, Jia-Mei Feng, Shi-Jun Shao, Hua Wan, Xue-Qing Wu Breast Department, Shuguang Hospital Affiliated to Shanghai University of Traditional Chinese Medicine, Shanghai, 200021, People’s Republic of ChinaCorrespondence: Xue-Qing Wu; Hua Wan, Breast Department, Shuguang Hospital Affiliated to Sh...
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| Format: | Article |
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Dove Medical Press
2024-02-01
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| Series: | Journal of Inflammation Research |
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| Online Access: | https://www.dovepress.com/construction-of-a-multi-indicator-model-for-abscess-prediction-in-gran-peer-reviewed-fulltext-article-JIR |
| _version_ | 1797335710677598208 |
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| author | Du NN Feng JM Shao SJ Wan H Wu XQ |
| author_facet | Du NN Feng JM Shao SJ Wan H Wu XQ |
| author_sort | Du NN |
| collection | DOAJ |
| description | Nan-Nan Du, Jia-Mei Feng, Shi-Jun Shao, Hua Wan, Xue-Qing Wu Breast Department, Shuguang Hospital Affiliated to Shanghai University of Traditional Chinese Medicine, Shanghai, 200021, People’s Republic of ChinaCorrespondence: Xue-Qing Wu; Hua Wan, Breast Department, Shuguang Hospital Affiliated to Shanghai University of Traditional Chinese Medicine, Shanghai, 200021, People’s Republic of China, Tel +86 13817792022 ; +86 13611666266, Email snow_zi@hotmail.com; drwanhua@163.comBackground: Granulomatous lobular mastitis (GLM) is a chronic inflammatory breast disease, and abscess formation is a common complication of GLM. The process of abscess formation is accompanied by changes in multiple inflammatory markers. The present study aimed to construct a diagnosis model for the early of GLM abscess formation based on multiple inflammatory parameters.Methods: Based on the presence or absence of abscess formation on breast magnetic resonance imaging (MRI), 126 patients with GLM were categorised into an abscess group (85 patients) and a non-abscess group (41 patients). Demographic characteristics and the related laboratory results for the 9 inflammatory markers were collected. Logistics univariate analysis and collinearity test were used for selecting independent variables. A regression model to predict abscess formation was constructed using Logistics multivariate analysis.Results: The univariate and multivariate analysis showed that the N, ESR, IL-4, IL-10 and INF-α were independent diagnostic factors of abscess formation in GLM (P< 0. 05). The nomogram was drawn on the basis of the logistics regression model. The area under the curve (AUC) of the model was 0.890, which was significantly better than that of a single indicator and the sensitivity and specificity of the model were high (81.2% and 85.40%, respectively). These results predicted by the model were highly consistent with the actual diagnostic results. The results of this calibration curve indicated that the model had a good value and stability in predicting abscess formation in GLM. The decision curve analysis (DCA) demonstrated a satisfactory positive net benefit of the model.Conclusion: A predictive model for abscess formation in GLM based on inflammatory markers was constructed in our study, which may provide a new strategy for early diagnosis and treatment of the abscess stage of GLM.Keywords: granulomatous lobular mastitis, abscess formation, risk factors, inflammation, ROC curve, diagnostic model |
| first_indexed | 2024-03-08T08:42:20Z |
| format | Article |
| id | doaj.art-6a4b52ff9344419cafe1aa7b3f84ce01 |
| institution | Directory Open Access Journal |
| issn | 1178-7031 |
| language | English |
| last_indexed | 2024-03-08T08:42:20Z |
| publishDate | 2024-02-01 |
| publisher | Dove Medical Press |
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| series | Journal of Inflammation Research |
| spelling | doaj.art-6a4b52ff9344419cafe1aa7b3f84ce012024-02-01T17:59:28ZengDove Medical PressJournal of Inflammation Research1178-70312024-02-01Volume 1755356490110Construction of a Multi-Indicator Model for Abscess Prediction in Granulomatous Lobular Mastitis Using Inflammatory IndicatorsDu NNFeng JMShao SJWan HWu XQNan-Nan Du, Jia-Mei Feng, Shi-Jun Shao, Hua Wan, Xue-Qing Wu Breast Department, Shuguang Hospital Affiliated to Shanghai University of Traditional Chinese Medicine, Shanghai, 200021, People’s Republic of ChinaCorrespondence: Xue-Qing Wu; Hua Wan, Breast Department, Shuguang Hospital Affiliated to Shanghai University of Traditional Chinese Medicine, Shanghai, 200021, People’s Republic of China, Tel +86 13817792022 ; +86 13611666266, Email snow_zi@hotmail.com; drwanhua@163.comBackground: Granulomatous lobular mastitis (GLM) is a chronic inflammatory breast disease, and abscess formation is a common complication of GLM. The process of abscess formation is accompanied by changes in multiple inflammatory markers. The present study aimed to construct a diagnosis model for the early of GLM abscess formation based on multiple inflammatory parameters.Methods: Based on the presence or absence of abscess formation on breast magnetic resonance imaging (MRI), 126 patients with GLM were categorised into an abscess group (85 patients) and a non-abscess group (41 patients). Demographic characteristics and the related laboratory results for the 9 inflammatory markers were collected. Logistics univariate analysis and collinearity test were used for selecting independent variables. A regression model to predict abscess formation was constructed using Logistics multivariate analysis.Results: The univariate and multivariate analysis showed that the N, ESR, IL-4, IL-10 and INF-α were independent diagnostic factors of abscess formation in GLM (P< 0. 05). The nomogram was drawn on the basis of the logistics regression model. The area under the curve (AUC) of the model was 0.890, which was significantly better than that of a single indicator and the sensitivity and specificity of the model were high (81.2% and 85.40%, respectively). These results predicted by the model were highly consistent with the actual diagnostic results. The results of this calibration curve indicated that the model had a good value and stability in predicting abscess formation in GLM. The decision curve analysis (DCA) demonstrated a satisfactory positive net benefit of the model.Conclusion: A predictive model for abscess formation in GLM based on inflammatory markers was constructed in our study, which may provide a new strategy for early diagnosis and treatment of the abscess stage of GLM.Keywords: granulomatous lobular mastitis, abscess formation, risk factors, inflammation, ROC curve, diagnostic modelhttps://www.dovepress.com/construction-of-a-multi-indicator-model-for-abscess-prediction-in-gran-peer-reviewed-fulltext-article-JIRgranulomatous lobular mastitisabscess formationrisk factorsinflammationroc curvediagnostic model |
| spellingShingle | Du NN Feng JM Shao SJ Wan H Wu XQ Construction of a Multi-Indicator Model for Abscess Prediction in Granulomatous Lobular Mastitis Using Inflammatory Indicators Journal of Inflammation Research granulomatous lobular mastitis abscess formation risk factors inflammation roc curve diagnostic model |
| title | Construction of a Multi-Indicator Model for Abscess Prediction in Granulomatous Lobular Mastitis Using Inflammatory Indicators |
| title_full | Construction of a Multi-Indicator Model for Abscess Prediction in Granulomatous Lobular Mastitis Using Inflammatory Indicators |
| title_fullStr | Construction of a Multi-Indicator Model for Abscess Prediction in Granulomatous Lobular Mastitis Using Inflammatory Indicators |
| title_full_unstemmed | Construction of a Multi-Indicator Model for Abscess Prediction in Granulomatous Lobular Mastitis Using Inflammatory Indicators |
| title_short | Construction of a Multi-Indicator Model for Abscess Prediction in Granulomatous Lobular Mastitis Using Inflammatory Indicators |
| title_sort | construction of a multi indicator model for abscess prediction in granulomatous lobular mastitis using inflammatory indicators |
| topic | granulomatous lobular mastitis abscess formation risk factors inflammation roc curve diagnostic model |
| url | https://www.dovepress.com/construction-of-a-multi-indicator-model-for-abscess-prediction-in-gran-peer-reviewed-fulltext-article-JIR |
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