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Войти в исследования Respiratory medicine · 2025
Clinical predictive model for identifying high-risk factors in pleurisy tuberculoma patients.
СтатьяПоддержка исследования: вне правительства СШАНазвание и аннотация: Перевод в очереди
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Gao Weiwei, Yang Chen, Wang Tianzhen, Guo Yicheng, Dai Guangchuan, Hu Weiyi, Chen Shanshan, Tang Xiaoli, Yin Chunyang, Chen Cheng, Zeng Yi
Аннотация
INTRODUCTION: Pleurisy tuberculoma (PTM) is a neoplastic lesion that primarily affects the pleural wall or internal organs. The majority of PTM cases are observed during the treatment of tuberculous pleural effusion(TPE), and although the precise pathogenesis remains unclear, there is a significant association between these two conditions. To identify high-risk factors for the development of PTM, we developed a clinical predictive model aimed at providing more insightful information for the development of PTM.
METHODS: A retrospective study was conducted on patients diagnosed with PTM or TPE who were treated at Nanjing Chest Hospital and the Second Hospital of Nanjing from March 2013 to April 2024. Predictors were identified using logistic regression, LASSO regression, and optimal subset regression. The performance of all models was evaluated using receiver operating characteristic (ROC) curves, calibration curves, decision curve analysis (DCA), and clinical impact curves to establish the final clinical predictive model. Internal and external validation was performed to assess the model's performance.
RESULTS: The final predictive model included two key risk factors associated with the development of PTM: Ki-67+CD4+T cells and Ki-67+CD8+T cells. The final model demonstrated good clinical net benefit and predictive validity, high predictive accuracy (Brier score: 0.033, 95 %CI: 0.011-0.054), and strong differentiation (AUC: 0.987, 95 %CI: 0.969-1.000). The model's robust performance was demonstrated by both internal and external verification.
CONCLUSIONS: The predictive model utilizing Ki-67+CD4+T cells and Ki-67+CD8+T cells can assist clinicians in making early predictions of PTM.
METHODS: A retrospective study was conducted on patients diagnosed with PTM or TPE who were treated at Nanjing Chest Hospital and the Second Hospital of Nanjing from March 2013 to April 2024. Predictors were identified using logistic regression, LASSO regression, and optimal subset regression. The performance of all models was evaluated using receiver operating characteristic (ROC) curves, calibration curves, decision curve analysis (DCA), and clinical impact curves to establish the final clinical predictive model. Internal and external validation was performed to assess the model's performance.
RESULTS: The final predictive model included two key risk factors associated with the development of PTM: Ki-67+CD4+T cells and Ki-67+CD8+T cells. The final model demonstrated good clinical net benefit and predictive validity, high predictive accuracy (Brier score: 0.033, 95 %CI: 0.011-0.054), and strong differentiation (AUC: 0.987, 95 %CI: 0.969-1.000). The model's robust performance was demonstrated by both internal and external verification.
CONCLUSIONS: The predictive model utilizing Ki-67+CD4+T cells and Ki-67+CD8+T cells can assist clinicians in making early predictions of PTM.