Frontiers in medicine · 2025
Development and validation of a prediction model based on a nomogram for tuberculous pleural effusion.
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Liu Suli, Yang Yao, Wang Dongmei, Gao Lijuan, Qin Jiangyue, Wu Yanqiu, Li Diandian, Li Xiaohua, Chen Mei, Wang Hao, Shen Yongchun, Wen Fuqiang, Chen Fangying
Аннотация
BACKGROUND: Diagnosing tuberculous pleural effusion (TPE) is challenging. There is a lack of cross-sectional lateral comparisons among TPE prediction models.
OBJECTIVES: We aimed to develop and validate a novel TPE prediction model and compare its diagnostic performance with that of existing models.
METHODS: Patients with pleural effusion were included in the training, testing, and external validation sets. Variable selection strategies included LASSO and logistic regression. The discriminability, calibration, and clinical efficacy of the prediction model were estimated in the three sets. The performance of the model was compared with that of two existing prediction models.
RESULTS: Fever, tuberculosis interferon-gamma release assays, pleural adenosine deaminase, the pleural mononuclear cell ratio, the ratio of pleural lactate dehydrogenase to pleural adenosine deaminase, pleural carcinoembryonic antigen, and pleural cytokeratin 19 fragment were selected to establish the prediction model. The AUCs were 0.931 (0.903-0.958), 0.856 (0.753-0.959), and 0.925 (0.867-0.984) in the training, testing, and external validation sets, respectively. The AUCs of the two existing prediction models were 0.793 (0.737-0.850) and 0.854 (0.816-0.892). The calibration curves revealed that this model had good consistency. Decision curve analysis revealed the acceptable clinical benefit of this model.
CONCLUSION: Compared with the existing models, the TPE prediction model developed in this study demonstrated good diagnostic performance.
OBJECTIVES: We aimed to develop and validate a novel TPE prediction model and compare its diagnostic performance with that of existing models.
METHODS: Patients with pleural effusion were included in the training, testing, and external validation sets. Variable selection strategies included LASSO and logistic regression. The discriminability, calibration, and clinical efficacy of the prediction model were estimated in the three sets. The performance of the model was compared with that of two existing prediction models.
RESULTS: Fever, tuberculosis interferon-gamma release assays, pleural adenosine deaminase, the pleural mononuclear cell ratio, the ratio of pleural lactate dehydrogenase to pleural adenosine deaminase, pleural carcinoembryonic antigen, and pleural cytokeratin 19 fragment were selected to establish the prediction model. The AUCs were 0.931 (0.903-0.958), 0.856 (0.753-0.959), and 0.925 (0.867-0.984) in the training, testing, and external validation sets, respectively. The AUCs of the two existing prediction models were 0.793 (0.737-0.850) and 0.854 (0.816-0.892). The calibration curves revealed that this model had good consistency. Decision curve analysis revealed the acceptable clinical benefit of this model.
CONCLUSION: Compared with the existing models, the TPE prediction model developed in this study demonstrated good diagnostic performance.