Development and validation of a prediction model based on a nomogram for tuberculous pleural effusion
BackgroundDiagnosing tuberculous pleural effusion (TPE) is challenging. There is a lack of cross-sectional lateral comparisons among TPE prediction models.ObjectivesWe aimed to develop and validate a novel TPE prediction model and compare its diagnostic performance with that of existing models.Metho...
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Frontiers Media S.A.
2025-07-01
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| Series: | Frontiers in Medicine |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fmed.2025.1589406/full |
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| author | Suli Liu Suli Liu Yao Yang Yao Yang Dongmei Wang Lijuan Gao Lijuan Gao Jiangyue Qin Jiangyue Qin Yanqiu Wu Yanqiu Wu Diandian Li Diandian Li Xiaohua Li Mei Chen Hao Wang Hao Wang Yongchun Shen Yongchun Shen Fuqiang Wen Fuqiang Wen Fangying Chen |
| author_facet | Suli Liu Suli Liu Yao Yang Yao Yang Dongmei Wang Lijuan Gao Lijuan Gao Jiangyue Qin Jiangyue Qin Yanqiu Wu Yanqiu Wu Diandian Li Diandian Li Xiaohua Li Mei Chen Hao Wang Hao Wang Yongchun Shen Yongchun Shen Fuqiang Wen Fuqiang Wen Fangying Chen |
| author_sort | Suli Liu |
| collection | DOAJ |
| description | BackgroundDiagnosing tuberculous pleural effusion (TPE) is challenging. There is a lack of cross-sectional lateral comparisons among TPE prediction models.ObjectivesWe aimed to develop and validate a novel TPE prediction model and compare its diagnostic performance with that of existing models.MethodsPatients 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.ResultsFever, 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.ConclusionCompared with the existing models, the TPE prediction model developed in this study demonstrated good diagnostic performance. |
| format | Article |
| id | doaj-art-a3d5efde743943ed9a9de2be0fe18404 |
| institution | Kabale University |
| issn | 2296-858X |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Medicine |
| spelling | doaj-art-a3d5efde743943ed9a9de2be0fe184042025-08-20T03:27:27ZengFrontiers Media S.A.Frontiers in Medicine2296-858X2025-07-011210.3389/fmed.2025.15894061589406Development and validation of a prediction model based on a nomogram for tuberculous pleural effusionSuli Liu0Suli Liu1Yao Yang2Yao Yang3Dongmei Wang4Lijuan Gao5Lijuan Gao6Jiangyue Qin7Jiangyue Qin8Yanqiu Wu9Yanqiu Wu10Diandian Li11Diandian Li12Xiaohua Li13Mei Chen14Hao Wang15Hao Wang16Yongchun Shen17Yongchun Shen18Fuqiang Wen19Fuqiang Wen20Fangying Chen21Division of Pulmonary Diseases, State Key Laboratory of Biotherapy of China, Department of Respiratory and Critical Care Medicine, West China Hospital, Sichuan University, Chengdu, ChinaState Key Laboratory of Respiratory Health and Multimorbidity, West China Hospital, Sichuan University, Chengdu, ChinaDivision of Pulmonary Diseases, State Key Laboratory of Biotherapy of China, Department of Respiratory and Critical Care Medicine, West China Hospital, Sichuan University, Chengdu, ChinaState Key Laboratory of Respiratory Health and Multimorbidity, West China Hospital, Sichuan University, Chengdu, ChinaDepartment of Medical Ultrasound, West China Hospital, Sichuan University, Chengdu, ChinaDivision of Pulmonary Diseases, State Key Laboratory of Biotherapy of China, Department of Respiratory and Critical Care Medicine, West China Hospital, Sichuan University, Chengdu, ChinaState Key Laboratory of Respiratory Health and Multimorbidity, West China Hospital, Sichuan University, Chengdu, ChinaDivision of Pulmonary Diseases, State Key Laboratory of Biotherapy of China, Department of Respiratory and Critical Care Medicine, West China Hospital, Sichuan University, Chengdu, ChinaState Key Laboratory of Respiratory Health and Multimorbidity, West China Hospital, Sichuan University, Chengdu, ChinaDivision of Pulmonary Diseases, State Key Laboratory of Biotherapy of China, Department of Respiratory and Critical Care Medicine, West China Hospital, Sichuan University, Chengdu, ChinaState Key Laboratory of Respiratory Health and Multimorbidity, West China Hospital, Sichuan University, Chengdu, ChinaDivision of Pulmonary Diseases, State Key Laboratory of Biotherapy of China, Department of Respiratory and Critical Care Medicine, West China Hospital, Sichuan University, Chengdu, ChinaState Key Laboratory of Respiratory Health and Multimorbidity, West China Hospital, Sichuan University, Chengdu, ChinaDepartment of Respiratory and Critical Care Medicine, Sixth People’s Hospital of Chengdu, Chengdu, ChinaKey Laboratory of Acupuncture for Senile Disease (Chengdu University of TCM), School of Medical and Life Sciences, Chengdu University of Traditional Chinese Medicine, Ministry of Education, Chengdu, ChinaDivision of Pulmonary Diseases, State Key Laboratory of Biotherapy of China, Department of Respiratory and Critical Care Medicine, West China Hospital, Sichuan University, Chengdu, ChinaState Key Laboratory of Respiratory Health and Multimorbidity, West China Hospital, Sichuan University, Chengdu, ChinaDivision of Pulmonary Diseases, State Key Laboratory of Biotherapy of China, Department of Respiratory and Critical Care Medicine, West China Hospital, Sichuan University, Chengdu, ChinaState Key Laboratory of Respiratory Health and Multimorbidity, West China Hospital, Sichuan University, Chengdu, ChinaDivision of Pulmonary Diseases, State Key Laboratory of Biotherapy of China, Department of Respiratory and Critical Care Medicine, West China Hospital, Sichuan University, Chengdu, ChinaState Key Laboratory of Respiratory Health and Multimorbidity, West China Hospital, Sichuan University, Chengdu, ChinaDepartment of Tuberculosis, The Third People’s Hospital of Tibet Autonomous Region, Lhasa, ChinaBackgroundDiagnosing tuberculous pleural effusion (TPE) is challenging. There is a lack of cross-sectional lateral comparisons among TPE prediction models.ObjectivesWe aimed to develop and validate a novel TPE prediction model and compare its diagnostic performance with that of existing models.MethodsPatients 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.ResultsFever, 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.ConclusionCompared with the existing models, the TPE prediction model developed in this study demonstrated good diagnostic performance.https://www.frontiersin.org/articles/10.3389/fmed.2025.1589406/fulltuberculosistuberculous pleural effusionclinical prediction modeldiagnosisnomogram |
| spellingShingle | Suli Liu Suli Liu Yao Yang Yao Yang Dongmei Wang Lijuan Gao Lijuan Gao Jiangyue Qin Jiangyue Qin Yanqiu Wu Yanqiu Wu Diandian Li Diandian Li Xiaohua Li Mei Chen Hao Wang Hao Wang Yongchun Shen Yongchun Shen Fuqiang Wen Fuqiang Wen Fangying Chen Development and validation of a prediction model based on a nomogram for tuberculous pleural effusion Frontiers in Medicine tuberculosis tuberculous pleural effusion clinical prediction model diagnosis nomogram |
| title | Development and validation of a prediction model based on a nomogram for tuberculous pleural effusion |
| title_full | Development and validation of a prediction model based on a nomogram for tuberculous pleural effusion |
| title_fullStr | Development and validation of a prediction model based on a nomogram for tuberculous pleural effusion |
| title_full_unstemmed | Development and validation of a prediction model based on a nomogram for tuberculous pleural effusion |
| title_short | Development and validation of a prediction model based on a nomogram for tuberculous pleural effusion |
| title_sort | development and validation of a prediction model based on a nomogram for tuberculous pleural effusion |
| topic | tuberculosis tuberculous pleural effusion clinical prediction model diagnosis nomogram |
| url | https://www.frontiersin.org/articles/10.3389/fmed.2025.1589406/full |
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