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Publications

Journal Article Epublish

PubMed: 40757203 DOI: 10.3389/fmed.2025.1589406

Development and validation of a prediction model based on a nomogram for tuberculous pleural effusion.

Front Med (Lausanne) 12:1589406

Authors: Suli Liu, Yao Yang, Dongmei Wang, Lijuan Gao, Jiangyue Qin, Yanqiu Wu, Diandian Li, Xiaohua Li, Mei Chen, Hao Wang, Yongchun Shen, Fuqiang Wen, Fangying Chen

Affiliations

  • Division of Pulmonary Diseases, State Key Laboratory of Biotherapy of China, Department of Respiratory and Critical Care Medicine, West China Hospital, Sichuan University, Chengdu, China. State Key Laboratory of Respiratory Health and Multimorbidity, West China Hospital, Sichuan University, Chengdu, China.
  • Department of Medical Ultrasound, West China Hospital, Sichuan University, Chengdu, China.
  • Department of Respiratory and Critical Care Medicine, Sixth People's Hospital of Chengdu, Chengdu, China.
  • Key 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, China.
  • Division of Pulmonary Diseases, State Key Laboratory of Biotherapy of China, Department of Respiratory and Critical Care Medicine, West China Hospital, Sichuan University, Chengdu, China.
  • State Key Laboratory of Respiratory Health and Multimorbidity, West China Hospital, Sichuan University, Chengdu, China.
  • Department of Tuberculosis, The Third People's Hospital of Tibet Autonomous Region, Lhasa, China.

Abstract

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.

MeSH

Adenosine Deaminase, Interferon-Gamma, Research Design, Pleural Effusion, Nomograms, Adenosine, Tuberculosis, Diagnosis