Observational studyPloS one2021
Diagnostic accuracy of adenosine deaminase for pleural tuberculosis in a low prevalence setting: A machine learning approach within a 7-year prospective multi-center study.
Observational study in PloS one, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed, 18 citations in OpenAlex.
- Biochemical Markers in Pleural Fluid for the Diagnosis of Tuberculous Pleural Effusion: A Retrospective Study.Diseases (Basel, Switzerland) · 2026Article
- Ten Recommendations for Modelling Cost Effectiveness of Screening: Perspectives of an International Stakeholder Group.PharmacoEconomics · 2026Review
- Biochemistry-based machine learning algorithms in differentiating pleural effusion: current status and perspective.ERJ open research · 2026Review
- Transthoracic ultrasound features to differentiate tuberculous from malignant pleural effusion: development of a machine learning-based clinical prediction model.Journal of thoracic disease · 2026Article
- Development and validation of a simplified machine learning model based on T-SPOT.TB and routine clinical data for the diagnosis of tuberculous pleural effusion.Journal of thoracic disease · 2026Article
- Machine learning algorithms for identifying tuberculous pericardial effusion: insights from a retrospective study in Vietnam.BMC medical informatics and decision making · 2026Article
- Artificial Intelligence in Pleural Diseases: Current Applications and Next Steps.Thoracic research and practice · 2026Article
- Transformer-based ensemble framework for tuberculosis treatment response prediction: multi-omics integration with clinical-grade performance.Frontiers in molecular biosciences · 2026Article
- Development and external validation of machine-learning models for diagnosing malignant pleural effusion.Frontiers in oncology · 2026Article
- Machine learning-based Diagnostic model for determining the etiology of pleural effusion using Age, ADA and LDH.Respiratory research · 2025Article
- Article
- Biological and cytological-morphological assessment of tuberculous pleural effusions.Romanian journal of morphology and embryology = Revue roumaine de morphologie et embryologieArticle
Corrections and comments
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Authors and funding
16 authors at 8 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveTo analyze the performance of adenosine deaminase in pleural fluid combined with other parameters routinely measured in clinical practice and assisted by machine learning algorithms for the diagnosis of pleural tuberculosis in a low prevalence setting, and secondly, to identify effusions that are non-tuberculous and most likely malignant. PATIENTS AND
methodsWe prospectively analyzed 230 consecutive patients diagnosed with lymphocytic exudative pleural effusion from March 2013 to June 2020. Diagnosis according to the composite reference standard was achieved in all cases. Pre-test probability of pleural tuberculosis was 3.8% throughout the study period. Parameters included were: levels of adenosine deaminase, pH, glucose, proteins, and lactate dehydrogenase, red and white cell counts and lymphocyte percentage in pleural fluid, as well as age. We tested six different machine learning-based classifiers to categorize the patients. Two different classifications were performed: a) tuberculous/non-tuberculous and b) tuberculous/malignant/other.
resultsOut of a total of 230 patients with pleural effusion included in the study, 124 were diagnosed with malignant effusion and 44 with pleural tuberculosis, while 62 were given other diagnoses. In the tuberculous/non-tuberculous classification, and taking into account the validation predictions, the support vector machine yielded the best result: an AUC of 0.98, accuracy of 97%, sensitivity of 91%, and specificity of 98%, whilst in the tuberculous/malignant/other classification, this type of classifier yielded an overall accuracy of 80%. With this three-class classifier, the same sensitivity and specificity was achieved in the tuberculous/other classification, but it also allowed the correct classification of 90% of malignant cases.
conclusionThe level of adenosine deaminase in pleural fluid together with cell count, other routine biochemical parameters and age, combined with a machine-learning approach, is suitable for the diagnosis of pleural tuberculosis in a low prevalence scenario. Secondly, non-tuberculous effusions that are suspected to be malignant may also be identified with adequate accuracy.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.