Abstract
To 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. We 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. Out 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. The 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.
Highlights
Tuberculosis remains a major global public health problem: in 2019, 10 million people developed tuberculosis and it caused 1.5 million deaths, with around 5.4% of the cases diagnosed in the World Health Organization (WHO) European region and WHO region of the Americas [1]
Out 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 Area under the curve (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%
Summary
Tuberculosis remains a major global public health problem: in 2019, 10 million people developed tuberculosis and it caused 1.5 million deaths, with around 5.4% of the cases diagnosed in the World Health Organization (WHO) European region and WHO region of the Americas [1]. Extrapulmonary disease is the initial presentation in about 25% of patients, involving lymph nodes and primarily the pleura [2]; as a result, tuberculosis is currently one of the most frequent causes of exudative pleural effusion worldwide [3]. Tuberculous pleural effusion (TPE) is a paucibacillary manifestation of tuberculosis, and bacteriological tests in pleural fluid have historically given suboptimal results, leading to a search for biomarkers in pleural fluid and the use of aggressive diagnostic techniques like pleural biopsy [4]. Adenosine deaminase (ADA) has proven to be the most useful and cost-effective biomarker in pleural fluid, but based on the Bayesian interpretation of its diagnostic accuracy, it is currently only accepted as a rule-out test in low prevalence scenarios. We decided to conduct a multicenter prospective study with diagnosis according to the composite reference standard in all patients included, in a low prevalence scenario and with 10-fold larger population
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
