Early diagnosis and prompt therapy for patients with TPE is critical to prevent severe complications (pleural thickening, empyema, and calcification, etc.) and mortality. Despite the availability of various diagnostic methods, the early differential diagnosis of TPE from MPE and other non-TB BPE remains to be challenging in clinical practice. Besides, paucibacillary nature of the disease, inappropriate and inadequate test samples, ineffective conventional microbiological techniques, lack of thoracoscopy equipment all lead to the difficulty for diagnosing TPE.
Conventional histopathologic presence of M. tuberculosis on culture, or pleural pathology showing caseating granuloma is the gold standard for diagnosing TPE, however, the diagnostic tests were time consuming and low positive rate [8, 11]. Tuberculin skin test (TST) and interferon-gamma release assays (IGRAs) were two common detection methods for diagnosing TPE, but the limitations of inaccuracy, inconsistent sensitivity, and time to diagnosis have retained its efficacies [11, 15, 16]. Under the circumstances, thoracoscopy seemed to provide a higher sensitivity (93%-100%) and accuracy for diagnosing TPE, however, it was an invasive and expensive diagnostic methods with a reported 2%-6% rate of complications [8, 17, 18]. The common complications were bleeding, fever, empyema, pneumonia, and prolonged air leak and so on . Besides, several patients with underlying disease progression and elderly patients cannot tolerate the examination.
In recently years, the Xpert MTB/RIF (Xpert) and/or next-generation Xpert MTB/RIF Ultra (Xpert Ultra), two nucleic acid detection methods, have been increasingly used to diagnose pulmonary TB, rifampicin (RIF) resistance as well as extra-pulmonary TB in various types of clinical specimens endorsed by World Health Organization (WHO) [19, 20]. A meta-analysis indicated that the pooled sensitivity of Xpert in diagnosing TPE was only 51.4% . The low sensitivity has compromised its diagnostic capacity for TPE, which might be attributed to the number of mycobacteria and performance of amplification techniques. Therefore, an effective and noninvasive diagnostic method is urgently needed for diagnosing and management of TPE.
Nomograms are a graphical representation of a complex mathematical formula, which are widely used to estimate diagnosis and prognosis for a variety of diseases by integrating clinical, biologic, and/or genetic variables in medicine . Previously, we and other investigators had reported the application of nomogram in differentiating MPE from BPE [23, 24]. In the present study, we developed a scoring system based on a nomogram to distinguish TPE from non-TB BPE. We initially integrated 25 variables, including not only primary clinical and laboratory variables but calculated ratios. We selected six most significant variables (age, effusion lymphocyte, effusion ADA, effusion LDH, effusion LDH/ADA, and serum WBC) analyzed by multivariate regression analysis to construct a predictive model. Our model showed a good diagnostic performance in distinguishing TPE from non-TB BPE in the derivation and validation sets. The integrated six commonly indexes were inexpensive, routinely tested, and readily available in most hospitals, therefore, our model is convenient to apply in clinical practice.
Effusion ADA has long been used to diagnose TPE in numerous studies [11, 15]. Michot et al. indicated that effusion ADA at an optimal value of 41.5 U/L might be a useful biomarker to differentiate TPE from non-TPE with a sensitivity and specificity were with a sensitivity of 97.1% and a specificity of 92.9% . A study conducted by Garcia-Zamalloa et al. showed a similar cutoff value of effusion ADA with 40U/L . However, a recent study from China showed that best cutoff value of effusion ADA for TBP was 27U/L with a sensitivity of 81% and a specificity of 78% . A similar cutoff value of effusion ADA was also found in our study (22.75 U/L). Therefore, the optimal cutoff values are still controversial due to the prevalence rates of the disease, sample sizes, different test methods, or HIV co-infection . Besides, a similar or even higher level of effusion ADA has been reported in PPE, especially in patients with empyema [28, 29]. Effusion LDH was recommended to assist in the classification of patients with complicated parapneumonic effusion (CPPE). However, an elevated effusion LDH in TPE, PPE, and MPE and the low sensitivity and specificity of LDH in differentiating TPE from PPE limited its utility in clinical practice .
The effusion LDH/ADA ratio was also assessed in differentiating TPE from PPE. Wang et al. indicated that effusion LDH/ADA ratio might be a useful biomarker in diagnosing TPE at a cut-off level of 16.20, with a sensitivity of 93.62% and a specificity of 93.06% . Another study from New Zealand also showed that effusion LDH/ADA ratio at a cutoff value of 15 demonstrated a high sensitivity and specificity in distinguishing TPE from non-TB effusion . However, our study showed a cutoff value of 19.46 for effusion LDH/ADA. Further prospective investigations were needed to validate the results in the future.
To our knowledge, this was the first study to evaluate a scoring system based on a nomogram in distinguishing TPE from non-TB BPE. The developed scoring system might be reliable and accuracy in distinguishing TPE from non-TB BPE, which was assessed by the indexes of sensitivity, specificity, PLR, NLR, PPV, and NPV in the training and validation sets. Our study incorporated the most common and valuable variables in clinical practice to differentiating TPE from non-TB BPE, which was better than any single variable alone. The six easily accessible and inexpensive variables routinely tested and acquired in most hospitals.
Our study had some limitations. First, the present study was retrospective design. Only routine biomarkers in serum and pleural effusion were included in the study. Several newly potential biomarkers, such as interleukin 27 (IL-27) and tumor necrosis factor-α (TNF-α), might provide better diagnostic accuracy. Second, external validation was a single-center with a small sample size. Third, our nomogram didn’t incorporated imaging data into the scoring system, which might be useful. Besides, we also didn’t compare the diagnostic accuracy of our scoring system and other diagnostic tests for unavailable data, such as IGRAs and Xpert Ultra. Further multicentric and prospective investigations containing comprehensive data was needed to validate our results.