Identifying the cause of pleural effusion is challenging for pulmonologists. Imaging, biopsy, microbiology and biochemical analyses are routinely used for diagnosing pleural effusion. Among these diagnostic tools, biochemical analyses are promising because they have the advantages of low cost, minimal invasiveness, observer independence and short turn-around time. Here, we reviewed the past, present and future of pleural fluid biochemical analysis. We reviewed the history of Light’s criteria and its modifications and the current status of biomarkers for heart failure, malignant pleural effusion, tuberculosis pleural effusion and parapneumonic pleural effusion. In addition, we anticipate the future of pleural fluid biochemical analysis, including the utility of machine learning, molecular diagnosis and high-throughput technologies. Clinical Chemistry and Laboratory Medicine (CCLM) should address the topic of pleural fluid biochemical analysis in the future to promote specific knowledge in the laboratory professional community.
Pleural effusion is a common sign that is associated with various disorders. It can cause symptoms such as cough, dyspnea and chest pain. Because these symptoms are not specific to a given disease, the differential diagnosis of pleural effusion is challenging for clinicians. The causes of pleural effusion vary across different countries and regions. Pneumonia, cancer, tuberculosis and heart failure (HF) are four frequent causes of pleural effusion [1, 2]. The first step in pleural effusion management is identifying its cause. Currently, several diagnostic tools are available for differentiating pleural effusion, including pleural fluid cytology, Ziehl–Neelsen staining and bacterial culture, biochemical analyses and biopsy. However, these tools have limitations. For example, pleural fluid cytology has high specificity for malignant pleural effusion (MPE), but its sensitivity is only 46% . Pleural fluid culture is the gold standard for parapneumonic pleural effusion (PPE) but has low sensitivity and a long turn-around time. Pleural biopsy guided by imaging (e.g., CT or ultrasound) or thoracoscopy has a high diagnostic yield for pleural effusion. Nevertheless, it is an invasive tool, and operation-related complications are problematic . In addition, special training and equipment are needed for biopsy, limiting its application in remote areas.
Pleural fluid biochemical analyses are promising diagnostic tools for pleural effusion because they have the advantages of low cost, short turn-around time, and objectivity. Some review articles have been published to summarize the diagnostic and prognostic value of pleural fluid biomarkers for specific etiologies, such as MPE , , , PPE , tuberculosis pleural effusion (TPE) [9, 10] and HF , including two reviews from our team [5, 10]. However, reviews on the history, current status and future of pleural fluid biochemical analyses are rare. Here, we performed a review to summarize the history of pleural fluid biochemical analyses. We also reviewed the current status and anticipated the future of pleural fluid biochemical analysis.
Pleural effusion can be categorized into exudate and transudate based on the cause and underlying pathophysiology. Transudates arise from increased hydrostatic pressure or decreased oncotic pressure . In a few cases, it can also be caused by the passage of ascitic fluid from the peritoneal cavity to the pleural surface via transdiaphragmatic lymphatics (hepatic hydrothorax) or low pressure in the pleural cavity (atelectasis) . In contrast, exudates develop due to inflammation in the pleural cavity. Inflammation can be caused by metastatic pleural tumors or infectious pathogens (e.g., Mycobacterium tuberculosis (Mtb) and Streptococcus pneumoniae) . Inflammation increases capillary permeability and allows serum proteins to enter the pleural cavity. The management of a transudate requires clinicians to treat the underlying condition with specific therapies (e.g., diuretics), and further investigations are unnecessary. In contrast, additional examinations and even invasive procedures are needed to elucidate the etiology of an exudate . Therefore, identification of the exudative or transudative nature of the pleural fluid is the initial step in the diagnostic work-up of pleural effusion . Notably, the appearance of pleural fluid does not help differentiate pleural effusion and thus should not be overemphasized . Biochemical analyses of pleural fluid are of great value for differentiating between exudates and transudates.
History of Light’s criteria
The earliest studies revealed that pleural fluid protein [17, 18], lactate dehydrogenase (LDH) , and the pleural fluid to serum LDH ratio were useful markers for differentiating exudates and transudates. These findings promote the proposition of Light’s criteria in 1972 . According to Light’s criteria, pleural effusion should be categorized as an exudate if it meets one or more of the following items: (i) A pleural fluid to serum protein ratio >0.5; (ii) A pleural fluid to serum LDH >0.6; (iii) A pleural fluid LDH activity >2/3 the upper limit of serum LDH’s reference interval. The original aim of Light’s criteria was to maximize the identification of exudates; thus, the items are combined in a parallel “or” rule. Light’s criteria have high diagnostic sensitivity (99%) and specificity (98%) for an exudate . However, subsequent studies did not obtain such a high diagnostic accuracy , , . All these studies revealed that the sensitivity of Light’s criteria is near 100%, but its specificity is approximately 70% . Light’s criteria are more accurate than clinical judgment for differentiating pleural transudates and exudates (84% vs. 93%) . Notably, more than 50% of the misclassified transudates only met one item of Light’s criteria, and the values of LDH and protein were near the established threshold . In patients who meet both a pleural fluid-to-serum total protein ratio >0.5 and LDH >2/3 of its reference interval, the presence of an exudate effusion is conclusive . Inadequate specificity is partially caused by diuretics [27, 28]. Under such conditions, an albumin gradient >12 g/L or a protein gradient >31 g/L is recommended [12, 25, 28]. Pleural fluid N-terminal pro-brain natriuretic peptide (NT-proBNP) >1,500 pg/mL is also an alternative tool with high accuracy in misclassified cardiac effusions , , , . Notably, the sensitivity of an albumin gradient >12 g/L for identifying an exudate is only 67% , indicating that 33% of the exudates will be misidentified as transudates. Therefore, the albumin gradient should be used only in patients with marginal exudative effusions with suspected HF .
Modified Light’s criteria
In addition to LDH and protein in pleural fluid and serum, some biomarkers have been proposed as alternative diagnostic tools, such as cholesterol , NT-proBNP , C-reactive protein (CRP) , bilirubin , cholinesterase , albumin and protein gradients . Among the reported markers, cholesterol is the most widely investigated. A meta-analysis revealed that it has a sensitivity of 88% and specificity of 96% , which is comparable to those of pleural fluid LDH, the serum-to-pleural fluid LDH ratio and the pleural fluid-to-serum protein ratio . Therefore, adding cholesterol is a potential modification of Light’s criteria.
Table 1 lists some of the modifications for Light’s criteria. Some modifications were made by adjusting the threshold of protein, LDH or their ratios [39, 40], while others introduced pleural fluid cholesterol into their criteria [41, 42]. Notably, pleural fluid LDH is highly correlated with the serum-to-pleural fluid LDH ratio , so it is reasonable to hypothesize that one of them can be moved from Light’s criteria. Two simplified Light’s criteria, which contain only pleural fluid cholesterol and LDH, have been proposed [41, 42]. These criteria have comparable, but not superior, diagnostic accuracy with Light’s criteria. Nevertheless, it should be noted that Light’s criteria are near perfect for discriminating between transudates and exudates. Although clinical diagnosis is the gold standard for defining transudates and exudates, it has a small but definite error rate. Although superior diagnostic criteria were theoretically possible, at least 13,000 subjects are needed to prove the superiority of any newly proposed criteria over Light’s criteria .
|Light’s criteria and its modifications||Criteria||Sensitivity||Specificity|
|Light’s criteria ||
|Romero’s criteria ||
|Costa’s criteria ||
|Lepine’s criteria ||
|Vives’ criteria ||
LDH, lactate dehydrogenase.
Perspective from laboratory medicine
Light’s criteria are undoubtedly the milestone in pleural fluid biochemical analyses. From the perspective of laboratory medicine, some issues should be strengthened. First, analytical platforms for LDH and protein analyses can affect the accuracy of Light’s criteria, and there is a 10% discrepancy among different platforms . The discrepancy increases to 18% in patients with a pleural fluid protein level between 25 and 35 g/L . Second, preanalytical errors should be considered . Pleural fluid protein and LDH are stable at room temperature for 6 h , but the long-term stability of LDH and protein remains unknown. Third, in Light’s work, the time interval between serum and pleural fluid specimen collection was within 30 min . However, it has been reported that the time interval between serum and pleural fluid specimen collection did not significantly affect the accuracy of Light’s criteria . Fourth, pleural erythrocyte count positively correlates with LDH activity, and the specificity of Light’s criteria decreased in patients with high pleural erythrocyte count [49, 50]. It is widely accepted that hemolysis can increase serum LDH . Therefore, it seems that increased LDH in pleural fluid specimens with high erythrocyte counts is associated with hemolysis. Indeed, a high prevalence of hemolysis can be observed in pleural fluid specimens . A formula proposed to correct LDH can increase the specificity of Light’s criteria . Fifth, although the biochemical analyzers used to measure pleural fluid LDH and protein have only validated their assays for serum or plasma, the recovery rates of LDH and protein are near 100%, indicating that there is no “matrix effect” for pleural fluid LDH and protein , , . In addition, the intra-assay and interassay precisions of pleural fluid LDH and protein are comparable to their serum partners .
The proposition of Light’s criteria is a landmark work in differentiating pleural effusion; however, additional procedures are needed to define the etiology of pleural effusion. As mentioned above, tuberculosis, HF, malignancy, and pneumonia are four primary causes of pleural effusion, accounting for 75% of pleural effusion [1, 2]. Many studies investigating the diagnostic role of pleural fluid biochemical analyses focus on these four causes. Here, we summarize the current status of pleural fluid biochemical analyses in these four disorders.
Biochemical analyses for HF
HF is the primary cause of transudates, accounting for 85% of the transudates [1, 2]. Nevertheless, Light’s criteria have low diagnostic accuracy for HF . Currently, circulating brain natriuretic peptide (BNP) and NT‐proBNP are two guideline-endorsed diagnostic biomarkers for HF . In patients with pleural effusion, both BNP and NT-proBNP, either in the blood or pleural fluid, have high diagnostic accuracy for HF-induced pleural effusion, also termed cardiac effusion . Evidence from systematic reviews and meta-analyses indicates that pleural fluid NT-proBNP has high diagnostic accuracy for HF in patients with undiagnosed pleural effusion, with both a sensitivity and a specificity higher than 90% , , . The diagnostic accuracy of pleural fluid BNP is slightly inferior to that of NT-proBNP, with a sensitivity of 92% and a specificity of 88% . The recommended threshold of pleural fluid NT-proBNP for HF is 1,500 ng/L . Notably, blood NT-proBNP is highly correlated with pleural fluid NT-proBNP, with a coefficient >0.95 . Therefore, both blood and pleural fluid NT-proBNP are useful diagnostic biomarkers for HF in undiagnosed pleural effusion, and their diagnostic accuracy is comparable. Because thoracocentesis can be avoided, blood NT-proBNP is more suitable than pleural fluid in patients who cannot tolerate thoracocentesis. The diagnostic accuracy of pleural fluid NT-proBNP is affected by age and estimated glomerular filtration rate (eGFR). A higher threshold should be adopted in patients with old age or decreased eGFR . The specificity of pleural fluid NT-proBNP for HF decreases because septic shock and acute kidney injury can elevate pleural fluid NT-proBNP. These two disorders are common in critical care settings . In cases when NT-proBNP is unavailable, a simple scoring system based on albumin gradient, age, pleural fluid LDH, bilateral effusion on CXR and protein gradient can assist clinicians in accurately identifying HF .
Serum mid-regional pro-atrial natriuretic peptide (MR-proANP) is a promising diagnostic marker for HF in patients admitted to the emergency department with dyspnea [66, 67]. Pleural fluid MR-proANP is also increased in pleural effusion patients with HF . Its diagnostic accuracy is comparable to that of pleural fluid NT-proBNP . The coefficient between MR-proANP and NT-proBNP is 0.79, indicating that combinational use of MR-proANP and NT-proBNP cannot improve the diagnostic yield for HF . The diagnostic accuracy of serum MR-proANP for HF patients with pleural effusion remains unknown.
Two studies revealed that serum soluble CD146 (sCD146) is a promising diagnostic marker for HF [68, 69]. Unlike NT-proBNP and MR-proANP, which are released by ventricular or atrial cardiomyocytes in response to stress, sCD146 is primarily released by vascular endothelial cells . The diagnostic accuracy of blood sCD146 and NT-proBNP is comparable . It remains unknown whether pleural fluid sCD146 is a promising diagnostic marker for HF. In addition, some other biomarkers have been proposed as diagnostic markers for HF in undiagnosed pleural effusion patients, such as ischemia-modified albumin [70, 71]. However, further studies are needed to validate the findings of the initial studies.
Biochemical analyses for MPE
Diagnosing MPE is a challenge for pulmonologists and laboratory clinicians. Numerous studies have investigated the diagnostic accuracy of serum or pleural fluid tumor markers for MPE, including neuron-specific enolase (NSE), carcinoembryonic antigen (CEA), carbohydrate antigen 125 (CA125), carbohydrate antigen 15-3 (CA15-3), carbohydrate antigen 19-9 (CA19-9), and a fragment of cytokeratin 19 (CYFRA 21-1) . Evidence from meta-analyses indicates that the specificities of these tumor markers are >90%, but their sensitivities are only approximately 50% , , . Notably, in diagnostic test accuracy studies, the sensitivity and specificity are threshold-dependent [75, 76], and the thresholds of tumor markers used in previous studies vary. Theoretically, higher sensitivity can be obtained by decreasing the threshold of the tumor marker, but the high sensitivity is at the expense of a lower specificity. To date, there is no uniform threshold used for pleural fluid tumor markers. However, an extremely high tumor marker value has 100% specificity for MPE. For example, CEA (>45 ng/mL) or CA 15-3 (>77 UI/l) can be used to confirm MPE because of their 100% specificities [56, 77].
Combinations of these tumor markers can slightly increase the diagnostic sensitivity, especially the combinations of CEA+CYFRA 21-1 and CA15-3+CYFRA 21-1 . A nomogram is a novel method to investigate the combination of these tumor markers and other biochemical analyses (e.g., erythrocyte sedimentation rate, LDH, ADA). Two previous studies have constructed nomograms to investigate the diagnostic accuracy of multiple tumor markers, and the AUCs of the nomograms in the studies were >0.90 [78, 79].
Serum tumor markers also increased in MPE patients, but their diagnostic accuracy was inferior to that of their pleural fluid partners , , , . The pleural fluid to serum ratios of tumor markers have been proposed to increase the diagnostic accuracy of MPE. Nevertheless, these ratios do not significantly increase the diagnostic accuracy of MPE , , , , , . With rigorous statistical methods such as net reclassification improvement (NRI) and integrated discrimination improvement (IDI) , we found that the CEA ratio did not provide added diagnostic value over pleural fluid CEA (our unpublished data). In addition to the pleural fluid to serum ratio, the tumor marker gradient has also been investigated in several studies. Nevertheless, their gradients do not show superior diagnostic accuracy over pleural fluid tumor markers . Therefore, the current evidence does not support determining serum and pleural fluid tumor markers simultaneously when pleural fluid tumor markers are available.
In addition to conventional tumor markers, some novel markers have been reported to be promising in diagnosing MPE, such as endostatin , vascular endothelial growth factor (VEGF) [88, 89], apolipoprotein E (Apo-E) , tumor-associated macrophages (TAMs) in pleural fluid , cancer ratio [92, 93] and cancer ratio plus [94, 95]. TAM (CD14+CD206+, CD14+CD163+) has exceptionally high diagnostic accuracy among these markers. However, TAM is determined by flow cytometry, which lacks standardization and thus limits its clinical implications. The cancer ratio is defined as the ratio of serum LDH to pleural fluid ADA and has high diagnostic accuracy for MPE (97% sensitivity and 89% specificity), as indicated by meta-analyses [92, 96]. The strength of the cancer ratio is low cost, easy to obtain, and well-standardized. However, our recent study indicated that the diagnostic accuracy of the cancer ratio decreased with age (unpublished data).
Biochemical analyses for TPE
TPE is one of the most common extrapulmonary tuberculosis forms in adults . The diagnosis of TPE is often challenging because the gold standards (e.g., Ziehl–Neelsen staining, pleural fluid Mtb culture, and biopsy) are time-consuming, invasive and have low sensitivity . The diagnostic value of many pleural fluid biomarkers for TPE has been investigated . Among the investigated biomarkers, adenosine deaminase (ADA) , interferon-gamma (IFN-γ) , and interleukin 27 (IL-27)  are the most promising.
ADA is an enzyme produced by many types of lymphocytes and is involved in the metabolism of purines. It has consistently demonstrated high accuracy for TPE since it was first reported in 1978 . Evidence from meta-analyses indicates that pleural fluid ADA has a sensitivity range between 86 and 93%, and the specificity varies between 88 and 93% [99, 103], , . The ADA threshold used in most published studies ranges between 35 U/L and 60 U/L [99, 103]. Some meta-analyses from specific countries (e.g., Spain , Brazil  and India ) showed that the diagnostic accuracy of ADA is similar across different regions. Notably, in areas with low tuberculosis prevalence, pleural fluid ADA ≥15 U/L has a sensitivity of 100% and a negative predictive value (NPV) of 100% . Extremely high pleural fluid ADA (>100 IU/L) is frequently observed in patients with empyema or lymphoma rather than TPE . The pleural fluid ADA level is negatively correlated with age [111, 112]. However, findings from studies with age stratification designs are not always consistent [111, 113, 114], and further studies are needed to address the effect of age on the diagnostic accuracy of ADA. In addition, pleural fluid ADA has no diagnostic value in pediatrics .
IFN-γ is a cytokine produced by activated CD4+ T helper cells in the pleural compartment and can increase the mycobactericidal activity of macrophages . Many studies have investigated the diagnostic value of pleural fluid IFN-γ for TPE since the first report, which was published in 1988 . Three meta-analyses summarized the diagnostic accuracy of pleural fluid IFN-γ for TPE [100, 104, 118]. All these meta-analyses indicated that the sensitivity and specificity of IFN-γ were >90%. Similar to ADA, the diagnostic accuracy of IFN-γ is also affected by age [113, 114].
The diagnostic value of pleural fluid IL-27 was first reported by Shi et al. in 2012 . To date, four meta-analyses have reported the diagnostic value of pleural fluid IL-27 for TPE [101, 120], , . The most recent and comprehensive study, which included eleven studies with 1,454 patients in the analysis, showed that pleural fluid IL-27 had a sensitivity of 95% and specificity of 91% . These results indicate that IL-27 has extremely high diagnostic accuracy for TPE. Although IL-27, IFN-γ and ADA have comparable and extremely high diagnostic accuracy for TPE, ADA is preferred because of its low cost. In addition, the ADA assay is well standardized, and the results from different laboratories are comparable. In contrast, IL-27 and IFN-γ were measured by enzyme-linked immunosorbent assays (ELISAs), which are expensive and lack standardization .
Notably, interferon-gamma release assays (IGRAs) have been proposed as a potential diagnostic tool for TPE. There are two types of IGRAs, named T-SPOT. TB (Oxford Immunotec) and QuantiFERON-TB Gold (QIAGEN). In both IGRAs, antigens from Mtb were used to stimulate lymphocytes from the patient’s blood or pleural fluid. IFN-γ in the culture media was determined by ELISA or enzyme-linked immunospot (ELISPOT) assay. The diagnostic accuracy of IGRAs for TPE is insufficient, as indicated by meta-analyses , , . According to the most recently published meta-analysis, the sensitivity and specificity of IGRA are 88 and 79%, respectively , which are obviously lower than those of ADA, IFN-γ and IL-27. In addition to its low diagnostic accuracy, other disadvantages, including high cost, long turn-around time, and labor consumption, limit its utility in diagnosing TPE.
Other biomarkers have been proposed as potential diagnostic markers for TPE, such as interleukin 32 (IL-32) , C1q , C-X-C motif chemokine receptor 3 (CXCR3) ligands (e.g., CXCL9, CXCL10, CXCL11) [129, 130] and soluble interleukin-2 receptor (sIL-2R) . The initial studies revealed that the diagnostic accuracy of these biomarkers is promising; however, further studies are needed to validate the findings reported in these studies. In addition, nucleic acid amplification tests (NAATs) are also promising diagnostic tools for TPE. Its specificity is close to 100%, but its sensitivity is only approximately 30% .
Biochemical analyses for PPE
PPE is a common complication associated with pneumonia . Approximately 18% of community-acquired pneumonia (CAP) patients will develop PPE during their disease courses . The in-hospital mortality rate of PPE is approximately 10% [134, 135]. There are three types of or progression phases of PPE: uncomplicated parapneumonic effusion (UPPE), complicated parapneumonic effusion (CPPE) and empyema [136, 137]. In UPPE, the pleural cavity is free of infection, and approximate antibiotic treatment can cure it . In CPPE and empyema, pathogens translocate from the lung to the pleural cavity, and drainage or surgery is needed because antibiotics alone are insufficient . Empyema is characterized by the presence of frank pus in the pleural cavity. Typically, CPPE is described as high LDH activity (>1000 U/L), decreased pleural fluid glucose (<2.2 mmol/L), low pleural fluid pH (<7.2) and positive pleural fluid bacterial culture . The diagnosis and stratification of PPE are two major roles of biochemical analysis in PPE.
Pleural fluid pH is the most accurate indicator of CPPE, as indicated by a meta-analysis . It is also endorsed by the guidelines released by the British Society of Chest Physicians , the European Respiratory Society (ERS) and the European Society of Thoracic Surgeons (ESTS) . Pleural fluid pH should be measured by blood gas analyzer rather than pH meter or indicator strip [140, 141]. There is no need to measure pH in purulent samples because it has the potential to damage the blood gas analyzer [141, 142]. Several factors can affect the value of pleural fluid pH, including the presence of air and residual lidocaine or heparin in the collection syringe . Notably, pleural fluid pH is unstable after collection. Pleural fluid specimens stored at room temperature should be analyzed within an hour after collection [143, 144]. When stored in slushed ice, samples should be analyzed within 2 h and 15 min .
As shown in studies since 1988, serum and pleural fluid C-reactive protein (CRP) have potential diagnostic value for PPE , , . However, the evidence from a meta-analysis published in 2012 revealed that the pooled sensitivity and specificity of serum CRP were 54% and 77%, respectively . A recently published meta-analysis showed that the sensitivity and specificity were 77% and 71%, respectively . These results suggest that serum CRP is not a good diagnostic marker for PPE. The diagnostic accuracy of pleural fluid CRP seems to be higher than that of serum CRP (80% sensitivity and 82% specificity) . In addition, pleural fluid CRP has moderate accuracy for discriminating UPPE from CPPE [150, 151]. A recent meta-analysis showed that the pooled sensitivity and specificity of pleural fluid CRP for distinguishing uncomplicated from complicated PPE were 65% and 85%, respectively . Serum CRP can also distinguish UPPE from CPPE, but its performance varies across available studies [150, 152, 153].
Procalcitonin (PCT) is the precursor of calcitonin, which is mainly synthesized by thyroid C cells . During the development of infectious disease, pathogens and inflammatory factors can induce the expression of PCT in thyroid C cells and other cells, which results in high blood PCT . Therefore, blood PCT is a promising diagnostic marker for bacterial infectious diseases, such as sepsis and pneumonia [156, 157]. PPE is caused by pneumonia, and blood PCT theoretically has diagnostic value for PPE. The diagnostic value of blood PCT for PPE has been investigated by many studies , , . The pooled sensitivity and specificity of blood PCT for PPE were 78% and 74%, respectively , indicating the unsatisfactory diagnostic value of PCT for PPE. Pleural fluid PCT has also been proposed as a diagnostic marker for PPE, but its pooled sensitivity and specificity are only 62% and 71%, respectively, as revealed by meta-analysis . Therefore, the diagnostic value of pleural fluid PCT is inferior to that of serum PCT. This conclusion is also supported by findings from head-to-head comparison studies [159, 160, 162]. Blood PCT is positively correlated with pleural fluid PCT , suggesting that pleural fluid PCT is derived from blood PCT, and pleural fluid PCT does not provide additional diagnostic value beyond serum PCT. The diagnostic accuracy of serum and pleural fluid PCT does not outperform CRP, as indicated by a head-to-head comparison study . Some studies showed that pleural fluid and serum PCT levels in UPPE, CPPE and empyema were similar [146, 163], indicating that PCT cannot be used for PPE stratification.
Other parameters, including soluble triggering receptor expressed on myeloid cells 1 (sTREM-1) , IL-6 [165, 166], IL-8 , presepsin , lipopolysaccharide-binding protein (LBP) , serum amyloid A (SAA) , pentraxin-3 (PTX3) , and soluble urokinase plasminogen activator receptor (suPAR) , are potential biomarkers for PPE diagnosis or stratification. Among those biomarkers, sTREM-1 in the pleural fluid has moderate diagnostic accuracy for PPE. The pooled sensitivity and specificity of pleural fluid sTREM-1 were 78% and 84%, respectively . However, no evidence suggests that pleural fluid sTREM-1 is beneficial for the stratification of PPE. In addition, whether serum sTREM-1 contributes to the diagnosis and stratification of PPE is unknown.
Machine learning is a subset of artificial intelligence. It enables the computer to have intelligence by creating algorithms with large and complex data . Machine learning has shown promising value in clinical diagnostics . The clinical utility of machine learning in patients with undiagnosed pleural effusion has been investigated in some studies, such as treatment selection  and imaging . Using machine learning algorithms with conventional biomarkers and other clinical characteristics (e.g., imaging, symptoms, signs, history, demography) can significantly improve the diagnostic accuracy of biomarkers in undiagnosed pleural effusion patients , , , . For example, in a study that investigated the diagnostic markers for TPE, the clinical characteristics of patients were incorporated into machine learning algorithms, including a logistic regression model, support vector machine (SVM), random forest (RF), and k-nearest neighbor (KNN). The AUC of the RF was 0.97, which is significantly higher than that of pleural fluid ADA (0.89) .
Currently, most pleural fluid biomarkers are protein, enzyme or cancer antigens. Recently, the diagnostic accuracy of cell-free nucleic acids in undiagnosed pleural effusion patients has attracted much attention . Serum or pleural fluid cell-free microRNAs, mRNAs, and long noncoding RNAs (lncRNAs) are the primary cell-free nucleic acids investigated. By using microarray or sequencing, several molecular markers have been identified [180, 181]. Some pilot studies with small sample sizes have revealed that these molecular markers represent promising diagnostic markers for pleural effusion [182, 183]. Further studies are needed to validate their diagnostic accuracy.
As mentioned above, a single biomarker is insufficient for differentiating the causes of pleural effusion. Therefore, high-throughput technologies are promising. First, high-throughput technologies generate significant opportunities for identifying novel biomarkers for differentiating pleural effusion. Second, high-throughput data can be incorporated into mathematical models, which yields good diagnostic accuracy for a given disease. Genomics, transcriptomics, proteomics, and metabolomics are the most popular high-throughput technologies. These technologies can generate massive data in a short period of time with a small volume of the specimen. The primary studies indicated that these technologies have high diagnostic accuracy in differencing pleural effusion. Here, we introduced several examples.
By comparing the protein profile of CPPE and UPPE with isobaric tags for relative and absolute quantification reagents (iTRAQ)-based mass spectrometry analysis, four useful biomarkers (bactericidal permeability-increasing protein, neutrophil gelatinase-associated lipocalin, azurocidin and calprotectin) for differentiating CPPE and UPPE were identified. These biomarkers are promising for differentiating between UPPE and CPPE, with AUCs >0.90 when used alone . With high-resolution nuclear magnetic resonance (NMR) spectrometry, lipoprotein was highly accurate for distinguishing exudates from transudates, with an AUC of 0.96 . In addition, label-free surface-enhanced Raman spectroscopy (SERS) has also been suggested to be a promising diagnostic tool for MPE, with an AUC of 0.99 . Next-generation sequencing (NGS) analysis can identify pathogens more accurately than pleural fluid culture and thus serves as a valuable tool that could facilitate the treatment of PPE with antibiotics .
To date, numerous diagnostic markers have been investigated. Table 2 summarizes the evidence from systematic reviews and meta-analyses. Generally, pleural fluid NT-proBNP and ADA have high diagnostic accuracy for HF and TPE, respectively. These two biomarkers have been endorsed by the guidelines released by the British Thoracic Society . However, the diagnostic markers for PPE and MPE are far from perfect. Therefore, novel biomarkers and analytical methods are needed to improve the diagnostic yield of undiagnosed pleural effusion. Clinical Chemistry and Laboratory Medicine (CCLM) should address the topic of pleural fluid biochemical analysis in the future to promote specific knowledge in the laboratory professional community.
|Biomarker||Disease||Sensitivity, %||Specificity, %||Reference|
|Pleural fluid NT-proBNP||HF||94–95||91–94||, , |
|ADA||TPE||65–94||89–92||[99, 105, 106, 108, 126, 188]|
|Interferon-γ||TPE||89–93||96–97||[118, 188, 189]|
|IGRA, pleural fluid||TPE||72–90||78–87||, , [126, 190]|
|IGRA, blood||TPE||77–80||71–72||[124, 125]|
|CEA||MPE||46–55||94–97||[72, 73, 191]|
|CA15-3||MPE||51–58||93–98||[72, 192, 193]|
|CA 19-9||MPE||25–38||96–98||[72, 192]|
|CA 125||MPE||48–58||85–93||[72, 192]|
|CYFRA 21-1||MPE||47–63||92–93||[72, 191, 192]|
|Cancer ratio||MPE||91–97||67–89||[92, 96]|
|Pleural fluid CRP||PPE||80||82|||
|Blood CRP||PPE||54–77||71–77||[148, 149]|
|Pleural fluid procalcitonin||PPE||62–67||70–71||[148, 161]|
|Blood procalcitonin||PPE||65–78||68–74||[148, 161]|
Funding source: Natural and Science Foundation of Inner Mongolia Autonomous Region for Distinguished Young Scholars
Award Identifier / Grant number: 2020JQ07
Funding source: Zhixue Project, Zhiyuan Funding of Inner Mongolia Medical University
Award Identifier / Grant number: ZY 0130013
Research funding: This work was supported by the Natural and Science Foundation of Inner Mongolia Autonomous Region for Distinguished Young Scholars [NO: 2020JQ07] and the Zhixue Project, Zhiyuan Funding of Inner Mongolia Medical University [NO: ZY 0130013]. The funders played no role in the design, conduct, or reporting of the research.
Author contributions: Zhi-De Hu and Wen-Qi Zheng designed and supervised the study. Wen-Qi Zheng drafted the manuscript. Zhi-De Hu critically reviewed and edited the manuscript. All authors have accepted responsibility for the entire content of this manuscript and approved its submission.
Competing interests: The authors have no conflicts of interest to declare.
Informed consent: Not applicable.
Ethical approval: Not applicable.
1. Tian, P, Qiu, R, Wang, M, Xu, S, Cao, L, Yang, P, et al.. Prevalence, causes, and health care burden of pleural effusions among hospitalized adults in China. JAMA Netw Open 2021;4:e2120306. https://doi.org/10.1001/jamanetworkopen.2021.20306.Search in Google Scholar PubMed PubMed Central
2. Porcel, JM, Esquerda, A, Vives, M, Bielsa, S. Etiology of pleural effusions: analysis of more than 3, 000 consecutive thoracenteses. Arch Bronconeumol 2014;50:161–5. https://doi.org/10.1016/j.arbr.2014.03.012.Search in Google Scholar
3. Arnold, DT, De Fonseka, D, Perry, S, Morley, A, Harvey, JE, Medford, A, et al.. Investigating unilateral pleural effusions: the role of cytology. Eur Respir J 2018;52:1801254. https://doi.org/10.1183/13993003.01254-2018.Search in Google Scholar PubMed
4. Wang, XJ, Yang, Y, Wang, Z, Xu, LL, Wu, YB, Zhang, J, et al.. Efficacy and safety of diagnostic thoracoscopy in undiagnosed pleural effusions. Respiration 2015;90:251–5. https://doi.org/10.1159/000435962.Search in Google Scholar PubMed
5. Zhang, M, Yan, L, Lippi, G, Hu, ZD. Pleural biomarkers in diagnostics of malignant pleural effusion: a narrative review. Transl Lung Cancer Res 2021;10:1557–70. https://doi.org/10.21037/tlcr-20-1111.Search in Google Scholar PubMed PubMed Central
6. Yang, Y, Liu, YL, Shi, HZ. Diagnostic accuracy of combinations of tumor markers for malignant pleural effusion: an updated meta-analysis. Respiration 2017;94:62–9. https://doi.org/10.1159/000468545.Search in Google Scholar PubMed
7. Sriram, KB, Relan, V, Clarke, BE, Duhig, EE, Yang, IA, Bowman, RV, et al.. Diagnostic molecular biomarkers for malignant pleural effusions. Future Oncol 2011;7:737–52. https://doi.org/10.2217/fon.11.45.Search in Google Scholar PubMed
9. Mollo, B, Jouveshomme, S, Philippart, F, Pilmis, B. Biological markers in the diagnosis of tuberculous pleural effusion. Ann Biol Clin 2017;75:19–27. https://doi.org/10.1684/abc.2016.1201.Search in Google Scholar PubMed
10. Zhang, M, Li, D, Hu, ZD, Huang, YL. The diagnostic utility of pleural markers for tuberculosis pleural effusion. Ann Transl Med 2020;8:607. https://doi.org/10.21037/atm.2019.09.110.Search in Google Scholar PubMed PubMed Central
11. Porcel, JM. Utilization of B-type natriuretic peptide and NT-proBNP in the diagnosis of pleural effusions due to heart failure. Curr Opin Pulm Med 2011;17:215–9. https://doi.org/10.1097/mcp.0b013e3283455cda.Search in Google Scholar
13. Husnain, SMN, Shojaee, S. Hepatic hydrothorax and congestive heart failure induced pleural effusion. Clin Chest Med 2021;42:625–35. https://doi.org/10.1016/j.ccm.2021.07.005.Search in Google Scholar PubMed
14. Esquerda, A, Trujillano, J, de Ullibarri, IL, Bielsa, S, Madronero, AB, Porcel, JM. Classification tree analysis for the discrimination of pleural exudates and transudates. Clin Chem Lab Med 2007;45:82–7. https://doi.org/10.1515/cclm.2007.001.Search in Google Scholar PubMed
16. Villena, V, Lopez-Encuentra, A, Garcia-Lujan, R, Echave-Sustaeta, J, Martinez, CJ. Clinical implications of appearance of pleural fluid at thoracentesis. Chest 2004;125:156–9. https://doi.org/10.1378/chest.125.1.156.Search in Google Scholar PubMed
17. Carr, DT, Power, MH. Clinical value of measurements of concentration of protein in pleural fluid. N Engl J Med 1958;259:926–7. https://doi.org/10.1056/nejm195811062591909.Search in Google Scholar PubMed
18. Chandrasekhar, AJ, Palatao, A, Dubin, A, Levine, H. Pleural fluid lactic acid dehydrogenase activity and protein content. Value in diagnosis. Arch Intern Med 1969;123:48–50. https://doi.org/10.1001/archinte.123.1.48.Search in Google Scholar
19. Light, RW, Macgregor, MI, Luchsinger, PC, Ball, WCJr. Pleural effusions: the diagnostic separation of transudates and exudates. Ann Intern Med 1972;77:507–13. https://doi.org/10.7326/0003-4819-77-4-507.Search in Google Scholar PubMed
20. Metintas, M, Alatas, O, Alatas, F, Colak, O, Ozdemir, N, Erginel, S. Comparative analysis of biochemical parameters for differentiation of pleural exudates from transudates Light’s criteria, cholesterol, bilirubin, albumin gradient, alkaline phosphatase, creatine kinase, and uric acid. Clin Chim Acta 1997;264:149–62. https://doi.org/10.1016/s0009-8981(97)00091-0.Search in Google Scholar PubMed
22. Valdes, L, Pose, A, Suarez, J, Gonzalez-Juanatey, JR, Sarandeses, A, San Jose, E, et al.. Cholesterol: a useful parameter for distinguishing between pleural exudates and transudates. Chest 1991;99:1097–102. https://doi.org/10.1378/chest.99.5.1097.Search in Google Scholar PubMed
23. Block, DR, Algeciras-Schimnich, A. Body fluid analysis: clinical utility and applicability of published studies to guide interpretation of today’s laboratory testing in serous fluids. Crit Rev Clin Lab Sci 2013;50:107–24. https://doi.org/10.3109/10408363.2013.844679.Search in Google Scholar PubMed
24. Romero-Candeira, S, Hernandez, L, Romero-Brufao, S, Orts, D, Fernandez, C, Martin, C. Is it meaningful to use biochemical parameters to discriminate between transudative and exudative pleural effusions? Chest 2002;122:1524–9. https://doi.org/10.1378/chest.122.5.1524.Search in Google Scholar PubMed
25. Bielsa, S, Porcel, JM, Castellote, J, Mas, E, Esquerda, A, Light, RW. Solving the Light’s criteria misclassification rate of cardiac and hepatic transudates. Respirology 2012;17:721–6. https://doi.org/10.1111/j.1440-1843.2012.02155.x.Search in Google Scholar PubMed
26. Ferreiro, L, Sanchez-Sanchez, R, Valdes, L, Kummerfeldt, CE, Huggins, JT. Concordant and discordant exudates and their effect on the accuracy of Light’s criteria to diagnose exudative pleural effusions. Am J Med Sci 2016;352:549–56. https://doi.org/10.1016/j.amjms.2016.08.016.Search in Google Scholar PubMed
27. Chakko, SC, Caldwell, SH, Sforza, PP. Treatment of congestive heart failure. Its effect on pleural fluid chemistry. Chest 1989;95:798–802. https://doi.org/10.1378/chest.95.4.798.Search in Google Scholar PubMed
28. Romero-Candeira, S, Fernandez, C, Martin, C, Sanchez-Paya, J, Hernandez, L. Influence of diuretics on the concentration of proteins and other components of pleural transudates in patients with heart failure. Am J Med 2001;110:681–6. https://doi.org/10.1016/s0002-9343(01)00726-4.Search in Google Scholar PubMed
29. Porcel, JM, Bielsa, S, Morales-Rull, JL, Civit, C, Cao, G, Light, RW, et al.. Comparison of pleural N-terminal pro-B-type natriuretic peptide, midregion pro-atrial natriuretic peptide and mid-region pro-adrenomedullin for the diagnosis of pleural effusions associated with cardiac failure. Respirology 2013;18:540–5. https://doi.org/10.1111/resp.12039.Search in Google Scholar PubMed
30. Porcel, JM, Martinez-Alonso, M, Cao, G, Bielsa, S, Sopena, A, Esquerda, A. Biomarkers of heart failure in pleural fluid. Chest 2009;136:671–7. https://doi.org/10.1378/chest.09-0270.Search in Google Scholar PubMed
31. Han, CH, Choi, JE, Chung, JH. Clinical utility of pleural fluid NT-pro brain natriuretic peptide (NT-proBNP) in patients with pleural effusions. Intern Med 2008;47:1669–74. https://doi.org/10.2169/internalmedicine.47.1276.Search in Google Scholar PubMed
32. Porcel, JM, Chorda, J, Cao, G, Esquerda, A, Ruiz-Gonzalez, A, Vives, M. Comparing serum and pleural fluid pro-brain natriuretic peptide (NT-proBNP) levels with pleural-to-serum albumin gradient for the identification of cardiac effusions misclassified by Light’s criteria. Respirology 2007;12:654–9. https://doi.org/10.1111/j.1440-1843.2007.01109.x.Search in Google Scholar PubMed
33. Shen, Y, Zhu, H, Wan, C, Chen, L, Wang, T, Yang, T, et al.. Can cholesterol be used to distinguish pleural exudates from transudates? Evidence from a bivariate meta-analysis. BMC Pulm Med 2014;14:61. https://doi.org/10.1186/1471-2466-14-61.Search in Google Scholar PubMed PubMed Central
34. Tomcsanyi, J, Nagy, E, Somloi, M, Moldvay, J, Bezzegh, A, Bozsik, B, et al.. NT-brain natriuretic peptide levels in pleural fluid distinguish between pleural transudates and exudates. Eur J Heart Fail 2004;6:753–6. https://doi.org/10.1016/j.ejheart.2003.11.017.Search in Google Scholar PubMed
35. Kogan, Y, Sabo, E, Odeh, M. Role of C-reactive protein in discrimination between transudative and exudative pleural effusions. Diagnostics 2021;11:2003. https://doi.org/10.3390/diagnostics11112003.Search in Google Scholar PubMed PubMed Central
36. Meisel, S, Shamiss, A, Thaler, M, Nussinovitch, N, Rosenthal, T. Pleural fluid to serum bilirubin concentration ratio for the separation of transudates from exudates. Chest 1990;98:141–4. https://doi.org/10.1378/chest.98.1.141.Search in Google Scholar PubMed
37. Romero, S, Martinez, A, Hernandez, L, Fernandez, C, Espasa, A, Candela, A, et al.. Light’s criteria revisited: consistency and comparison with new proposed alternative criteria for separating pleural transudates from exudates. Respiration 2000;67:18–23. https://doi.org/10.1159/000029457.Search in Google Scholar PubMed
38. Heffner, JE, Brown, LK, Barbieri, CA. Diagnostic value of tests that discriminate between exudative and transudative pleural effusions. Primary study investigators. Chest 1997;111:970–80. https://doi.org/10.1378/chest.111.4.970.Search in Google Scholar PubMed
39. Romero, S, Candela, A, Martin, C, Hernandez, L, Trigo, C, Gil, J. Evaluation of different criteria for the separation of pleural transudates from exudates. Chest 1993;104:399–404. https://doi.org/10.1378/chest.104.2.399.Search in Google Scholar PubMed
40. Vives, M, Porcel, JM, de Vera, MV, Ribelles, E, Rubio, M. A study of Light’s criteria and possible modifications for distinguishing exudative from transudative pleural effusions. Chest 1996;109:1503–7. https://doi.org/10.1378/chest.109.6.1503.Search in Google Scholar PubMed
41. Costa, M, Quiroga, T, Cruz, E. Measurement of pleural fluid cholesterol and lactate dehydrogenase. A simple and accurate set of indicators for separating exudates from transudates. Chest 1995;108:1260–3. https://doi.org/10.1378/chest.108.5.1260.Search in Google Scholar PubMed
42. Lepine, PA, Thomas, R, Nguyen, S, Lacasse, Y, Cheah, HM, Creaney, J, et al.. Simplified criteria using pleural fluid cholesterol and lactate dehydrogenase to distinguish between exudative and transudative pleural effusions. Respiration 2019;98:48–54. https://doi.org/10.1159/000496396.Search in Google Scholar PubMed
43. Lee, YCG, Davies, RJO, Light, RW. Diagnosing pleural effusion: moving beyond transudate-exudate separation. Chest 2007;131:942–3. https://doi.org/10.1378/chest.06-2847.Search in Google Scholar PubMed
44. Adams, A, Straseski, JA, Lehman, CM, Pearson, LN. Peritoneal and pleural fluid chemistry measurements performed on three chemistry platforms. Lab Med 2019;50:145–9. https://doi.org/10.1093/labmed/lmy056.Search in Google Scholar PubMed
45. Cornes, MP, Chadburn, AJ, Thomas, C, Darby, C, Webster, R, Ford, C, et al.. The impact of between analytical platform variability on the classification of pleural effusions into exudate or transudate using Light’s criteria. J Clin Pathol 2017;70:607–9. https://doi.org/10.1136/jclinpath-2016-204142.Search in Google Scholar PubMed
47. Kopcinovic, LM, Brcic, M, Vrtaric, A, Unic, A, Bozovic, M, Gabaj, NN, et al.. Long-term stability of clinically relevant chemistry analytes in pleural and peritoneal fluid. Biochem Med 2020;30:020701. https://doi.org/10.11613/bm.2020.020701.Search in Google Scholar PubMed PubMed Central
48. Jenkinson, F, Murphy, MJ. Biochemical analysis of pleural and ascitic fluid: effect of sample timing on interpretation of results. Ann Clin Biochem 2007;44:471–3. https://doi.org/10.1258/000456307781645978.Search in Google Scholar PubMed
49. Porcel, JM, Esquerda, A, Martinez, M, Rodriguez-Panadero, F, Bielsa, S. Influence of pleural fluid red blood cell count on the misidentification of transudates. Med Clin 2008;131:770–2.10.1016/S0025-7753(08)75501-5Search in Google Scholar PubMed
50. Ugurman, F, Gozu, A, Gocmen, S, Samurkasoglu, B, Onde, G, Akkalyoncu, B, et al.. Effect of iatrogenic haemorrhage on biochemical parameters in pleural effusions. Respir Med 2003;97:1265–8. https://doi.org/10.1016/s0954-6111(03)00257-9.Search in Google Scholar PubMed
51. Lippi, G, Salvagno, GL, Montagnana, M, Brocco, G, Guidi, GC. Influence of hemolysis on routine clinical chemistry testing. Clin Chem Lab Med 2006;44:311–6. https://doi.org/10.1515/cclm.2006.054.Search in Google Scholar PubMed
52. Eigsti, RL, Krasowski, MD, Vidholia, A, Merrill, AE. Review of interference indices in body fluid specimens submitted for clinical chemistry analyses. Pract Lab Med 2020;19:e00155. https://doi.org/10.1016/j.plabm.2020.e00155.Search in Google Scholar PubMed PubMed Central
53. Owen, WE, Thatcher, ML, Crabtree, KJ, Greer, RW, Strathmann, FG, Straseski, JA, et al.. Body fluid matrix evaluation on a Roche cobas 8000 system. Clin Biochem 2015;48:911–4. https://doi.org/10.1016/j.clinbiochem.2015.05.012.Search in Google Scholar PubMed
54. Block, DR, Ouverson, LJ, Wittwer, CA, Saenger, AK, Baumann, NA. An approach to analytical validation and testing of body fluid assays for the automated clinical laboratory. Clin Biochem 2018;58:44–52. https://doi.org/10.1016/j.clinbiochem.2018.05.002.Search in Google Scholar PubMed
55. Lin, MJ, Hoke, C, Dlott, R, Lorey, TS, Greene, DN. Performance specifications of common chemistry analytes on the AU series of chemistry analyzers for miscellaneous body fluids. Clin Chim Acta 2013;426:121–6. https://doi.org/10.1016/j.cca.2013.08.011.Search in Google Scholar PubMed
56. Porcel, JM. Biomarkers in the diagnosis of pleural diseases: a 2018 update. Ther Adv Respir Dis 2018;12:1753466618808660. https://doi.org/10.1177/1753466618808660.Search in Google Scholar PubMed PubMed Central
57. Authors/Task Force M, McDonagh, TA, Metra, M, Adamo, M, Gardner, RS, Baumbach, A, et al.. 2021 ESC guidelines for the diagnosis and treatment of acute and chronic heart failure: developed by the task force for the diagnosis and treatment of acute and chronic heart failure of the European society of cardiology (ESC). With the special contribution of the heart failure association (HFA) of the ESC. Eur J Heart Fail 2022;24:4–131.Search in Google Scholar
59. Han, ZJ, Wu, XD, Cheng, JJ, Zhao, SD, Gao, MZ, Huang, HY, et al.. Diagnostic accuracy of natriuretic peptides for heart failure in patients with pleural effusion: a systematic review and updated meta-analysis. PLoS One 2015;10:e0134376. https://doi.org/10.1371/journal.pone.0134376.Search in Google Scholar PubMed PubMed Central
60. Janda, S, Swiston, J. Diagnostic accuracy of pleural fluid NT-pro-BNP for pleural effusions of cardiac origin: a systematic review and meta-analysis. BMC Pulm Med 2010;10:58. https://doi.org/10.1186/1471-2466-10-58.Search in Google Scholar PubMed PubMed Central
61. Zhou, Q, Ye, ZJ, Su, Y, Zhang, JC, Shi, HZ. Diagnostic value of N-terminal pro-brain natriuretic peptide for pleural effusion due to heart failure: a meta-analysis. Heart 2010;96:1207–11. https://doi.org/10.1136/hrt.2009.188474.Search in Google Scholar PubMed
62. Hooper, C, Lee, YC, Maskell, N, Group BTSPG. Investigation of a unilateral pleural effusion in adults: British thoracic society pleural disease guideline 2010. Thorax 2010;65(2 Suppl):ii4–17. https://doi.org/10.1136/thx.2010.136978.Search in Google Scholar PubMed
63. Kolditz, M, Halank, M, Schiemanck, CS, Schmeisser, A, Hoffken, G. High diagnostic accuracy of NT-proBNP for cardiac origin of pleural effusions. Eur Respir J 2006;28:144–50. https://doi.org/10.1183/09031936.06.00113205.Search in Google Scholar PubMed
64. Yeh, JH, Huang, CT, Liu, CH, Ruan, SY, Tsai, YJ, Chien, YC, et al.. Cautious application of pleural N-terminal pro-B-type natriuretic peptide in diagnosis of congestive heart failure pleural effusions among critically ill patients. PLoS One 2014;9:e115301. https://doi.org/10.1371/journal.pone.0115301.Search in Google Scholar PubMed PubMed Central
65. Porcel, JM, Ferreiro, L, Civit, C, Valdes, L, Esquerda, A, Light, RW, et al.. Development and validation of a scoring system for the identification of pleural exudates of cardiac origin. Eur J Intern Med 2018;50:60–4. https://doi.org/10.1016/j.ejim.2017.11.008.Search in Google Scholar PubMed
66. Hu, Z, Han, Z, Huang, Y, Sun, Y, Li, B, Deng, A. Diagnostic power of the mid-regional pro-atrial natriuretic peptide for heart failure patients with dyspnea: a meta-analysis. Clin Biochem 2012;45:1634–9. https://doi.org/10.1016/j.clinbiochem.2012.08.028.Search in Google Scholar PubMed
67. Roberts, E, Ludman, AJ, Dworzynski, K, Al-Mohammad, A, Cowie, MR, McMurray, JJ, et al.. The diagnostic accuracy of the natriuretic peptides in heart failure: systematic review and diagnostic meta-analysis in the acute care setting. BMJ 2015;350:h910. https://doi.org/10.1136/bmj.h910.Search in Google Scholar PubMed PubMed Central
68. Arrigo, M, Truong, QA, Onat, D, Szymonifka, J, Gayat, E, Tolppanen, H, et al.. Soluble CD146 is a novel marker of systemic congestion in heart failure patients: an experimental mechanistic and transcardiac clinical study. Clin Chem 2017;63:386–93. https://doi.org/10.1373/clinchem.2016.260471.Search in Google Scholar PubMed
69. Gayat, E, Caillard, A, Laribi, S, Mueller, C, Sadoune, M, Seronde, MF, et al.. Soluble CD146, a new endothelial biomarker of acutely decompensated heart failure. Int J Cardiol 2015;199:241–7. https://doi.org/10.1016/j.ijcard.2015.07.039.Search in Google Scholar PubMed
70. Ozsu, S, Gulsoy, A, Karahan, SC, Mentese, A, Nuhoglu, I, Ozlu, T. Diagnostic value of pleural effusion ischaemia-modified albumin in patients with cardiac failure. Ann Clin Biochem 2011;48:45–50. https://doi.org/10.1258/acb.2010.010159.Search in Google Scholar PubMed
71. Dikensoy, O, Celik, N, Kul, S, Gogebakan, B, Bayram, H, Light, RW. Ischemia modified albumin in the differential diagnosis of pleural effusions. Respir Med 2011;105:1712–7. https://doi.org/10.1016/j.rmed.2011.07.015.Search in Google Scholar PubMed
72. Nguyen, AH, Miller, EJ, Wichman, CS, Berim, IG, Agrawal, DK. Diagnostic value of tumor antigens in malignant pleural effusion: a meta-analysis. Transl Res 2015;166:432–9. https://doi.org/10.1016/j.trsl.2015.04.006.Search in Google Scholar PubMed PubMed Central
73. Shi, HZ, Liang, QL, Jiang, J, Qin, XJ, Yang, HB. Diagnostic value of carcinoembryonic antigen in malignant pleural effusion: a meta-analysis. Respirology 2008;13:518–27. https://doi.org/10.1111/j.1440-1843.2008.01291.x.Search in Google Scholar PubMed
74. Zhu, J, Feng, M, Liang, L, Zeng, N, Wan, C, Yang, T, et al.. Is neuron-specific enolase useful for diagnosing malignant pleural effusions? Evidence from a validation study and meta-analysis. BMC Cancer 2017;17:590. https://doi.org/10.1186/s12885-017-3572-2.Search in Google Scholar PubMed PubMed Central
75. Zhang, M, Hu, ZD. Suggestions for designing studies investigating diagnostic accuracy of biomarkers. Ann Transl Med 2019;7:788. https://doi.org/10.21037/atm.2019.11.133.Search in Google Scholar PubMed PubMed Central
76. Linnet, K, Bossuyt, PM, Moons, KG, Reitsma, JB. Quantifying the accuracy of a diagnostic test or marker. Clin Chem 2012;58:1292–301. https://doi.org/10.1373/clinchem.2012.182543.Search in Google Scholar PubMed
77. Porcel, JM, Civit, C, Esquerda, A, Salud, A, Bielsa, S. Utility of CEA and CA 15-3 measurements in non-purulent pleural exudates in the diagnosis of malignancy: a single-center experience. Arch Bronconeumol 2017;53:427–31. https://doi.org/10.1016/j.arbr.2016.12.015.Search in Google Scholar
78. Wang, S, Tian, S, Li, Y, Zhan, N, Guo, Y, Liu, Y, et al.. Development and validation of a novel scoring system developed from a nomogram to identify malignant pleural effusion. EBioMedicine 2020;58:102924. https://doi.org/10.1016/j.ebiom.2020.102924.Search in Google Scholar PubMed PubMed Central
79. Wu, A, Liang, Z, Yuan, S, Wang, S, Peng, W, Mo, Y, et al.. Development and validation of a scoring system for early diagnosis of malignant pleural effusion based on a nomogram. Front Oncol 2021;11:775079. https://doi.org/10.3389/fonc.2021.775079.Search in Google Scholar PubMed PubMed Central
80. Gu, Y, Zhai, K, Shi, HZ. Clinical value of tumor markers for determining cause of pleural effusion. Chin Med J 2016;129:253–8. https://doi.org/10.4103/0366-6999.174501.Search in Google Scholar PubMed PubMed Central
81. Volaric, D, Flego, V, Zauhar, G, Bulat-Kardum, L. Diagnostic value of tumour markers in pleural effusions. Biochem Med 2018;28:010706. https://doi.org/10.11613/bm.2018.010706.Search in Google Scholar
82. Korczynski, P, Krenke, R, Safianowska, A, Gorska, K, Abou Chaz, MB, Maskey-Warzechowska, M, et al.. Diagnostic utility of pleural fluid and serum markers in differentiation between malignant and non-malignant pleural effusions. Eur J Med Res 2009;14(4 Suppl):128–33. https://doi.org/10.1186/2047-783x-14-s4-128.Search in Google Scholar PubMed PubMed Central
83. Zhai, K, Wang, W, Wang, Y, Liu, JY, Zhou, Q, Shi, HZ. Diagnostic accuracy of tumor markers for malignant pleural effusion: a derivation and validation study. J Thorac Dis 2017;9:5220–9. https://doi.org/10.21037/jtd.2017.11.62.Search in Google Scholar PubMed PubMed Central
84. Zhang, H, Li, C, Hu, F, Zhang, X, Shen, Y, Chen, Y, et al.. Auxiliary diagnostic value of tumor biomarkers in pleural fluid for lung cancer-associated malignant pleural effusion. Respir Res 2020;21:284. https://doi.org/10.1186/s12931-020-01557-z.Search in Google Scholar PubMed PubMed Central
85. Fan, X, Liu, Y, Liang, Z, Wang, S, Yang, J, Wu, A. Diagnostic value of six tumor markers for malignant pleural effusion in 1, 230 patients: a single-center retrospective study. Pathol Oncol Res 2022;28:1610280. https://doi.org/10.3389/pore.2022.1610280.Search in Google Scholar PubMed PubMed Central
86. Pencina, MJ, D’Agostino, RBSr, D’Agostino, RBJr, Vasan, RS. Evaluating the added predictive ability of a new marker: from area under the ROC curve to reclassification and beyond. Stat Med 2008;27:157–72. https://doi.org/10.1002/sim.2929.Search in Google Scholar PubMed
87. Yang, SY, Zhao, Y, Wang, XR, Wu, J, Yang, DN, Liu, CL, et al.. Diagnostic accuracy of endostatin for malignant pleural effusion: a systematic review and meta-analysis. J Lab Precis Med 2021;6:5. https://doi.org/10.21037/jlpm-20-91.Search in Google Scholar
88. Fiorelli, A, Vicidomini, G, Di Domenico, M, Napolitano, F, Messina, G, Morgillo, F, et al.. Vascular endothelial growth factor in pleural fluid for differential diagnosis of benign and malignant origin and its clinical applications. Interact Cardiovasc Thorac Surg 2011;12:420–4. https://doi.org/10.1510/icvts.2010.250357.Search in Google Scholar PubMed
89. Gu, Y, Zhang, M, Li, GH, Gao, JZ, Guo, L, Qiao, XJ, et al.. Diagnostic values of vascular endothelial growth factor and epidermal growth factor receptor for benign and malignant hydrothorax. Chin Med J 2015;128:305–9. https://doi.org/10.4103/0366-6999.150091.Search in Google Scholar PubMed PubMed Central
90. Wang, Y, Chen, Z, Chen, J, Pan, J, Zhang, W, Pan, Q, et al.. The diagnostic value of apolipoprotein E in malignant pleural effusion associated with non-small cell lung cancer. Clin Chim Acta 2013;421:230–5. https://doi.org/10.1016/j.cca.2013.03.013.Search in Google Scholar PubMed
91. Pei, XB, Wu, XZ, Yi, FS, Zhai, K, Shi, HZ. Diagnostic value of CD206(+)CD14(+) macrophages in diagnosis of lung cancer originated malignant pleural effusion. J Thorac Dis 2019;11:2730–6. https://doi.org/10.21037/jtd.2019.06.44.Search in Google Scholar PubMed PubMed Central
92. Han, YQ, Zhang, L, Yan, L, Ouyang, PH, Li, P, Hu, ZD. Diagnostic accuracy of cancer ratio for malignant pleural effusion: a systematic review and meta-analysis. Ann Transl Med 2019;7:554. https://doi.org/10.21037/atm.2019.09.85.Search in Google Scholar PubMed PubMed Central
93. Verma, A, Abisheganaden, J, Light, RW. Identifying malignant pleural effusion by a cancer ratio (serum LDH: pleural fluid ADA ratio). Lung 2016;194:147–53. https://doi.org/10.1007/s00408-015-9831-6.Search in Google Scholar PubMed PubMed Central
94. Verma, A, Dagaonkar, RS, Marshall, D, Abisheganaden, J, Light, RW. Differentiating malignant from tubercular pleural effusion by cancer ratio plus (cancer ratio: pleural lymphocyte count). Can Respir J 2016;2016:7348239. https://doi.org/10.1155/2016/7348239.Search in Google Scholar PubMed PubMed Central
95. Wang, F, Yang, L, Gao, Q, Huang, L, Wang, L, Wang, J, et al.. CD163+CD14+ macrophages, a potential immune biomarker for malignant pleural effusion. Cancer Immunol Immunother 2015;64:965–76. https://doi.org/10.1007/s00262-015-1701-9.Search in Google Scholar PubMed
96. Zhang, Y, Li, X, Liu, J, Hu, X, Wan, C, Zhang, R, et al.. Diagnostic accuracy of the cancer ratio for the prediction of malignant pleural effusion: evidence from a validation study and meta-analysis. Ann Med 2021;53:558–66. https://doi.org/10.1080/07853890.2021.1906943.Search in Google Scholar PubMed PubMed Central
99. Liang, QL, Shi, HZ, Wang, K, Qin, SM, Qin, XJ. Diagnostic accuracy of adenosine deaminase in tuberculous pleurisy: a meta-analysis. Respir Med 2008;102:744–54. https://doi.org/10.1016/j.rmed.2007.12.007.Search in Google Scholar PubMed
100. Aggarwal, AN, Agarwal, R, Dhooria, S, Prasad, KT, Sehgal, IS, Muthu, V. Unstimulated pleural fluid interferon gamma for diagnosis of tuberculous pleural effusion: a systematic review and meta-analysis. J Clin Microbiol 2021;59. https://doi.org/10.1128/jcm.02112-20.Search in Google Scholar PubMed PubMed Central
101. Zhang, Q, Ma, Y, Zhang, M, Wang, Y, Wu, W. Diagnostic accuracy of interleukin-27 in tuberculous pleurisy: a systematic review and meta-analysis. Qjm 2021;114:568–76. https://doi.org/10.1093/qjmed/hcaa215.Search in Google Scholar PubMed
102. Piras, MA, Gakis, C, Budroni, M, Andreoni, G. Adenosine deaminase activity in pleural effusions: an aid to differential diagnosis. Br Med J 1978;2:1751–2. https://doi.org/10.1136/bmj.2.6154.1751-a.Search in Google Scholar PubMed PubMed Central
103. Goto, M, Noguchi, Y, Koyama, H, Hira, K, Shimbo, T, Fukui, T. Diagnostic value of adenosine deaminase in tuberculous pleural effusion: a meta-analysis. Ann Clin Biochem 2003;40:374–81. https://doi.org/10.1258/000456303766477011.Search in Google Scholar PubMed
104. Greco, S, Girardi, E, Masciangelo, R, Capoccetta, GB, Saltini, C. Adenosine deaminase and interferon gamma measurements for the diagnosis of tuberculous pleurisy: a meta-analysis. Int J Tubercul Lung Dis 2003;7:777–86.Search in Google Scholar
105. Aggarwal, AN, Agarwal, R, Sehgal, IS, Dhooria, S. Adenosine deaminase for diagnosis of tuberculous pleural effusion: a systematic review and meta-analysis. PLoS One 2019;14:e0213728. https://doi.org/10.1371/journal.pone.0213728.Search in Google Scholar PubMed PubMed Central
106. Palma, RM, Bielsa, S, Esquerda, A, Martinez-Alonso, M, Porcel, JM. Diagnostic accuracy of pleural fluid adenosine deaminase for diagnosing tuberculosis. Meta-analysis of Spanish studies. Arch Bronconeumol 2019;55:23–30. https://doi.org/10.1016/j.arbr.2018.11.007.Search in Google Scholar
107. Morisson, P, Neves, DD. Evaluation of adenosine deaminase in the diagnosis of pleural tuberculosis: a Brazilian meta-analysis. J Bras Pneumol 2008;34:217–24. https://doi.org/10.1590/s1806-37132008000400006.Search in Google Scholar PubMed
108. Aggarwal, AN, Agarwal, R, Sehgal, IS, Dhooria, S, Behera, D. Meta-analysis of Indian studies evaluating adenosine deaminase for diagnosing tuberculous pleural effusion. Int J Tubercul Lung Dis 2016;20:1386–91. https://doi.org/10.5588/ijtld.16.0298.Search in Google Scholar PubMed
109. Blakiston, M, Chiu, W, Wong, C, Morpeth, S, Taylor, S. Diagnostic performance of pleural fluid adenosine deaminase for tuberculous pleural effusion in a low-incidence setting. J Clin Microbiol 2018;56:e00258–18. https://doi.org/10.1128/jcm.00258-18.Search in Google Scholar PubMed PubMed Central
110. Chang, KC, Chan, MC, Leung, WM, Kong, FY, Mak, CM, Chen, SP, et al.. Optimising the utility of pleural fluid adenosine deaminase for the diagnosis of adult tuberculous pleural effusion in Hong Kong. Hong Kong Med J 2018;24:38–47.10.12809/hkmj176238Search in Google Scholar PubMed
111. Abrao, FC, de Abreu, IR, Miyake, DH, Busico, MA, Younes, RN. Role of adenosine deaminase and the influence of age on the diagnosis of pleural tuberculosis. Int J Tubercul Lung Dis 2014;18:1363–9. https://doi.org/10.5588/ijtld.14.0257.Search in Google Scholar PubMed
112. Tay, TR, Tee, A. Factors affecting pleural fluid adenosine deaminase level and the implication on the diagnosis of tuberculous pleural effusion: a retrospective cohort study. BMC Infect Dis 2013;13:546. https://doi.org/10.1186/1471-2334-13-546.Search in Google Scholar PubMed PubMed Central
113. Korczynski, P, Klimiuk, J, Safianowska, A, Krenke, R. Impact of age on the diagnostic yield of four different biomarkers of tuberculous pleural effusion. Tuberculosis 2019;114:24–9. https://doi.org/10.1016/j.tube.2018.11.004.Search in Google Scholar PubMed
114. Jiang, CG, Wang, W, Zhou, Q, Wu, XZ, Wang, XJ, Wang, Z, et al.. Influence of age on the diagnostic accuracy of soluble biomarkers for tuberculous pleural effusion: a post hoc analysis. BMC Pulm Med 2020;20:178. https://doi.org/10.1186/s12890-020-01219-2.Search in Google Scholar PubMed PubMed Central
115. Wu, YH, Zhao, GW, Wang, XF, Wang, MS. Pleural effusion adenosine deaminase is not accurate in diagnosis of pediatric tuberculous pleural effusion: a retrospective study. Eur Rev Med Pharmacol Sci 2015;19:1706–10.Search in Google Scholar
116. Wu, YB, Ye, ZJ, Qin, SM, Wu, C, Chen, YQ, Shi, HZ. Combined detections of interleukin 27, interferon-gamma, and adenosine deaminase in pleural effusion for diagnosis of tuberculous pleurisy. Chin Med J 2013;126:3215–21.Search in Google Scholar
117. Ribera, E, Ocana, I, Martinez-Vazquez, JM, Rossell, M, Espanol, T, Ruibal, A. High level of interferon gamma in tuberculous pleural effusion. Chest 1988;93:308–11. https://doi.org/10.1378/chest.93.2.308.Search in Google Scholar PubMed
118. Jiang, J, Shi, HZ, Liang, QL, Qin, SM, Qin, XJ. Diagnostic value of interferon-gamma in tuberculous pleurisy: a metaanalysis. Chest 2007;131:1133–41. https://doi.org/10.1378/chest.06-2273.Search in Google Scholar PubMed
119. Yang, WB, Liang, QL, Ye, ZJ, Niu, CM, Ma, WL, Xiong, XZ, et al.. Cell origins and diagnostic accuracy of interleukin 27 in pleural effusions. PLoS One 2012;7:e40450. https://doi.org/10.1371/journal.pone.0040450.Search in Google Scholar PubMed PubMed Central
120. Wang, W, Zhou, Q, Zhai, K, Wang, Y, Liu, JY, Wang, XJ, et al.. Diagnostic accuracy of interleukin 27 for tuberculous pleural effusion: two prospective studies and one meta-analysis. Thorax 2018;73:240–7. https://doi.org/10.1136/thoraxjnl-2016-209718.Search in Google Scholar PubMed
121. Zeng, N, Wan, C, Qin, J, Wu, Y, Yang, T, Shen, Y, et al.. Diagnostic value of interleukins for tuberculous pleural effusion: a systematic review and meta-analysis. BMC Pulm Med 2017;17:180. https://doi.org/10.1186/s12890-017-0530-3.Search in Google Scholar PubMed PubMed Central
122. Li, M, Zhu, W, Khan, RSU, Saeed, U, Wang, R, Shi, S, et al.. Accuracy of interleukin-27 assay for the diagnosis of tuberculous pleurisy: a PRISMA-compliant meta-analysis. Medicine 2017;96:e9205. https://doi.org/10.1097/md.0000000000009205.Search in Google Scholar PubMed PubMed Central
124. Zhou, Q, Chen, YQ, Qin, SM, Tao, XN, Xin, JB, Shi, HZ. Diagnostic accuracy of T-cell interferon-gamma release assays in tuberculous pleurisy: a meta-analysis. Respirology 2011;16:473–80. https://doi.org/10.1111/j.1440-1843.2011.01941.x.Search in Google Scholar PubMed
125. Aggarwal, AN, Agarwal, R, Gupta, D, Dhooria, S, Behera, D. Interferon gamma release assays for diagnosis of pleural tuberculosis: a systematic review and meta-analysis. J Clin Microbiol 2015;53:2451–9. https://doi.org/10.1128/jcm.00823-15.Search in Google Scholar PubMed PubMed Central
126. Zhang, X, Meng, Q, Miao, R, Huang, P. The diagnostic value of T cell spot test and adenosine deaminase in pleural effusion for tuberculous pleurisy: a systematic review and meta-analysis. Tuberculosis 2022;135:102223. https://doi.org/10.1016/j.tube.2022.102223.Search in Google Scholar PubMed
127. Du, J, Shao, MM, Yi, FS, Huang, ZY, Qiao, X, Chen, QY, et al.. Interleukin 32 as a potential marker for diagnosis of tuberculous pleural effusion. Microbiol Spectr 2022;10:e0255321. https://doi.org/10.1128/spectrum.02553-21.Search in Google Scholar PubMed PubMed Central
128. Qiao, X, Shao, MM, Yi, FS, Shi, HZ. Complement component C1q as an emerging biomarker for the diagnosis of tuberculous pleural effusion. Front Microbiol 2021;12:765471. https://doi.org/10.3389/fmicb.2021.765471.Search in Google Scholar PubMed PubMed Central
129. Chung, W, Jung, Y, Lee, K, Park, J, Sheen, S, Park, K. CXCR3 ligands in pleural fluid as markers for the diagnosis of tuberculous pleural effusion. Int J Tubercul Lung Dis 2017;21:1300–6. https://doi.org/10.5588/ijtld.17.0232.Search in Google Scholar PubMed
130. Tong, X, Lu, H, Yu, M, Wang, G, Han, C, Cao, Y. Diagnostic value of interferon-gamma-induced protein of 10kDa for tuberculous pleurisy: a meta-analysis. Clin Chim Acta 2017;471:143–9. https://doi.org/10.1016/j.cca.2017.05.034.Search in Google Scholar PubMed
131. Yan, Z, Wang, H, Zheng, WQ, Hu, ZD. Pleural fluid soluble interleukin-2 receptor as a biomarker for the diagnosis of tuberculosis pleural effusion: a systematic review and meta-analysis. J Trop Med 2022;2022:4348063. https://doi.org/10.1155/2022/4348063.Search in Google Scholar PubMed PubMed Central
132. Tyagi, S, Sharma, N, Tyagi, JS, Haldar, S. Challenges in pleural tuberculosis diagnosis: existing reference standards and nucleic acid tests. Future Microbiol 2017;12:1201–18. https://doi.org/10.2217/fmb-2017-0028.Search in Google Scholar PubMed
134. Falguera, M, Carratala, J, Bielsa, S, Garcia-Vidal, C, Ruiz-Gonzalez, A, Chica, I, et al.. Predictive factors, microbiology and outcome of patients with parapneumonic effusion. Eur Respir J 2011;38:1173–9. https://doi.org/10.1183/09031936.00000211.Search in Google Scholar PubMed
135. Chalmers, JD, Singanayagam, A, Murray, MP, Scally, C, Fawzi, A, Hill, AT. Risk factors for complicated parapneumonic effusion and empyema on presentation to hospital with community-acquired pneumonia. Thorax 2009;64:592–7. https://doi.org/10.1136/thx.2008.105080.Search in Google Scholar PubMed
138. Heffner, JE, Brown, LK, Barbieri, C, DeLeo, JM. Pleural fluid chemical analysis in parapneumonic effusions. A meta-analysis. Am J Respir Crit Care Med 1995;151:1700–8. https://doi.org/10.1164/ajrccm.151.6.7767510.Search in Google Scholar PubMed
139. Bedawi, EO, Ricciardi, S, Hassan, M, Gooseman, MR, Asciak, R, Castro-Anon, O, et al.. ERS/ESTS statement on the management of pleural infection in adults. Eur Respir J 2022, In press. https://doi.org/10.1183/13993003.01062-2022. 36229045.Search in Google Scholar PubMed
140. Cheng, DS, Rodriguez, RM, Rogers, J, Wagster, M, Starnes, DL, Light, RW. Comparison of pleural fluid pH values obtained using blood gas machine, pH meter, and pH indicator strip. Chest 1998;114:1368–72. https://doi.org/10.1378/chest.114.5.1368.Search in Google Scholar PubMed
141. Lesho, EP, Roth, BJ. Is pH paper an acceptable, low-cost alternative to the blood gas analyzer for determining pleural fluid pH? Chest 1997;112:1291–2. https://doi.org/10.1378/chest.112.5.1291.Search in Google Scholar PubMed
143. Rahman, NM, Mishra, EK, Davies, HE, Davies, RJ, Lee, YC. Clinically important factors influencing the diagnostic measurement of pleural fluid pH and glucose. Am J Respir Crit Care Med 2008;178:483–90. https://doi.org/10.1164/rccm.200801-062oc.Search in Google Scholar
144. Zavorsky, GS. The stability of pleural fluid pH under slushed ice and room temperature conditions. Clin Chem Lab Med 2023;61:e22–4. https://doi.org/10.1515/cclm-2022-0669.Search in Google Scholar PubMed
145. Kim, JW, Yang, IA, Oh, EA, Rhyoo, YG, Jang, YH, Ryang, DW, et al.. C-reactive protein, sialic acid and adenosine deaminase levels in serum and pleural fluid from patients with pleural effusion. Korean J Intern Med 1988;3:122–7. https://doi.org/10.3904/kjim.19126.96.36.199.Search in Google Scholar PubMed PubMed Central
146. Porcel, JM, Vives, M, Cao, G, Bielsa, S, Ruiz-Gonzalez, A, Martinez-Iribarren, A, et al.. Biomarkers of infection for the differential diagnosis of pleural effusions. Eur Respir J 2009;34:1383–9. https://doi.org/10.1183/09031936.00197208.Search in Google Scholar PubMed
147. Porcel, JM, Bielsa, S, Esquerda, A, Ruiz-Gonzalez, A, Falguera, M. Pleural fluid C-reactive protein contributes to the diagnosis and assessment of severity of parapneumonic effusions. Eur J Intern Med 2012;23:447–50. https://doi.org/10.1016/j.ejim.2012.03.002.Search in Google Scholar PubMed
148. Zou, MX, Zhou, RR, Wu, WJ, Zhang, NJ, Liu, WE, Fan, XG. The use of pleural fluid procalcitonin and C-reactive protein in the diagnosis of parapneumonic pleural effusions: a systemic review and meta-analysis. Am J Emerg Med 2012;30:1907–14. https://doi.org/10.1016/j.ajem.2012.04.004.Search in Google Scholar PubMed
149. Li, D, Shen, Y, Qin, J, Wan, C, Zeng, N, Chen, L, et al.. Diagnostic performance of C-reactive protein for parapneumonic pleural effusion: a meta-analysis. Ann Transl Med 2019;7:1. https://doi.org/10.21037/atm.2018.11.44.Search in Google Scholar PubMed PubMed Central
150. Skouras, V, Boultadakis, E, Nikoulis, D, Polychronopoulos, V, Daniil, Z, Kalomenidis, I, et al.. Prognostic value of C-reactive protein in parapneumonic effusions. Respirology 2012;17:308–14. https://doi.org/10.1111/j.1440-1843.2011.02078.x.Search in Google Scholar PubMed
151. Porcel, JM, Galindo, C, Esquerda, A, Trujillano, J, Ruiz-Gonzalez, A, Falguera, M, et al.. Pleural fluid interleukin-8 and C-reactive protein for discriminating complicated non-purulent from uncomplicated parapneumonic effusions. Respirology 2008;13:58–62. https://doi.org/10.1111/j.1440-1843.2007.01189.x.Search in Google Scholar PubMed
152. Bielsa, S, Valencia, H, Ruiz-Gonzalez, A, Esquerda, A, Porcel, JM. Serum C-reactive protein as an adjunct for identifying complicated parapneumonic effusions. Lung 2014;192:577–81. https://doi.org/10.1007/s00408-014-9606-5.Search in Google Scholar PubMed
153. Kogan, Y, Sabo, E, Odeh, M. Diagnostic value of C-reactive protein in discrimination between uncomplicated and complicated parapneumonic effusion. Diagnostics 2020;10:829. https://doi.org/10.3390/diagnostics10100829.Search in Google Scholar PubMed PubMed Central
156. Kamat, IS, Ramachandran, V, Eswaran, H, Guffey, D, Musher, DM. Procalcitonin to distinguish viral from bacterial pneumonia: a systematic review and meta-analysis. Clin Infect Dis 2020;70:538–42. https://doi.org/10.1093/cid/ciz545.Search in Google Scholar PubMed
157. Cong, S, Ma, T, Di, X, Tian, C, Zhao, M, Wang, K. Diagnostic value of neutrophil CD64, procalcitonin, and interleukin-6 in sepsis: a meta-analysis. BMC Infect Dis 2021;21:384. https://doi.org/10.1186/s12879-021-06064-0.Search in Google Scholar PubMed PubMed Central
158. Dixon, G, Lama-Lopez, A, Bintcliffe, OJ, Morley, AJ, Hooper, CE, Maskell, NA. The role of serum procalcitonin in establishing the diagnosis and prognosis of pleural infection. Respir Res 2017;18:30. https://doi.org/10.1186/s12931-017-0501-5.Search in Google Scholar PubMed PubMed Central
159. Jose, MES, Valdes, L, Vizcaino, LH, Mora, T, Pose, A, Soneira, E, et al.. Procalcitonin, C-reactive protein, and cell counts in the diagnosis of parapneumonic pleural effusions. J Invest Med 2010;58:971–6. https://doi.org/10.2310/jim.0b013e3181f88648.Search in Google Scholar
160. Lee, SH, Lee, EJ, Min, KH, Hur, GY, Lee, SY, Kim, JH, et al.. Procalcitonin as a diagnostic marker in differentiating parapneumonic effusion from tuberculous pleurisy or malignant effusion. Clin Biochem 2013;46:1484–8. https://doi.org/10.1016/j.clinbiochem.2013.03.018.Search in Google Scholar PubMed
161. He, C, Wang, B, Li, D, Xu, H, Shen, Y. Performance of procalcitonin in diagnosing parapneumonic pleural effusions: a clinical study and meta-analysis. Medicine 2017;96:e7829. https://doi.org/10.1097/md.0000000000007829.Search in Google Scholar
162. Lin, MC, Chen, YC, Wu, JT, Ko, YC, Wang, CC. Diagnostic and prognostic values of pleural fluid procalcitonin in parapneumonic pleural effusions. Chest 2009;136:205–11. https://doi.org/10.1378/chest.08-1134.Search in Google Scholar PubMed
163. Determann, RM, Achouiti, AA, El Solh, AA, Bresser, P, Vijfhuizen, J, Spronk, PE, et al.. Infectious pleural effusions can be identified by sTREM-1 levels. Respir Med 2010;104:310–5. https://doi.org/10.1016/j.rmed.2009.09.008.Search in Google Scholar PubMed
164. Summah, H, Tao, LL, Zhu, YG, Jiang, HN, Qu, JM. Pleural fluid soluble triggering receptor expressed on myeloid cells-1 as a marker of bacterial infection: a meta-analysis. BMC Infect Dis 2011;11:280. https://doi.org/10.1186/1471-2334-11-280.Search in Google Scholar PubMed PubMed Central
165. Xirouchaki, N, Tzanakis, N, Bouros, D, Kyriakou, D, Karkavitsas, N, Alexandrakis, M, et al.. Diagnostic value of interleukin-1alpha, interleukin-6, and tumor necrosis factor in pleural effusions. Chest 2002;121:815–20. https://doi.org/10.1378/chest.121.3.815.Search in Google Scholar PubMed
166. San Jose, ME, Valdes, L, Gonzalez-Barcala, FJ, Vizcaino, L, Garrido, M, Sanmartin, A, et al.. Diagnostic value of proinflammatory interleukins in parapneumonic effusions. Am J Clin Pathol 2010;133:884–91. https://doi.org/10.1309/ajcpb67pykvrvppr.Search in Google Scholar
167. Watanabe, N, Ishii, T, Kita, N, Kanaji, N, Nakamura, H, Nanki, N, et al.. The usefulness of pleural fluid presepsin, C-reactive protein, and procalcitonin in distinguishing different causes of pleural effusions. BMC Pulm Med 2018;18:176. https://doi.org/10.1186/s12890-018-0740-3.Search in Google Scholar PubMed PubMed Central
168. Boultadakis, V, Skouras, V, Makris, D, Damianaki, A, Nikoulis, DJ, Kiropoulos, T, et al.. Serum amyloid alpha in parapneumonic effusions. Mediat Inflamm 2011;2011:237638. https://doi.org/10.1155/2011/237638.Search in Google Scholar PubMed PubMed Central
169. Sharma, A, Agrawal, A, Sindhwani, G, Sharma, A, Tomo, S, Charan, J, et al.. Efficacy of procalcitonin and pentraxin-3 as early biomarkers for differential diagnosis of pleural effusions. Pleura Peritoneum 2021;6:83–90. https://doi.org/10.1515/pp-2021-0111.Search in Google Scholar PubMed PubMed Central
170. Arnold, DT, Hamilton, FW, Elvers, KT, Frankland, SW, Zahan-Evans, N, Patole, S, et al.. Pleural fluid suPAR levels predict the need for invasive management in parapneumonic effusions. Am J Respir Crit Care Med 2020;201:1545–53. https://doi.org/10.1164/rccm.201911-2169oc.Search in Google Scholar PubMed PubMed Central
171. Gruson, D, Helleputte, T, Rousseau, P, Gruson, D. Data science, artificial intelligence, and machine learning: opportunities for laboratory medicine and the value of positive regulation. Clin Biochem 2019;69:1–7. https://doi.org/10.1016/j.clinbiochem.2019.04.013.Search in Google Scholar PubMed
172. Saberi-Karimian, M, Khorasanchi, Z, Ghazizadeh, H, Tayefi, M, Saffar, S, Ferns, GA, et al.. Potential value and impact of data mining and machine learning in clinical diagnostics. Crit Rev Clin Lab Sci 2021;58:275–96. https://doi.org/10.1080/10408363.2020.1857681.Search in Google Scholar PubMed
173. Khemasuwan, D, Sorensen, J, Griffin, DC. Predictive variables for failure in administration of intrapleural tissue plasminogen activator/deoxyribonuclease in patients with complicated parapneumonic effusions/empyema. Chest 2018;154:550–6. https://doi.org/10.1016/j.chest.2018.01.037.Search in Google Scholar PubMed
174. Sexauer, R, Yang, S, Weikert, T, Poletti, J, Bremerich, J, Roth, JA, et al.. Automated detection, segmentation, and classification of pleural effusion from computed tomography scans using machine learning. Invest Radiol 2022;57:552–9. https://doi.org/10.1097/rli.0000000000000869.Search in Google Scholar PubMed PubMed Central
175. Ren, Z, Hu, Y, Xu, L. Identifying tuberculous pleural effusion using artificial intelligence machine learning algorithms. Respir Res 2019;20:220. https://doi.org/10.1186/s12931-019-1197-5.Search in Google Scholar PubMed PubMed Central
176. Garcia-Zamalloa, A, Vicente, D, Arnay, R, Arrospide, A, Taboada, J, Castilla-Rodriguez, I, et al.. 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. PLoS One 2021;16:e0259203. https://doi.org/10.1371/journal.pone.0259203.Search in Google Scholar PubMed PubMed Central
177. Li, Y, Tian, S, Huang, Y, Dong, W. Driverless artificial intelligence framework for the identification of malignant pleural effusion. Transl Oncol 2021;14:100896. https://doi.org/10.1016/j.tranon.2020.100896.Search in Google Scholar PubMed PubMed Central
178. Chen, Z, Chen, K, Lou, Y, Zhu, J, Mao, W, Song, Z. Machine learning applied to near-infrared spectra for clinical pleural effusion classification. Sci Rep 2021;11:9411. https://doi.org/10.1038/s41598-021-87736-4.Search in Google Scholar PubMed PubMed Central
179. Zhao, W, Cao, XS, Han, YL, Wen, XH, Zheng, WQ, Hu, ZD. Diagnostic utility of pleural cell-free nucleic acids in undiagnosed pleural effusions. Clin Chem Lab Med 2022;60:1518–24. https://doi.org/10.1515/cclm-2022-0519.Search in Google Scholar PubMed
180. Bao, Q, Xu, Y, Ding, M, Chen, P. Identification of differentially expressed miRNAs in differentiating benign from malignant pleural effusion. Hereditas 2020;157:6. https://doi.org/10.1186/s41065-020-00119-z.Search in Google Scholar PubMed PubMed Central
181. Han, HS, Yun, J, Lim, SN, Han, JH, Lee, KH, Kim, ST, et al.. Downregulation of cell-free miR-198 as a diagnostic biomarker for lung adenocarcinoma-associated malignant pleural effusion. Int J Cancer 2013;133:645–52. https://doi.org/10.1002/ijc.28054.Search in Google Scholar PubMed
182. Tamiya, H, Mitani, A, Saito, A, Ishimori, T, Saito, M, Isago, H, et al.. Exosomal MicroRNA expression profiling in patients with lung adenocarcinoma-associated malignant pleural effusion. Anticancer Res 2018;38:6707–14. https://doi.org/10.21873/anticanres.13039.Search in Google Scholar PubMed
183. Santotoribio, JD, Cabrera-Alarcon, JL, Batalha-Caetano, P, Macher, HC, Guerrero, JM. Pleural fluid cell-free DNA in parapneumonic pleural effusion. Clin Biochem 2015;48:1003–5. https://doi.org/10.1016/j.clinbiochem.2015.07.096.Search in Google Scholar PubMed
184. Wu, KA, Wu, CC, Chen, CD, Chu, CM, Shih, LJ, Liu, YC, et al.. Proteome profiling reveals novel biomarkers to identify complicated parapneumonic effusions. Sci Rep 2017;7:4026. https://doi.org/10.1038/s41598-017-04189-4.Search in Google Scholar PubMed PubMed Central
185. Lam, CW, Law, CY. Pleural effusion lipoproteins measured by NMR spectroscopy for diagnosis of exudative pleural effusions: a novel tool for pore-size estimation. J Proteome Res 2014;13:4104–12. https://doi.org/10.1021/pr5004856.Search in Google Scholar PubMed
186. Liu, K, Jin, S, Song, Z, Jiang, L. High accuracy detection of malignant pleural effusion based on label-free surface-enhanced Raman spectroscopy and multivariate statistical analysis. Spectrochim Acta A Mol Biomol Spectrosc 2020;226:117632. https://doi.org/10.1016/j.saa.2019.117632.Search in Google Scholar PubMed
187. Shiraishi, Y, Kryukov, K, Tomomatsu, K, Sakamaki, F, Inoue, S, Nakagawa, S, et al.. Diagnosis of pleural empyema/parapneumonic effusion by next-generation sequencing. Inf Disp 2021;53:450–9. https://doi.org/10.1080/23744235.2021.1892178.Search in Google Scholar PubMed
188. Aggarwal, AN, Agarwal, R, Dhooria, S, Prasad, KT, Sehgal, IS, Muthu, V. Comparative accuracy of pleural fluid unstimulated interferon-gamma and adenosine deaminase for diagnosing pleural tuberculosis: a systematic review and meta-analysis. PLoS One 2021;16:e0253525. https://doi.org/10.1371/journal.pone.0253525.Search in Google Scholar PubMed PubMed Central
189. Aggarwal, AN, Agarwal, R, Dhooria, S, Prasad, KT, Sehgal, IS, Muthu, V. Unstimulated pleural fluid interferon-gamma for diagnosis of tuberculous pleural effusion: a systematic review and meta-analysis. J Clin Microbiol 2021;59:e02112–20. https://doi.org/10.1128/jcm.02112-20.Search in Google Scholar PubMed PubMed Central
190. Tong, X, Li, Z, Zhao, J, Liu, S, Fan, H. The value of single or combined use of pleural fluid interferon gamma release assay in the diagnosis of tuberculous pleurisy. Trop Med Int Health 2021;26:1356–66. https://doi.org/10.1111/tmi.13659.Search in Google Scholar PubMed
191. Gu, P, Huang, G, Chen, Y, Zhu, C, Yuan, J, Sheng, S. Diagnostic utility of pleural fluid carcinoembryonic antigen and CYFRA 21-1 in patients with pleural effusion: a systematic review and meta-analysis. J Clin Lab Anal 2007;21:398–405. https://doi.org/10.1002/jcla.20208.Search in Google Scholar PubMed PubMed Central
192. Liang, QL, Shi, HZ, Qin, XJ, Liang, XD, Jiang, J, Yang, HB. Diagnostic accuracy of tumour markers for malignant pleural effusion: a meta-analysis. Thorax 2008;63:35–41. https://doi.org/10.1136/thx.2007.077958.Search in Google Scholar PubMed
193. Wu, Q, Li, M, Zhang, S, Chen, L, Gu, X, Xu, F. Clinical diagnostic utility of CA 15-3 for the diagnosis of malignant pleural effusion: a meta-analysis. Exp Ther Med 2015;9:232–8. https://doi.org/10.3892/etm.2014.2039.Search in Google Scholar PubMed PubMed Central
© 2022 Walter de Gruyter GmbH, Berlin/Boston