[Contribution of pleural fluid analysis to the diagnosis of pleural effusion]

Identification of Pleural Response Patterns: A Cluster Analysis

BACKGROUND

Pleural effusion occurs as a response of the pleura to aggressions. The pleura reacts differently according to the type of injury. However, pleural reactions have not yet been characterized. The objective of this study was to identify homogeneous clusters of patients based on the analytical characteristics of their pleural fluid and identify pleural response patterns.

METHODS

A prospective study was conducted of consecutive patients seen in our unit for pleural effusion. Principal component and cluster analyses were carried out to identify pleural response patterns based on a combination of pleural fluid biomarkers.

RESULTS

A total of 1613 patients were grouped into six clusters, namely: cluster 1 (10.5% of the cohort, primarily composed of patients with malignant pleural effusions); cluster 2 (17.4%, pleural effusions with inflammatory biomarkers); cluster 3 (16.1%, primarily composed of patients with infectious pleural effusions); cluster 4 (2.5%, a subcluster of cluster 3, superinfectious effusions); cluster 5 (23.4%, paucicellular pleural effusions); and cluster 6 (30.1%, miscellaneous). Significant differences were observed across clusters in terms of the analytical characteristics of PF (p<0.001 for all), age (p<0.001), and gender (p=0.016). A direct relationship was found between the type of cluster and the etiology of pleural effusion.

CONCLUSION

Pleural response is heterogeneous. The pleura may respond differently to the same etiology or similarly to different etiologies, which hinders diagnosis of pleural effusion.

Advances in pleural effusion diagnostics

Introduction: Pleural effusion is a common clinical problem. Yet, in a significant proportion of patients (~20%), the cause of pleural effusion remains unknown. Understanding the diagnostic value of pleural fluid tests is crucial for the development of accurate diagnostic models.Areas covered: This paper provides an overview of latest advances in the diagnosis of pleural effusion based on the best evidence available.Expert opinion: For pleural fluid tests to have a good diagnostic value, it is necessary that data obtained from clinical history, physical examination, and radiological studies are correctly interpreted. Thoracentesis and pleural biopsy should always be performed under image guidance to improve its diagnostic sensitivity and prevent complications. Nucleic acid amplification tests, pleural tissue cultures, and collection of pleural fluid in blood culture bottles improve the diagnostic yield of pleural fluid cultures. Although undiagnosed pleural effusions generally have a favorable prognosis, follow-up is recommended to prevent the development of a malignant pleural effusion.

The diagnostic value of pleural fluid homocysteine in malignant pleural effusion

Background

Pleural fluid homocysteine (HCY) can be useful for diagnosis of malignant pleural effusion (MPE). There are no published studies comparing the diagnostic accuracy of HCY with other tumour markers in pleural fluid for diagnosis of MPE. The aim was to compare the accuracy of HCY with that of carcinoembryonic antigen (CEA), cancer antigen (CA) 15.3, CA19.9 and CA125 in pleural fluid and to develop a probabilistic model using these biomarkers to differentiate benign (BPE) from MPE.

Methods

Patients with pleural effusion were randomly included. HCY, CEA, CA15.3, CEA19.9 and CA125 were quantified in pleural fluid. Patients were classified into two groups: MPE or BPE. By applying logistic regression analysis, a multivariate probabilistic model was developed using pleural fluid biomarkers. The diagnostic accuracy was determined by receiver operating characteristic (ROC) curves and calculating the area under the curve (AUC).

Results

Population of study comprised 133 patients (72 males and 61 females) aged between 1 and 96 years (median = 70 years), 81 BPE and 52 MPE. The logistic regression analysis included HCY (p<0.0001) and CEA (p = 0.0022) in the probabilistic model and excluded the other tumour markers. The probabilistic model was: HCY+CEA = Probability(%) = 100×(1+e^-z)^-1, where Z = 0.5471×[HCY]+0.3846×[CEA]–8.2671. The AUCs were 0.606, 0.703, 0.778, 0.800, 0.846 and 0.948 for CA125, CA19.9, CEA, CA15.3, HCY and HCY+CEA, respectively.

Conclusions

Pleural fluid HCY has higher accuracy for diagnosis of MPE than CEA, CA15.3, CA19.9 and CA125. The combination of HCY and CEA concentrations in pleural fluid significantly improves the diagnostic accuracy of the test.

PD-L1 detection in histology specimens and matched pleural fluid cell blocks of patients with NSCLC

BACKGROUND AND OBJECTIVE

Analysis of programmed death ligand-1 (PD-L1) in tumour samples is necessary to identify candidates for anti-PD-L1/PD-L1 therapy. Because PD-L1 is evaluated by immunohistochemistry (IHC), an adequate amount of tumour tissue is a prerequisite for PD-L1 testing. To examine whether pleural fluid might be an alternative to biopsy/resection specimens for IHC evaluation of PD-L1 in patients with non-small cell lung carcinoma (NSCLC), we compared PD-L1 by IHC between histological specimens and matched pleural fluid.

METHODS

A retrospective cohort study of patients with NSCLC who underwent core biopsy of a lung mass/surgical resection with PD-L1 IHC and had a pleural fluid cell block (CB) available for PD-L1 staining was conducted. PD-L1 was categorized as negative (PD-L1 in <1% of tumour cells), moderately positive (PD-L1 in ≥1% to <50%), strongly positive (PD-L1 ≥ 50) or inadequate for PD-L1 testing (<100 tumour cells in the CB). Weighted Cohen's kappa was calculated to evaluate the agreement between PD-L1 on biopsy/resection specimen and pleural fluid for variables with more than two categories.

RESULTS

Of the 115 patients included in this study, 82 (71.3%) had at least 100 tumour cells and were included in the analysis. Of these, 80 (97.6%) had adenocarcinoma. For PD-L1 of histological specimens versus pleural fluid categorized as negative, moderately positive or strongly positive, the weighted kappa statistic was 0.76 (95% CI: 0.64-0.88), and the concordance was 0.78 (95% CI: 0.68-0.86).

CONCLUSION

Correlation and concordance are high between PD-L1 in histological specimens and matched pleural fluid. Evaluation of PD-L1 in pleural fluid should be considered in patients unable to undergo histological biopsies.

[Thoracentesis in Primary Care]

Thoracentesis is a simple test with few complications that provides relevant information in the diagnosis of a pleural effusion, through a correct interpretation of the pleural fluid analysis. An interesting initiative would be to incorporate this technique by those Primary Care teams that treat serious and complex patients, with difficulties in moving to specialised centres far from their homes. In this context, a good knowledge of the diagnostic possibilities offered by the pleural fluid analysis could be very useful in the hands of well trained staff to establish the aetiology of a pleural effusion and be able to initiate, as quickly as possible, its treatment. This article aims to contribute to this, by suggesting guidelines on how a simple technique can provide relevant information in order to determine the aetiology of pleural effusion, and which could be implemented within a given Primary Care framework.

Label-free microfluidic chip for the identification of mesothelial cell clusters in pleural effusion

The detection of tumor cells and clusters in pleural effusion assists in the diagnosis of lung cancer. The proportion of tumor cells and clusters to the total number of cells in each patient varies substantially due to individual differences and the severity of the disease. The identification of one tumor cell or cluster from a large number of pleural effusions is the main challenge for hydrothorax tumor cell detection techniques. In the present study, by using A549 lung cancer and Met-5A mesothelial cell lines, a label-free microfluidic chip based on cell cluster size was designed. By setting the parameters of the chip, individual cells and clusters were able to enter different microfluidic channels. Subsequent to non-specific staining, the recovered components were stained using acridine orange (AO). A charge-coupled device camera was used to captured images of the cell, and the features of these cells were analyzed in their R and G channels using Matlab software to establish the characteristics and finally differentiate between the tumor and non-tumor cell or clusters. According to the results, when inlet A and B were under a velocity of 10 and 8.5 ml/h, respectively, the tumor cell clusters were successfully collected through microfluidic channels III–V, with a recovery rate of ~80%. Subsequent to staining with AO, the feature values in the R and G channels were identified, and initial differentiation was achieved. The present study combined the microfluidic chip, which is based on cluster size, with a computer identification method for pleural effusion. The successful differentiation of tumor cell clusters from non-tumor clusters provides the basis for the identification of tumor clusters in hydrothorax.

Diagnostic value of tumour markers in pleural effusions

Introduction

We investigated whether tumour markers carcinoembryonic antigen (CEA), neuron-specific enolase (NSE), cancer antigen 125 (CA-125), and cytokeratin 19 fragment (CYFRA 21-1) in pleural effusions and serum can be used to distinguish pleural effusion aetiology.

Materials and methods

During the first thoracentesis, we measured pleural fluid and serum tumour marker concentrations and calculated the pleural fluid/serum ratio for patients diagnosed with pleural effusion, using electrochemiluminescence immunoassays. Receiver operating characteristic (ROC) analysis was carried out and the Hanley and McNeil method was used to test the significance of the difference between the areas under ROC curves (AUCs). In order to detect which tumour marker best discriminates between malignant and non-malignant pleural effusions and to establish the predictive value of those markers, discriminant function analysis (DFA) and logistic regression analysis were utilized.

Results

Serum tumour markers CYFRA 21-1 and NSE as well as pleural NSE were good predictors of pleural effusion malignancy and their combined model was found statistically significant (Chi-square = 28.415, P < 0.001). Respective ROC analysis showed significant discrimination value of the combination of these three markers (AUC = 0.79).

Conclusions

Serum markers showed superiority to pleural fluid markers in determining pleural fluid aetiology. Serum CYFRA 21-1 and NSE concentrations as well as pleural fluid NSE values had the highest clinical value in differentiating between malignant and non-malignant pleural effusions. The combination of these three markers produced a significant model to resolve pleural effusion aetiology.

Identification of differential expressed PE exosomal miRNA in lung adenocarcinoma, tuberculosis, and other benign lesions

Pleural effusion (PE) is a common clinical complication of many pulmonary and systemic diseases, including lung cancer and tuberculosis. Nevertheless, there is no clinical effective biomarker to identify the cause of PE. We attempted to investigate differential expressed exosomal miRNAs in PEs of lung adenocarcinoma (APE), tuberculous (TPE), and other benign lesions (NPE) by using deep sequencing and quantitative polymerase chain reaction (qRT-PCR). As a result, 171 differentiated miRNAs were observed in 3 groups of PEs, and 11 significantly differentiated exosomal miRNAs were validated by qRT-PCR. We identified 9 miRNAs, including miR-205-5p, miR-483-5p, miR-375, miR-200c-3p, miR-429, miR-200b-3p, miR-200a-3p, miR-203a-3p, and miR-141-3p which were preferentially represented in exosomes derived from APE when compared with TPE or NPE, while 3 miRNAs, including miR-148a-3p, miR-451a, and miR-150-5p, were differentially expressed between TPE and NPE. These different miRNAs profiles may hold promise as biomarkers for differential diagnosis of PEs with more validation based on larger cohorts.

Performance of clinical prediction rules for diagnosis of pleural tuberculosis in a high-incidence setting

OBJECTIVES

Diagnosis of pleural tuberculosis (PT) is still a challenge, particularly in resource-constrained settings. Alternative diagnostic tools are needed. We aimed at evaluating the utility of Clinical Prediction Rules (CPRs) for diagnosis of pleural tuberculosis in Peru.

METHODS

We identified CPRs for diagnosis of PT through a structured literature search. CPRs using high-complexity tests, as defined by the FDA, were excluded. We applied the identified CPRs to patients with pleural exudates attending two third-level hospitals in Lima, Peru, a setting with high incidence of tuberculosis. Besides pleural fluid analysis, patients underwent closed pleural biopsy for reaching a final diagnosis through combining microbiological and histopathological criteria. We evaluated the performance of the CPRs against this composite reference standard using classic indicators of diagnostic test validity.

RESULTS

We found 15 eligible CPRs, of which 12 could be validated. Most included ADA, age, lymphocyte proportion and protein in pleural fluid as predictive findings. A total of 259 patients were included for their validation, of which 176 (67%) had PT and 50 (19%) malignant pleural effusion. The overall accuracy of the CPRs varied from 41% to 86%. Two had a positive likelihood ratio (LR) above 10, but none a negative LR below 0.1. ADA alone at a cut-off of ≥40 IU attained 87% diagnostic accuracy and had a positive LR of 6.6 and a negative LR of 0.2.

CONCLUSION

Many CPRs for PT are available. In addition to ADA alone, none of them contributes significantly to diagnosis of PT.

Homocysteine: new tumor marker in pleural fluid

There are no published studies examining the utility of total homocysteine (HCY) in pleural fluid. The aim was to measure the accuracy of pleural fluid HCY concentration for diagnosis of malignant pleural effusion (MPE). We studied pleural fluids obtained by thoracocentesis in patients with pleural effusion. Pleural fluid HCY concentration was measured by immunonephelometry using N Latex HCY reagent with monoclonal antibody in automated analyzers BNII (Siemens Diagnostics®). Patients were classified into two groups according to the etiology of pleural effusion: benign pleural effusions (BPE) and MPE. Pleural effusion was categorized as MPE if malignant cells were demonstrated in pleural fluid or pleural biopsy. The accuracy of pleural fluid HCY concentration for diagnosis of MPE was determined using receiver operating characteristic (ROC) techniques by analyzing the area under the ROC curve (AUC). We studied 89 patients with ages between 1 and 96 years old (median = 66). Forty-eight patients were BPE and 41 were MPE. Pleural fluid HCY concentration was significantly higher in patients with MPE (median = 13.70 μmol/L) than in those with BPE (median = 8.05 μmol/L). The AUC value was 0.833 (95 % confidence interval (CI) 0.739-0.903). The optimal cutoff value was 13.1 μmol/L exhibiting 56.1 % (95 % CI 39.8-71.5) sensitivity and 85.4 % (95 % CI 72.2-93.9) specificity. Pleural fluid HCY concentration showed high diagnostic accuracy to predict whether a pleural effusion is benign or malignant. Pleural fluid HCY concentration may be measured easily and quickly in automated analyzers and could be a tumor marker commonly used for diagnosis of MPE.