A retrospective study on the combined biomarkers and ratios in serum and pleural fluid to distinguish the multiple types of pleural effusion

Performance verification of a biochemical detection system for hydrothorax and ascites and clinical diagnostic accuracy evaluation of exudate and tuberculous effusion

Background

Lactate dehydrogenase (LDH), total protein (TP) and glucose (Glu) in pleural hydrothorax and ascites can be used in the diagnosis of exudate, and adenosine deaminase (ADA) can be used in the diagnosis of tuberculous effusion. However, the manufacturers do not claim that their biochemical reagents can be used to detect hydrothorax and ascites samples. Therefore, medical laboratories must conduct suitability studies on biochemical reagents for hydrothorax and ascites samples to comply with regulatory requirements for humor detection. This study aimed to verify the analytical performance and clinical diagnostic accuracy of the Mindray biochemical reagents, including LDH, TP, Glu and ADA, for hydrothorax and ascites.

Methods

The repeatability, detection limits and reference intervals of Mindray biochemical reagents (LDH, TP, Glu, ADA) in detecting hydrothorax and ascites were determined. The comparison of different measurement procedures was performed. Meanwhile, the diagnostic accuracy of LDH, TP, Glu and ADA were assessed.

Results

The quality control results of LDH, TP, Glu, and ADA were all under control. The repeatability coefficient of variation (%) of LDH, TP, Glu, and ADA were all less than 1%. The limits of blank of LDH, TP, Glu, and ADA were 0.33 U/L, 0.45 g/L, 0.00 mmol/L, and 0.04 U/L, respectively; the limits of detection were 1.57 U/L, 1.85 g/L, 0.05 mmol/L, and 0.12 U/L, respectively. Compared with the reference measurement program, the correlation coefficients of LDH, TP, Glu and ADA were 0.9931, 0.9983, 0.9996 and 0.9966, respectively; the regression equations were y=1.0082x-10.06, y=0.9965x-0.4732, y=0.9903x+0.0522 and y=1.0051x-0.0232, respectively. The reference intervals of LDH, TP, Glu, and ADA in hydrothorax and ascites were ≤198.39 U/L, ≤32.97 g/L, ≥5.03 mmol/L. and ≤11.00 U/L respectively. For differentiating between exudates and transudates, the area under the curve (AUC) of LDH, TP, and Glu were 0.913, 0.875, and 0.767, respectively; the AUC of ADA for the differential diagnosis of tuberculous and nontuberculous effusions was 0.876.

Conclusions

The LDH, TP, Glu, and ADA assays were validated for use with the Mindray BS-2800 analyzer for hydrothorax and ascites evaluation. LDH, TP, and Glu in hydrothorax and ascites are applicable to the differential diagnosis of exudates and transudates; ADA in hydrothorax and ascites can be employed to differentiate and diagnose tuberculous and nontuberculous effusions.

Clinical significance of pleural fluid lactate dehydrogenase/adenosine deaminase ratio in the diagnosis of tuberculous pleural effusion

BACKGROUND

Pleural fluid is one of the common complications of thoracic diseases, and tuberculous pleural effusion (TPE) is the most common cause of pleural effusion in TB-endemic areas and the most common type of exudative pleural effusion in China. In clinical practice, distinguishing TPE from pleural effusion caused by other reasons remains a relatively challenging issue. The objective of present study was to explore the clinical significance of the pleural fluid lactate dehydrogenase/adenosine deaminase ratio (pfLDH/pfADA) in the diagnosis of TPE.

METHODS

The clinical data of 618 patients with pleural effusion were retrospectively collected, and the patients were divided into 3 groups: the TPE group (412 patients), the parapneumonic pleural effusion (PPE) group (106 patients), and the malignant pleural effusion (MPE) group (100 patients). The differences in the ratios of pleural effusion-related and serology-related indicators were compared among the three groups, and receiver operating characteristic curves were drawn to analyze the sensitivity and specificity of the parameter ratios of different indicators for the diagnosis of TPE.

RESULTS

The median serum ADA level was higher in the TPE group (13 U/L) than in the PPE group (10 U/L, P < 0.01) and MPE group (10 U/L, P < 0.001). The median pfADA level in the TPE group was 41 (32, 52) U/L; it was lowest in the MPE group at 9 (7, 12) U/L and highest in the PPE group at 43 (23, 145) U/L. The pfLDH level in the PPE group was 2542 (1109, 6219) U/L, which was significantly higher than that in the TPE group 449 (293, 664) U/L. In the differential diagnosis between TPE and non-TPE, the AUC of pfLDH/pfADA for diagnosing TPE was the highest at 0.946 (0.925, 0.966), with an optimal cutoff value of 23.20, sensitivity of 93.9%, specificity of 87.0%, and Youden index of 0.809. In the differential diagnosis of TPE and PPE, the AUC of pfLDH/pfADA was the highest at 0.964 (0.939, 0.989), with an optimal cutoff value of 24.32, sensitivity of 94.6%, and specificity of 94.4%; this indicated significantly better diagnostic efficacy than that of the single index of pfLDH. In the differential diagnosis between TPE and MPE, the AUC of pfLDH/pfADA was 0.926 (0.896, 0.956), with a sensitivity of 93.4% and specificity of 80.0%; this was not significantly different from the diagnostic efficacy of pfADA.

CONCLUSIONS

Compared with single biomarkers, pfLDH/pfADA has higher diagnostic value for TPE and can identify patients with TPE early, easily, and economically.

Diagnostic Accuracy of Lactate Dehydrogenase/Adenosine Deaminase Ratio in Differentiating Tuberculous and Parapneumonic Effusions: A Systematic Review

BACKGROUND

Tuberculous pleural effusion (TPE) and parapneumonic effusion (PPE) are often difficult to differentiate owing to the overlapping clinical features. Observational studies demonstrate that the ratio of lactate dehydrogenase to adenosine deaminase (LDH/ADA) is lower in TPE compared to PPE, but integrated analysis is warranted.

METHODS

We conducted a systematic review to evaluate the diagnostic accuracy of the LDH/ADA ratio in differentiating TPE and PPE. We explored the PubMed and Scopus databases for studies evaluating the LDH/ADA ratio in differentiating TPE and PPE.

RESULTS

From a yield of 110 studies, five were included for systematic review. The cutoff value for the LDH/ADA ratio in TPE ranged from <14.2 to <25. The studies demonstrated high heterogeneity, precluding meta-analysis. Quality Assessment of Diagnostic Accuracy Studies Tool 2 assessment revealed a high risk of bias in terms of patient selection and index test.

CONCLUSION

LDH/ADA ratio is a potentially useful parameter to differentiate between TPE and PPE. Based on the limited data, we recommend an LDH/ADA ratio cutoff value of <15 in differentiating TPE and PPE. However, more rigorous studies are needed to further validate this recommendation.

Biomarkers for distinguishing tuberculous pleural effusion from non-tuberculosis effusion: a retrospective study

BACKGROUND

Pleural effusion (PE) is a common clinical feature that presents a diagnostic challenge for clinicians. In this retrospective study, we aimed to assess the biomarkers, ratios, and multiple indicators in serum and Pleural effusion for the differential diagnosis of tuberculous pleural effusion (TPE) from non-tuberculosis effusion (non-TPE).

METHODS

The participants, who were divided into two groups: TPE and non-TPE (MPE and PPE), from Ningbo First Hospital, were incorporated in this study. The clinical and laboratory features were collected and analyzed using logistic regression analysis. Twelve biomarkers and their ratios in serum and PE were investigated for TPE versus non-TPE. Additionally, the value of multiple indicators for joint diagnosis was estimated.

RESULTS

Biomarkers and ratios showed good diagnostic performance. The five variables including Serum ADA, IGRA, Effusion ADA, Effusion ADA/Serum ADA and Effusion LDH/Effusion ADA were identified as valuable parameters for differential diagnosis of TPE from non-TPE. The combined diagnosis of the five indexes yielded the highest diagnostic accuracy for TPE with an AUC (0.919), sensitivity (90.30%), and specificity (94.50%).

CONCLUSIONS

The biomarkers and ratios demonstrated strong diagnostic performance, and the utilization of multiple indicators for joint diagnosis can improve the diagnostic efficacy of tuberculous pleurisy.

Construction of a multi-classified decision tree model for identifying malignant pleural effusion and tuberculous pleural effusion

OBJECTIVE

Pleural effusion (PE) is a common clinical complication associated with various disorders. We aimed to utilize laboratory variables and their corresponding ratios in serum and PE for the differential diagnosis of multiple types of PE based on a decision tree (DT) algorithm.

METHODS

A total of 1435 untreated patients with PE admitted to The First Affiliated Hospital of Ningbo University were enrolled. The demographic and laboratory variables were collected and compared. The receiver operating characteristic curve was used to select important variables for diagnosing malignant pleural effusion (MPE) or tuberculous pleural effusion (TPE) and included in the DT model. The data were divided into the training set and the test set at a ratio of 7:3. The training data was used to develop the DT model, and the test data was for evaluating the model. Independent data was collected as external validation.

RESULTS

Three PE indicators (carcinoembryonic antigen, adenosine deaminase [ADA], and total protein), two serum indicators (neuron-specific enolase and cytokeratin 19 fragments), and two ratios [high-sensitivity C-reactive protein (hsCRP)/ PE lymphocyte and hsCRP/PE ADA] were used to construct the DT model. The area under the curve (AUC), sensitivity, and specificity for diagnosing MPE were 0.963, 84.0%, 91.6% in the training set, 0.976, 84.1%, 88.6% in the test set, and 0.955,83.3%, 86.7% in the external validation set. The AUC, sensitivity, and specificity of diagnosing TPE were 0.898, 86.8%, 92.3% in the training set, 0.888, 88.8%, 92.7% in the test set, and 0.778, 84.8%, 94.3% in the external validation set.

CONCLUSION

The DT model showed good diagnostic efficacy and could be applied for the differential diagnosis of MPE and TPE in clinical settings.

Diagnosis of malignant pleural effusion with combinations of multiple tumor markers: A comparison study of five machine learning models

BACKGROUND

To evaluate the diagnostic value of combinations of tumor markers carcinoembryonic antigen (CEA), carbohydrate antigen (CA) 125, CA153, and CA19-9 in identifying malignant pleural effusion (MPE) from non-malignant pleural effusion (non-MPE) using machine learning, and compare the performance of popular machine learning methods.

METHODS

A total of 319 samples were collected from patients with pleural effusion in Beijing and Wuhan, China, from January 2018 to June 2020. Five machine learning methods including Logistic regression, extreme gradient boosting (XGBoost), Bayesian additive regression tree, random forest, and support vector machine were applied to evaluate the diagnostic performance. Sensitivity, specificity, Youden's index, and the area under the receiver operating characteristic curve (AUC) were used to evaluate the performance of different diagnostic models.

RESULTS

For diagnostic models with a single tumor marker, the model using CEA, constructed by XGBoost, performed best (AUC = 0.895, sensitivity = 0.80), and the model with CA153, also by XGBoost, showed the largest specificity 0.98. Among all combinations of tumor markers, the combination of CEA and CA153 achieved the best performance (AUC = 0.921, sensitivity = 0.85) in identifying MPE under the diagnostic model constructed by XGBoost.

CONCLUSIONS

Diagnostic models for MPE with a combination of multiple tumor markers outperformed the models with a single tumor marker, particularly in sensitivity. Using machine learning methods, especially XGBoost, could comprehensively improve the diagnostic accuracy of MPE.

Diagnostic accuracy of pleural fluid lactate dehydrogenase to adenosine deaminase ratio for tuberculous pleural effusion: an analysis of two cohorts

Background

This study aimed to evaluate the diagnostic accuracy of pleural fluid (PF) lactate dehydrogenase (LDH) to adenosine deaminase (ADA) (LDH/ADA) ratio for tuberculous pleural effusion (TPE). Especially to explore whether the LDH/ADA ratio provides added diagnostic value to ADA.

Methods

The diagnostic accuracy of PF LDH/ADA ratio and ADA for TPE was evaluated in two cohorts, named the BUFF (Biomarkers for patients with Undiagnosed pleural eFFusion) cohort (62 with TPE and 194 with non-TPE) and the SIMPLE (a Study Investigating Markers in PLeural Effusion) cohort (33 with TPE and 177 with non-TPE). Receiver operating characteristic (ROC) curve and decision curve were used to measure the diagnostic accuracy of the PF LDH/ADA ratio. The added diagnostic value of the LDH/ADA ratio to ADA was evaluated with net reclassification improvement (NRI) and integrated discrimination improvement (IDI).

Results

The area under the ROC curves (AUCs) of PF ADA and LDH/ADA ratio in the BUFF cohort were 0.76 and 0.74, respectively. In the SIMPLE cohort, the AUCs of PF ADA and LDH/ADA ratio were 0.80 and 0.85, respectively. The decision curves of PF LDH/ADA and ADA were close in both the BUFF and SIMPLE cohorts. The NRI and IDI analyses did not reveal any added diagnostic value of LDH/ADA to ADA.

Conclusions

PF LDH/ADA ratio has moderate diagnostic accuracy for TPE. It does not provide added diagnostic value beyond ADA. The current evidence does not support LDH/ADA ratio for diagnosing TPE.

Development and Validation of a Scoring System for Early Diagnosis of Malignant Pleural Effusion Based on a Nomogram

Background

The diagnostic value of clinical and laboratory features to differentiate between malignant pleural effusion (MPE) and benign pleural effusion (BPE) has not yet been established.

Objectives

The present study aimed to develop and validate the diagnostic accuracy of a scoring system based on a nomogram to distinguish MPE from BPE.

Methods

A total of 1,239 eligible patients with PE were recruited in this study and randomly divided into a training set and an internal validation set at a ratio of 7:3. Logistic regression analysis was performed in the training set, and a nomogram was developed using selected predictors. The diagnostic accuracy of an innovative scoring system based on the nomogram was established and validated in the training, internal validation, and external validation sets (n = 217). The discriminatory power and the calibration and clinical values of the prediction model were evaluated.

Results

Seven variables [effusion carcinoembryonic antigen (CEA), effusion adenosine deaminase (ADA), erythrocyte sedimentation rate (ESR), PE/serum CEA ratio (CEA ratio), effusion carbohydrate antigen 19-9 (CA19-9), effusion cytokeratin 19 fragment (CYFRA 21-1), and serum lactate dehydrogenase (LDH)/effusion ADA ratio (cancer ratio, CR)] were validated and used to develop a nomogram. The prediction model showed both good discrimination and calibration capabilities for all sets. A scoring system was established based on the nomogram scores to distinguish MPE from BPE. The scoring system showed favorable diagnostic performance in the training set [area under the curve (AUC) = 0.955, 95% confidence interval (CI) = 0.942-0.968], the internal validation set (AUC = 0.952, 95% CI = 0.932-0.973), and the external validation set (AUC = 0.973, 95% CI = 0.956-0.990). In addition, the scoring system achieved satisfactory discriminative abilities at separating lung cancer-associated MPE from tuberculous pleurisy effusion (TPE) in the combined training and validation sets.

Conclusions

The present study developed and validated a scoring system based on seven parameters. The scoring system exhibited a reliable diagnostic performance in distinguishing MPE from BPE and might guide clinical decision-making.