Can parapneumonic effusion be diagnosed only with pleural fluid analysis?

Machine learning-based Diagnostic model for determining the etiology of pleural effusion using Age, ADA and LDH

BACKGROUND

Classification of the etiologies of pleural effusion is a critical challenge in clinical practice. Traditional diagnostic methods rely on a simple cut-off method based on the laboratory tests. However, machine learning (ML) offers a novel approach based on artificial intelligence to improving diagnostic accuracy and capture the non-linear relationships.

METHOD

A retrospective study was conducted using data from patients diagnosed with pleural effusion. The dataset was divided into training and test set with a ratio of 7:3 with 6 machine learning algorithms implemented to diagnosis pleural effusion. Model performances were assessed by accuracy, precision, recall, F1 scores and area under the receiver operating characteristic curve (AUC). Feature importance and average prediction of age, Adenosine (ADA) and Lactate dehydrogenase (LDH) was analyzed. Decision tree was visualized.

RESULTS

A total of 742 patients were included (training cohort: 522, test cohort: 220), 397 (53.3%) diagnosed with malignant pleural effusion (MPE) and 253 (34.1%) with tuberculous pleural effusion (TPE) in the cohort. All of the 6 models performed well in the diagnosis of MPE, TPE and transudates. Extreme Gradient Boosting and Random Forest performed better in the diagnosis of the MPE, with F1 scores above 0.890, while K-Nearest Neighbors and Tabular Transformer performed better in the diagnosis of the TPE, with F1 scores above 0.870. ADA was identified as the most important feature. The ROC of machine learning model outperformed those of conventional diagnostic thresholds.

CONCLUSIONS

This study demonstrates that ML models using age, ADA, and LDH can effectively classify the etiologies of pleural effusion, suggesting that ML-based approaches may enhance diagnostic decision-making.

Two Cases of Group A Streptococcus-Induced Right Empyema: Rare Occurrences in Adult Medicine

Group A Streptococcus (GAS) empyema, though rare in adults, poses serious clinical challenges. We present two cases of GAS-induced right empyema in immunocompetent patients. Case 1 involved a 45-year-old female Chinese healthcare worker with persistent pleural effusion despite antibiotic therapy. GAS was isolated from her sputum and bronchoalveolar lavage, necessitating a treatment shift to clindamycin and co-amoxiclav. Case 2 featured a 55-year-old Filipino domestic helper exhibiting right lower chest consolidation and effusion. Thoracocentesis confirmed empyema, prompting intrapleural fibrinolytic administration. Both cases highlight the diagnostic complexity and therapeutic intricacies of adult GAS empyema, underscoring the importance of early recognition and tailored management strategies for optimal patient outcomes.

Natural antibodies targeting LPS in pleural effusions of various etiologies

Respiratory infection, cancer, and heart failure can cause abnormal accumulation of fluid in the pleural cavity. The immune responses within the cavity are orchestrated by leucocytes that reside in the serosal-associated lymphoid tissue. Natural antibodies (NAbs) are abundant in the serum (S) having a major role in systemic and mucosal immunity; however, their occurrence in pleural fluid (PF) remains an open question. Our aim herein was to detect and measure the levels of NAbs (IgM, IgG, IgA) targeting lipopolysaccharides (LPS) in both the pleural fluid and the serum of 78 patients with pleural effusions (PEs) of various etiologies. The values of anti-LPS NAb activity were extracted through a normalization step regarding the total IgM, IgG, and IgA levels, all determined by in-house ELISA. In addition, the ratios of PF/S values were analyzed further with other critical biochemical parameters from pleural fluids. Anti-LPS NAbs of all Ig classes were detected in most of the samples, while a significant increase of anti-LPS activity was observed in infectious and noninfectious compared with malignant PEs. Multivariate linear regression confirmed a negative correlation of IgM and IgA anti-LPS PF/S ratio with malignancy. Moreover, anti-LPS NAbs PF/S measurements led to increased positive and negative predictive power in ROC curves generated for the discrimination between benign and malignant PEs. Our results highlight the role of anti-LPS NAbs in the pleural cavity and demonstrate the potential translational impact that should be further explored.NEW & NOTEWORTHY Here we describe the detection and quantification of natural antibodies (NAbs) in the human pleural cavity. We show for the first time that IgM, IgG, and IgA anti-LPS natural antibodies are detected and measured in pleural effusions of infectious, noninfectious, and malignant etiologies and provide clinical correlates to demonstrate the translational impact of our findings.

Infective Pleural Effusions-A Comprehensive Narrative Review Article

Infective pleural effusions are mainly represented by parapneumonic effusions and empyema. These conditions are a spectrum of pleural diseases that are commonly encountered and carry significant mortality and morbidity rates reaching upwards of 50%. The causative etiology is usually an underlying bacterial pneumonia with the subsequent seeding of the infectious culprit and inflammatory agents to the pleural space leading to an inflammatory response and fibrin deposition. Radiographical evaluation through a CT scan or ultrasound yields high specificity and sensitivity, with features such as septations or pleural thickening indicating worse outcomes. Although microbiological yields from pleural studies are around 56% only, fluid analysis assists in both diagnosis and prognosis by evaluating pH, glucose, and other biomarkers such as lactate dehydrogenase. Management centers around antibiotic therapy for 2-6 weeks and the drainage of the infected pleural space when the effusion is complicated through tube thoracostomies or surgical intervention. Intrapleural enzymatic therapy, used to increase drainage, significantly decreases treatment failure rates, length of hospital stay, and surgical referrals but carries a risk of pleural hemorrhage. This comprehensive review article aims to define and delineate the progression of parapneumonic effusions and empyema as well as discuss pathophysiology, diagnostic, and treatment modalities with aims of broadening the generalist's understanding of such complex disease by reviewing the most recent and relevant high-quality evidence.

Differential Diagnosis of Pleural Effusion Using Machine Learning

Rationale: Differential diagnosis of pleural effusion is challenging in clinical practice. Objectives: We aimed to develop a machine learning model to classify the five common causes of pleural effusions. Methods: This retrospective study collected 49 features from clinical information, blood, and pleural fluid of adult patients who underwent diagnostic thoracentesis between October 2013 and December 2018. Pleural effusions were classified into the following five categories: transudative, malignant, parapneumonic, tuberculous, and other. The performance of five different classifiers, including multinomial logistic regression, support vector machine, random forest, extreme gradient boosting, and light gradient boosting machine (LGB), was evaluated in terms of accuracy and area under the receiver operating characteristic curve through fivefold cross-validation. Hybrid feature selection was applied to determine the most relevant features for classifying pleural effusion. Results: We analyzed 2,253 patients (training set, n = 1,459; validation set, n = 365; extra-validation set, n = 429) and found that the LGB model achieved the best performance in both validation and extra-validation sets. After feature selection, the accuracy of the LGB model with the selected 18 features was equivalent to that with all 49 features (mean ± standard deviation): 0.818 ± 0.012 and 0.777 ± 0.007 in the validation and extra-validation sets, respectively. The model's mean area under the receiver operating characteristic curve was as high as 0.930 ± 0.042 and 0.916 ± 0.044 in the validation and extra-validation sets, respectively. In our model, pleural lactate dehydrogenase, protein, and adenosine deaminase levels were the most important factors for classifying pleural effusions. Conclusions: Our LGB model showed satisfactory performance for differential diagnosis of the common causes of pleural effusions. This model could provide clinicians with valuable information regarding the major differential diagnoses of pleural diseases.

Classification of pleural effusions using deep learning visual models: contrastive-loss

Blood and fluid analysis is extensively used for classifying the etiology of pleural effusion. However, most studies focused on determining the presence of a disease. This study classified pleural effusion etiology employing deep learning models by applying contrastive-loss. Patients with pleural effusion who underwent thoracentesis between 2009 and 2019 at the Asan Medical Center were analyzed. Five different models for categorizing the etiology of pleural effusion were compared. The performance metrics were top-1 accuracy, top-2 accuracy, and micro-and weighted-AUROC. UMAP and t-SNE were used to visualize the contrastive-loss model’s embedding space. Although the 5 models displayed similar performance in the validation set, the contrastive-loss model showed the highest accuracy in the extra-validation set. Additionally, the accuracy and micro-AUROC of the contrastive-loss model were 81.7% and 0.942 in the validation set, and 66.2% and 0.867 in the extra-validation set. Furthermore, the embedding space visualization in the contrastive-loss model exhibited typical and atypical effusion results by comparing the true and false positives of the rule-based criteria. Therefore, classifying the etiology of pleural effusion was achievable using the contrastive-loss model. Conclusively, visualization of the contrastive-loss model will provide clinicians with valuable insights for etiology diagnosis by differentiating between typical and atypical disease types.