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

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.

Biological and cytological-morphological assessment of tuberculous pleural effusions

AIM

Tuberculosis (TB) came back in the top of causes for infectious disease-related deaths and its pleural involvement is still in the top two extrapulmonary sites. The authors continued their studies on TB pleural effusions (Pl-Effs) with the assessment of biological and cytological variable of pleural fluid (PF), introducing in the investigation algorithm and testing a new tool, the computer-assisted evaluation of cell populations on PF smears.

PATIENTS, MATERIALS AND METHODS

A series of 85 patients with TB pleurisy (PLTB) were selected from a larger group of 322 patients with different types of Pl-Effs. The algorithm of investigation included. clinical variables, biological assays of PF, gross aspects including imagistic variables and PF cytology on May-Grünwald-Giemsa (MGG)-stained smears. All the data obtained were entered into and processed using Microsoft Excel module of the 2019 Microsoft Office Professional software along with the 2014 XLSTAT add-in program for MS Excel. The PF cellularity was assessed qualitatively by a cytologist and quantitatively with in-house software. Continuous variables were compared using Pearson's correlation test, while categorical variables were compared using χ² (chi-squared) test.

RESULTS

Our analysis showed that patients were usually males, aged between 25 and 44 years with Pl-Eff discovered at clinical imagistic examination, almost always one-sided and free in the pleural cavity. Its extension was either moderate or reduced. The PF had a serous citrine appearance in most of the cases, and biological characteristics pleaded for an exudate [high levels of proteins and lactate dehydrogenase (LDH)], with elevated adenosine deaminase (ADA) values and rich in lymphocytes (Ly). The attempt to identify the pathogen in PF was not of much help. Apart from Ly, neutrophils [polymorphonuclear neutrophils (PMNs)] were a rare presence and their amount had only a trend of direct correlation with Ly. The same situation was encountered in the case of mesothelial cells (MCs). The comparison between the qualitative and the quantitative, computer-assisted evaluations of cytological smears showed that the results of the two methods overlapped in less than one third of the cases, although the sensitivity and specificity values as well as the two calculated predictive values of the qualitative method were encouraging.

CONCLUSIONS

The assessment of biological variables and cell populations of the PF are basic tools in the diagnosis of pleural TB. The assessment of PF cell population could be improved by the use of computer-assisted quantitative analysis of the PF smears, which is simple to design, easy to introduce and handle and reliable.

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.

Diagnostic and comparative performance for the prediction of tuberculous pleural effusion using machine learning algorithms

OBJECTIVE

Early diagnosis and differential diagnosis of tuberculous pleural effusion (TPE) remains challenging and is critical to the patients' prognosis. The present study aimed to develop nine machine learning (ML) algorithms for early diagnosis of TPE and compare their performance.

METHODS

A total of 1435 untreated patients with pleural effusions (PEs) were retrospectively included and divided into the training set (80%) and the test set (20%). The demographic and laboratory variables were collected, preprocessed, and analyzed to select features, which were fed into nine ML algorithms to develop an optimal diagnostic model for TPE. The proposed model was validated by an independently external data. The decision curve analysis (DCA) and the SHapley Additive exPlanations (SHAP) were also applied.

RESULTS

Support vector machine (SVM) was the best model in discriminating TPE from non-TPE, with a balanced accuracy of 87.7%, precision of 85.3%, area under the curve (AUC) of 0.914, sensitivity of 94.7%, specificity of 80.7%, and F1-score of 86.0% among the nine ML algorithms. The excellent diagnostic performance was also validated by the external data (a balanced accuracy of 87.7%, precision of 85.2%, and AUC of 0.898). Neural network (NN) and K-nearest neighbor (KNN) had better net benefits in clinical usefulness. Besides, PE adenosine deaminase (ADA), PE carcinoembryonic antigen (CEA), and serum CYFRA21-1 were identified as the top three important features for diagnosing TPE.

CONCLUSIONS

This study developed and validated a SVM model for the early diagnosis of TPE, which might help clinicians provide better diagnosis and treatment for TPE patients.

A Review of Current Practices and Future Trends in Body Fluid Testing

BACKGROUND

Body fluid testing in the clinical chemistry laboratory is a cornerstone in the diagnostic workup of pathological effusions. Laboratorians may not be aware of the preanalytical workflows used in the collection of body fluids though the value is evident whenever processes change or issues arise. The analytical validation requirements can vary depending on the regulations dictated by the laboratories' jurisdiction and accreditor requirements. Much of analytical validation hinges on how useful testing is to clinical care. Usefulness of testing varies with how well established and incorporated the tests and interpretation are in practice guidelines.

CONTENT

Body fluid collections are depicted and described so clinical laboratorians have a basic appreciation of what specimens are submitted to the laboratory for testing. A review of validation requirements by major laboratory accreditation entities is presented. A review of the usefulness and proposed decision limits for common body fluid chemistry analytes is presented. Body fluid tests that show promise and those that are losing (or lost long ago) value are also reviewed.

SUMMARY

The total testing process from collection to result interpretation can be complicated and easily overlooked by the clinical laboratory. This review aims to improve the understanding and awareness of collections, validation, result interpretation, and provide an update on recent trends.

Machine learning approaches in diagnosing tuberculosis through biomarkers - A systematic review

Biomarker-based tests may facilitate Tuberculosis (TB) diagnosis, accelerate treatment initiation, and thus improve outcomes. This review synthesizes the literature on biomarker-based detection for TB diagnosis using machine learning. The systematic review approach follows the PRISMA guideline. Articles were sought using relevant keywords from Web of Science, PubMed, and Scopus, resulting in 19 eligible studies after a meticulous screening. All the studies were found to have focused on the supervised learning approach, with Support Vector Machine (SVM) and Random Forest emerging as the top two algorithms, with the highest accuracy, sensitivity and specificity reported to be 97.0%, 99.2%, and 98.0%, respectively. Further, protein-based biomarkers were widely explored, followed by gene-based such as RNA sequence and, Spoligotypes. Publicly available datasets were observed to be popularly used by the studies reviewed whilst studies targeting specific cohorts such as HIV patients or children gathering their own data from healthcare facilities, leading to smaller datasets. Of these, most studies used the leave one out cross validation technique to mitigate overfitting. The review shows that machine learning is increasingly assessed in research to improve TB diagnosis through biomarkers, as promising results were shown in terms of model's detection performance. This provides insights on the possible application of machine learning approaches to diagnose TB using biomarkers as opposed to the traditional methods that can be time consuming. Low-middle income settings, where access to basic biomarkers could be provided as compared to sputum-based tests that are not always available, could be a major application of such models.

Diagnostic value of soluble biomarkers for parapneumonic pleural effusion

Parapneumonic pleural effusion (PPE) is a common complication in patients with pneumonia. Timely and accurate diagnosis of PPE is of great value for its management. Measurement of biomarkers in circulating and pleural fluid have the advantages of easy accessibility, short turn-around time, objectiveness and low cost and thus have utility for PPE diagnosis and stratification. To date, many biomarkers have been reported to be of value for the management of PPE. Here, we review the values of pleural fluid and circulating biomarkers for the diagnosis and stratification PPE. The biomarkers discussed are C-reactive protein, procalcitonin, presepsin, soluble triggering receptor expressed on myeloid cells 1, lipopolysaccharide-binding protein, inflammatory markers, serum amyloid A, soluble urokinase plasminogen activator receptor, matrix metalloproteinases, pentraxin-3 and cell-free DNA. We found that none of the available biomarkers has adequate performance for diagnosing and stratifying PPE. Therefore, further work is needed to identify and validate novel biomarkers, and their combinations, for the management of PPE.

Pleural fluid biochemical analysis: the past, present and future

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.