ArticleAnnals of thoracic medicine
Diagnosis of infectious pleural effusion using predictive models based on pleural fluid biomarkers.
Article in Annals of thoracic medicine. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 1 of them a synthesis that pooled it.
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Who cites it
8 citing papers in PubMed, 1 synthesis or guideline pooled it.
- A meta-analysis of Th1 and Th2 cytokine profiles differentiating tuberculous from malignant pleural effusion.Scientific reports · 2022Pooled it
- A Strong Correlation Between Pleural Fluid and Serum C-Reactive Protein Levels Across a Spectrum of Pleural Effusions.Journal of clinical medicine research · 2026Article
- Diagnostic Efficacy of C-Reactive Protein in Differentiating Various Causes of Exudative Pleural Effusion: Disease Research Should Not Be Exclusive to the Wealthy.Advances in respiratory medicine · 2025Article
- Diagnostic accuracy of interleukin-6 in multiple diseases: An umbrella review of meta-analyses.Heliyon · 2024Review
- Classification of pleural effusions using deep learning visual models: contrastive-loss.Scientific reports · 2022Article
- Effusion fluid cytology and COVID-19 infection.Cancer cytopathology · 2022Article
- Diagnosing pleural effusions using mass spectrometry-based multiplexed targeted proteomics quantitating mid- to high-abundance markers of cancer, infection/inflammation and tuberculosis.Scientific reports · 2022Article
- Can parapneumonic effusion be diagnosed only with pleural fluid analysis?Journal of thoracic disease · 2020Article
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Authors and funding
12 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
introductionDiagnosis of pleural infection (PI) may be challenging. The purpose of this paper is to develop and validate a clinical prediction model for the diagnosis of PI based on pleural fluid (PF) biomarkers.
methodsA prospective study was conducted on pleural effusion. Logistic regression was used to estimate the likelihood of having PI. Two models were built using PF biomarkers. The power of discrimination (area under the curve) and calibration of the two models were evaluated.
resultsThe sample was composed of 706 pleural effusion (248 malignant; 28 tuberculous; 177 infectious; 48 miscellaneous exudates; and 212 transudates). Areas under the curve for Model 1 (leukocytes, percentage of neutrophils, and C-reactive protein) and Model 2 (the same markers plus interleukin-6 [IL-6]) were 0.896 and 0.909, respectively (not significant differences). However, both models showed higher capacity of discrimination than their biomarkers when used separately (
conclusionsThe two predictive models developed for IPE showed a good diagnostic performance, superior to that of any of the markers when used separately. Although IL-6 contributes a slight greater capacity of discrimination to the model that includes it, its routine determination does not seem justified.
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