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Published on: April 12, 2019
Diagnosis of infectious pleural effusion using predictive models based on pleural fluid biomarkers
Lucía Ferreiro1,2, Óscar Lado-Baleato3,4, Juan Suárez-Antelo1
1Department of Pulmonology, University Clinical Hospital of Santiago, Santiago de Compostela, Spain.
Introduction:
Diagnosis 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.
Methods:
A 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.
Results:
The 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 (P < 0.001 for all). Rates of correct classification for Models 1 and 2 were 88.2% (623/706: 160/177 [90.4%] with infectious pleural effusion [IPE] and 463/529 [87.5%] with non-IPE) and 89.2% (630/706: 153/177 [86.4%] of IPE and 477/529 [90.2%] of non-IPE), respectively.
Conclusions:
The 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.
Insights
This study developed two clinical prediction models for diagnosing pleural infection (PI) using pleural fluid biomarkers. Both models demonstrated high accuracy, outperforming individual biomarkers for diagnosing infectious pleural effusion (IPE).
Area of Science:
- Pulmonary Medicine
- Diagnostic Biomarkers
- Clinical Prediction Modeling
Background:
- Diagnosing pleural infection (PI) presents clinical challenges.
- Pleural fluid (PF) biomarkers are crucial for PI diagnosis.
- Developing accurate predictive models for PI is essential.
Purpose of the Study:
- To develop and validate clinical prediction models for PI diagnosis.
- To assess the diagnostic performance of PF biomarkers.
- To compare the efficacy of combined biomarkers versus individual markers.
Main Methods:
- Prospective study on pleural effusion samples.
- Logistic regression used to build two predictive models based on PF biomarkers.
- Model discrimination (AUC) and calibration evaluated.
Main Results:
- Two models developed using leukocytes, neutrophils, C-reactive protein, and IL-6.
- Both models showed high diagnostic performance (AUC 0.896-0.909), significantly better than individual biomarkers.
- Correct classification rates reached 88.2% and 89.2% for infectious pleural effusion.
Conclusions:
- The developed predictive models for infectious pleural effusion (IPE) exhibit strong diagnostic performance.
- Models incorporating multiple biomarkers are superior to single biomarker analysis.
- While IL-6 slightly improves discrimination, its routine use may not be clinically justified.
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