Annals of Thoracic Medicine Official publication of the Saudi Thoracic Society, affiliated to King Saud University
Search Ahead of print Current Issue Archives Instructions Subscribe e-Alerts Login 
Home Email this article link Print this article Bookmark this page Decrease font size Default font size Increase font size
Year : 2019  |  Volume : 14  |  Issue : 4  |  Page : 254-263

Diagnosis of infectious pleural effusion using predictive models based on pleural fluid biomarkers

1 Department of Pulmonology, University Clinical Hospital of Santiago; Interdisciplinary Research Group in Pulmonology, Santiago de Compostela, Spain
2 Department ofClinical Epidemiology, University Clinical Hospital of Santiago; Research Group for Epidemiology of Common Diseases, Health Research Institute of Santiago de Compostela (IDIS), Santiago de Compostela, Spain
3 Department of Pulmonology, University Clinical Hospital of Santiago, Santiago de Compostela, Spain
4 Interdisciplinary Research Group in Pulmonology; Department ofClinical Laboratory Analysis, University Clinical Hospital of Santiago, Santiago de Compostela, Spain

Correspondence Address:
Dr. Lucía Ferreiro
Department of Pulmonology, University Hospital Complex of Santiago de Compostela, Travesîa da Choupana s/n, 15706, Santiago de Compostela
Login to access the Email id

Source of Support: None, Conflict of Interest: None

DOI: 10.4103/atm.ATM_77_19

Rights and Permissions

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.

Print this article     Email this article
 Next article
 Previous article
 Table of Contents

 Similar in PUBMED
   Search Pubmed for
   Search in Google Scholar for
 Related articles
 Citation Manager
 Access Statistics
 Reader Comments
 Email Alert *
 Add to My List *
 * Requires registration (Free)

 Article Access Statistics
    PDF Downloaded397    
    Comments [Add]    
    Cited by others 7    

Recommend this journal