| ORIGINAL ARTICLE |
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| Year : 2019 | Volume
: 14
| Issue : 4 | Page : 254-263 |
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Diagnosis of infectious pleural effusion using predictive models based on pleural fluid biomarkers
Lucía Ferreiro1, Óscar Lado-Baleato2, Juan Suárez-Antelo3, María Elena Toubes3, María Esther San José4, Adriana Lama3, Nuria Rodríguez-Núñez3, José Manuel Álvarez-Dobaño1, Francisco J González-Barcala1, Jorge Ricoy3, Francisco Gude2, Luis Valdés1
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
Spain
 Source of Support: None, Conflict of Interest: None  | Check |
DOI: 10.4103/atm.ATM_77_19
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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.
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