title: The diagnostic role of glycosaminoglycans in pleural effusions: a pilot study.
abstract: BACKGROUND
  Pleural effusions are classified into transudates and exudates. Various criteria have been used with Light's et al being the most accepted ones. Glycosaminoglycans (GAGs) have been detected during pleural fluids (PF) analysis in various causes. In this pilot study, we investigated: (a) the usefulness of GAGs in the assessment of pleural effusions, and (b) whether and in what way GAGs correlate with established criteria used to indicate an exudate.
  
  
  METHODS
  LDH, total protein, cholesterol and GAG levels were measured in pleural fluid and serum from 50 patients with pleural effusion. GAG levels were defined by the photometric method of Hata. The discriminative properties of pleural GAGs (pGAG), pleural fluid/serum GAG ratio (GAGR), serum GAGs (sGAG) and serum LDH (sLDH) were explored with ROC analysis.
  
  
  RESULTS
  According to ROC analysis, pGAG and GAGR exhibited satisfactory discriminative properties in the separation of pleural effusions. For GAGR, at a 1.1 cut off point, sensitivity and specificity reached 75.6%; 95%CI: 60.5-87.1 and 100%; 95%CI: 47.8-100, respectively. For pGAG at a cut off value of 8.4 microg/ml, these percentages changed to 86.7%; 95%CI: 73.2-94.9 and 100%; 95%CI: 47.8-100. The study also revealed the differential role of sGAG between malignancies and benign cases, scoring 68.8%; 95%CI: 50.0-83.9 for sensitivity, and 84.6%; 95%CI: 54.5-97.6 for specificity at a 7.8 microg/ml cut off.
  
  
  CONCLUSIONS
  Our results suggest that glycosaminoglycan measurement of both serum and pleural effusions could be useful for simultaneous differentiation of exudates from transudates, and of malignant from benign exudates.
authors.affiliation: Department of Pharmacology, School of Medicine, University of Athens, Athens, Greece. rvavetsi@med.uoa.gr
authors.forename: Rozina
authors.initials: R
authors.lastname: Vavetsi
authors.forename: Stefanos
authors.initials: S
authors.lastname: Bonovas
authors.forename: Paraskevi
authors.initials: P
authors.lastname: Polizou
authors.forename: Chrysanthi
authors.initials: C
authors.lastname: Papanastasopoulou
authors.forename: Georgia
authors.initials: G
authors.lastname: Dougekou
authors.forename: Nikolaos M
authors.initials: NM
authors.lastname: Sitaras
mesh_terms: D000368:Aged; D000369:Aged, 80 and over; D015415:Biomarkers / Q000378:metabolism; D002784:Cholesterol / Q000378:metabolism; D003937:Diagnosis, Differential; D005260:Female; D006025:Glycosaminoglycans / Q000378:metabolism*; D006801:Humans; D007770:L-Lactate Dehydrogenase / Q000378:metabolism; D008297:Male; D008875:Middle Aged; D010865:Pilot Projects; D010996:Pleural Effusion / Q000175:diagnosis* / Q000378:metabolism*; D016066:Pleural Effusion, Malignant / Q000175:diagnosis* / Q000378:metabolism*; D012372:ROC Curve; D012680:Sensitivity and Specificity
chemical_list: D015415:Biomarkers; D006025:Glycosaminoglycans; D002784:Cholesterol; D007770:L-Lactate Dehydrogenase