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  • Title: Bayesian analysis using continuous likelihood ratios for identifying pleural exudates.
    Author: Porcel JM, Peña JM, Vicente de Vera C, Esquerda A, Vives M, Light RW.
    Journal: Respir Med; 2006 Nov; 100(11):1960-5. PubMed ID: 16626953.
    Abstract:
    STUDY OBJECTIVES: To ascertain if equations that calculate continuous likelihood ratios (CLRs) for pleural exudates improve pleural fluid categorization, especially when false positive or false negative test results are obtained by using Light's criteria. DESIGN AND SETTING: Retrospective review of the clinical and pleural fluid data from a consecutive series of patients with pleural effusion who underwent thoracentesis at the University Hospital Arnau de Vilanova (Lleida, Spain) over an 11-year period. PATIENTS AND METHODS: A total of 1490 patients with pleural effusion (298 transudates and 1192 exudates) were recruited into the study. The presence of a transudate or exudate was established by clinical judgment. We examined the comparative diagnostic accuracy of 4 tests (i.e. pleural fluid protein and lactate dehydrogenase (LDH), and pleural fluid to serum protein and LDH ratios) for discriminating between transudates and exudates. Decision thresholds were determined by receiver operating characteristics (ROC) analysis. Equations for calculating CLRs derived from a logistic regression analysis based on a previously described method. RESULTS: Individual pleural fluid tests did not differ in their diagnostic accuracies according to ROC analysis. We calculated CLRs for the elements of Light's criteria and pleural fluid protein, and also illustrated the sequential use of CLRs for determining posttest probabilities. Overall, CLR formulas had marginal performance for the correct categorization of pleural fluid. CONCLUSIONS: CLRs provide a probabilistic statement as to the likelihood an effusion is a transudate or exudate. However, clinical judgment is little changed by the application of CLRs, and in doubtful cases a great amount of uncertainty remains. This Bayesian approach is likely to have no major impact on the clinical practice.
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