Predictive model for the diagnosis of tuberculous pleural effusion

ABSTRACT

This study developed a predictive model to identify pleural tuberculosis. A consecutive cases study of patients investigating the cause of pleural effusion, in an area of high prevalence of tuberculosis (Rio de Janeiro, Brazil). Clinical and laboratory variables were compared among patients with tuberculosis (TB) and without tuberculosis (NTB), individually and using logistic regression. The performance was described as diagnostic accuracy, compared to a gold standard in a masked way. We have studied 104 TB patients, 41 with malignant, 29 transudates, 28 parapneumonic, 13 with miscellaneous diseases. After identification of individual discrimination power aided by clinical, radiological and laboratory variables, the following ones were included in a multivariate analysis: ADA, total leukocytes, percentile of lymphocytes, protein, lactate dehydrogenase, duration of disease, age and gender. A logistic regression model to predict pleural tuberculosis including the five first variables showed the best performance. A receiver operating characteristic curve identified the best cutoff at 0.7, resulting in a sensitivity and specificity of more then 95 percent. The predictive model improved the specificity of ADA alone, keeping its sensitivity. This model seems helpful when a microbiological or histological diagnosis of pleural tuberculosis could not be established. External validation of these results is necessary before recommendation for routine application.