Diagnosing tuberculous pericarditis

Reuter H. ; Burgess L. ; van Vuuren W. ; Doubell A. (2006)


Background: Definitive diagnosis of tuberculous pericarditis requires isolation of the tubercle bacillus from pericardial fluid, but isolating the organism is often difficult. Aim: To improve diagnostic efficiency for tuberculous pericarditis, using available tests. Design: Prospective observational study. Methods: Consecutive patients (n=233) presenting with pericardial effusions underwent a predetermined diagnostic work-up. This included (i) clinical examination; (ii) pericardial fluid tests: biochemistry, microbiology, cytology, differential white blood cell (WBC) count, gamma interferon (IFN-γ), adenosine deaminase (ADA) levels, polymerase chain reaction testing for Mycobacterium tuberculosis; (iii) HIV; (iv) sputum smear and culture; (v) blood biochemistry; and (vi) differential WBC count. A model was developed using 'classification and regression tree' analysis. The cut-off for the total diagnostic index (DI) was optimized using receiver operating characteristic (ROC) curves. Results: Fever, night sweats, weight loss, serum globulin (>40g/l) and peripheral blood leukocyte count (<10 × 10 9/l) were independently predictive. The derived prediction model had 86% sensitivity and 84% specificity when applied to the study population. Pericardial fluid IFN-γ ≥50pg/ml, concentration had 92% sensitivity, 100% specificity and a positive predictive value (PPV) of 100% for the diagnosis of tuberculous pericarditis; pericardial fluid ADA ≥40 U/l had 87% sensitivity and 89% specificity. A diagnostic model including pericardial ADA, lymphocyte/neutrophil ratio, peripheral leukocyte count and HIV status had 96% sensitivity and 97% specificity; substituting pericardial IFN-γ for ADA yielded 98% sensitivity and 100% specificity. Discussion: Basic clinical and laboratory features can aid the diagnosis of tuberculous pericarditis. If available, pericardial IFN-γ is the most useful diagnostic test. Otherwise we propose a prediction model that incorporates pericardial ADA and differential WBC counts. © 2006 Oxford University Press.

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