title: Diagnosing tuberculous pericarditis.
abstract: BACKGROUND
  Definitive diagnosis of tuberculous pericarditis requires isolation of the tubercle bacillus from pericardial fluid, but isolating the organism is often difficult.
  
  
  OBJECTIVE
  To improve diagnostic efficiency for tuberculous pericarditis, using available tests.
  
  
  METHODS
  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-gamma), 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 (>40 g/l) and peripheral blood leukocyte count (<10 x 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-gamma >or=50 pg/ml, concentration had 92% sensitivity, 100% specificity and a positive predictive value (PPV) of 100% for the diagnosis of tuberculous pericarditis; pericardial fluid ADA >or=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-gamma for ADA yielded 98% sensitivity and 100% specificity.
  
  
  CONCLUSIONS
  Basic clinical and laboratory features can aid the diagnosis of tuberculous pericarditis. If available, pericardial IFN-gamma is the most useful diagnostic test. Otherwise we propose a prediction model that incorporates pericardial ADA and differential WBC counts.
authors.affiliation: Cardiology Unit/TREAD Research, Tygerberg Hospital and Stellenbosch University, Parow, South Africa.
authors.forename: H
authors.initials: H
authors.lastname: Reuter
authors.forename: L
authors.initials: L
authors.lastname: Burgess
authors.forename: W
authors.initials: W
authors.lastname: van Vuuren
authors.forename: A
authors.initials: A
authors.lastname: Doubell
mesh_terms: D005260:Female; D006801:Humans; D008297:Male; D009169:Mycobacterium tuberculosis / Q000302:isolation & purification*; D010495:Pericarditis, Tuberculous / Q000175:diagnosis*; D011446:Prospective Studies; D013019:South Africa / Q000453:epidemiology