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-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. DISCUSSION: 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.
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-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. DISCUSSION: 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: C Gecmen; G G Gecmen; D Ece; M Kahyaoğlu; A Kalayci; C Y Karabay; O Candan; M E Isik; F Yilmaz; O Akgun; M Celik; I A Izgi; C Kirma; S Keser Journal: Herz Date: 2017-07-10 Impact factor: 1.443
Authors: Bongani M Mayosi; Mpiko Ntsekhe; Jackie Bosch; Shaheen Pandie; Hyejung Jung; Freedom Gumedze; Janice Pogue; Lehana Thabane; Marek Smieja; Veronica Francis; Laura Joldersma; Kandithalal M Thomas; Baby Thomas; Abolade A Awotedu; Nombulelo P Magula; Datshana P Naidoo; Albertino Damasceno; Alfred Chitsa Banda; Basil Brown; Pravin Manga; Bruce Kirenga; Charles Mondo; Phindile Mntla; Jacob M Tsitsi; Ferande Peters; Mohammed R Essop; James B W Russell; James Hakim; Jonathan Matenga; Ayub F Barasa; Mahmoud U Sani; Taiwo Olunuga; Okechukwu Ogah; Victor Ansa; Akinyemi Aje; Solomon Danbauchi; Dike Ojji; Salim Yusuf Journal: N Engl J Med Date: 2014-09-01 Impact factor: 91.245