Mycobacterium caprae infection in humans.

Mycobacterium caprae, a member of the Mycobacterium tuberculosis complex, causes tuberculosis (TB) in man and animals. Some features distinguish M. caprae from its epidemiological twin, Mycobacterium bovis: M. caprae is evolutionarily older, accounts for a smaller burden of zoonotic TB and is not globally distributed, but primarily restricted to European countries. M. caprae occurs only in a low proportion of human TB cases and this proportion may even decrease, if progress toward eradication of animal TB in Europe continues. So why bother, if M. caprae is not an enigma for diagnostic TB tests and if resistance against first-line drugs is a rarity with M. caprae? This 'European' pathogen of zoonotic TB asks interesting questions regarding the definition of a species. The latter, seemingly only an academic question, particularly requires and challenges the collaboration between human and veterinary medicine.

Rapid diagnostic algorithms as a screening tool for tuberculosis: an assessor blinded cross-sectional study.

BACKGROUND: A major obstacle to effectively treat and control tuberculosis is the absence of an accurate, rapid, and low-cost diagnostic tool. A new approach for the screening of patients for tuberculosis is the use of rapid diagnostic classification algorithms. METHODS: We tested a previously published diagnostic algorithm based on four biomarkers as a screening tool for tuberculosis in a Central European patient population using an assessor-blinded cross-sectional study design. In addition, we developed an improved diagnostic classification algorithm based on a study population at a tertiary hospital in Vienna, Austria, by supervised computational statistics. RESULTS: The diagnostic accuracy of the previously published diagnostic algorithm for our patient population consisting of 206 patients was 54% (CI: 47%-61%). An improved model was constructed using inflammation parameters and clinical information. A diagnostic accuracy of 86% (CI: 80%-90%) was demonstrated by 10-fold cross validation. An alternative model relying solely on clinical parameters exhibited a diagnostic accuracy of 85% (CI: 79%-89%). CONCLUSION: Here we show that a rapid diagnostic algorithm based on clinical parameters is only slightly improved by inclusion of inflammation markers in our cohort. Our results also emphasize the need for validation of new diagnostic algorithms in different settings and patient populations.