GX TECHNOLOGIES FOR RAPID DIAGNOSIS OF TUBERCULOSIS IN GENERAL HEALTHCARE NETWORK
Introduction. There is still a high percentage of tuberculosis detection by the number of complaints to the general medical network. Differential diagnosis is often performed in pulmonology departments using a standard diagnostic minimum. Reducing the time of diagnosis of tuberculosis is possible with the introduction of molecular genetic methods in the standards of primary examination. Aim. The aim is to determine the possibility of improving the detection of pulmonary tuberculosis using molecular genetic methods by the example of GeneXpert MTB/RIF cartridge technology in the pulmonology department of the general treatment network of healthcare. Material and methods. GeneXpert MTB/ RIF cartridge technology (MGM) was used in 159 patients with high risks of tuberculosis, characteristic changes on the X-ray image in addition to the standard study for the search for M. tuberculosis in biological material (sputum or bronchoalveolar fluid). Аccording to verified diagnoses, two groups were formed: 1st - tuberculosis (n = 119), second - pneumonia (n = 40). Statistical processing was carried out using the IBM SPSS Statistics 22.0 statistical software package. Results and discussions. In the differential diagnosis of tuberculosis in patients of the pulmonology department, there is a commonality of criteria for age (p=0.066), gender (p=0.249), unemployment (p=0.452), HIV infection (p=0.115), drug addiction (p=0.066), hepatitis (p=0.076). Positive results were on smear microscopy with sputum staining by Ziehel- Neelsen 18.9%, bronchoalveolar fluid 31%. The method of GeneXpert MTB/RIF cartridge technology showed a high sensitivity of 100% and specificity of 99% in all cases of a verified diagnosis of «tuberculosis» using culture methods. Conclusion. In patients with pneumonia who have risk factors for tuberculosis, when the GeneXpert MTB/RIF cartridge technology for M. tuberculosis search is included in the standard diagnostic minimum in the general medical network, the effectiveness of tuberculosis detection increases while reducing the time of diagnosis.