Subject. 433 workers of coal mines of the South of Kuzbass (drifters, stope miners, operators of rock removing machines) were examined at th
Subject. 433 workers of coal mines of the South of Kuzbass (drifters, stope miners, operators of rock removing machines) were examined at the age of 40 to 54 with a previously established diagnosis of chronic mechanic bronchitis. Objective. To develop a system for predicting the probability of developing respiratory failure in the patients with chronic mechanic bronchitis based on the clinic-functional and genetic researches. Methods. Clinical studies were carried out, the function of external respiration was studied; the body mass index, constitutional-morphological types (CMT) according to Rhys-Eysenck and Tanner, blood groups of AB0, Rhesus, MN systems, the level of C-reactive protein were determined. Main results. During examination the signs of respiratory failure were revealed in 344 patients (79.4 %). Considering the high percentage of this complication, using the Bayes method we elaborated the system to predict the probability of developing respiratory failure in the patients with chronic mechanic bronchitis. The most significant markers of the risk of developing respiratory failure were revealed: the duration of the course of chronic bronchitis is 5 years and more, the presence of tobacco smoking, the body mass index is 30 and more, the hypersthenic CMT according to Rhys-Eysenck, andromorphic CMT according to Tanner, A(II), AB(IV), Rh-, NN blood groups, the increase in the level of C-reactive protein. Scope of application. The system for predicting the probability of developing respiratory failure can be used during periodic medical examinations and in the rehabilitation of the patients with chronic mechanic bronchitis. Conclusions. The system for predicting the probability of developing respiratory failure in the patients with chronic mechanic bronchitis has been elaborated, which makes it possible to raise the accuracy of predicting the respiratory failure by increasing the number of the factors analyzed using highly sensitive markers.
