Role of correlation and regression analysis in the diagnosis of cardiovascular desynchronization among locomotive drivers in Russia

Background. The main manifestations of cardiovascular dysfunctions are increase in blood pressure and heart rate and endothelial dysfunctions leading to cardiovascular diseases (CVDs). This work aims to explain the pathophysiological approach of desynchronization for diagnosis of a disease at an early stage. Subjects and Methods. After clearance from the review board of the institute and informed consent from the subjects, 48 locomotive drivers from Chelyabinsk station were examined. The control group included 28 healthy students and employees of RUDN University who were matched by age and sex. The automated pre-trip medical examination system (hardware-software complex KAPD-01-st “system technologies,” St. Petersburg) was used to measure the heart rate (HR), systolic blood pressure (SBP) and diastolic blood pressure (DBP). The control subjects underwent continuous automatic monitoring (TM2421, A & D, Japan) for 2 to 7 days (96-336 measurements each). This method has also been further illustrated by monitoring data of other persons. Regression analysis was used for correlation of desynchronization with manifestations of CVDs. Results. A total of 380-400 observations were made for each of the screened locomotive drivers. Taking into account only the correlation coefficients without determining their statistical significance, hides the possibility of logical errors. If we classify the strength of the correlation as high, medium and weak, the values of the correlation coefficients in the given example could be interpreted as a manifestation of a strong association. However, the regression coefficients, the magnitude of their standard error and the statistical significance of the estimates confirm a very close relationship between SBP and DBP (P = 0.0004). But at the same time they testify to the independence of the HR both from the SBP (P = 0.279) and from the DBP (P = 0.185). The HR at this point of time was almost constant, as it was controlled by a pacemaker implanted earlier. Conclusions. To identify desynchronization, in addition to evaluating the specific rhythms’ parameters, it requires specialized software tools. However, simple methods can be used to ensure the consistency and /or degree of mismatch in physiological functions. The complex correlation and regression analyses of observed phenomena are easily accessible due to technological advancements. © Nova Science Publishers, Inc