HEART RATE TURBULENCE: HRTO/HRTS INDICATORS AND INNOVATIVE CALCULATIONS OF THE PRECISION CARDIOLOGY MODEL IN DATA ANALYSIS SOFTWARE

Objective of the study – to objectify the analysis of quantitative electrocardiogram indicators by creating and utilizing an individual formula for calculating heart rate turbulence (HRT) indicators (HRTO and HRTS) in data analysis software tools, as well as to demonstrate the clinical applicability of the proposed model of precision cardiologyMaterials and methods. In the first stage of the study, the formulas for determining HRT parameters (HRTO and HRTS) were analyzed and implemented in a spreadsheet editor like Excel. The second stage of the study involved examining 32 patients with ischemic heart disease with diagnoses of chronic coronary syndrome, acute coronary syndrome (STEMI type), and hypertension with ventricular arrhythmia. Holter monitoring of electrocardiograms was conducted, heart rate variability (SDNN, RMSSD) was assessed, ECG digitization and differentiation with the assessment of the ratio of maximum speeds (VMSh) were carried out, echocardiography with determination of left ventricular ejection fraction (LVEF) was performed, blood lipid spectrum was analyzed, and HADS anxiety scale, EQ-5D-3L quality of life assessment with determination of Visual Analogue Scale (VAS) pain scores, and results of coronary angiography were evaluated.Results. As a result of our research, formulas for calculating HRT were created and implemented in a spreadsheet editor. Their relationships with the clinical-pathogenetic indicators of the examined patients were determined. In the group of positive/negative direction depending on the dynamics of the HRTO indicator (HRTO < 0% or HRTO > 0%), there was a dependence on the predominance of VAS pain scores in unfavorable HRTO distribution (51,33±3,69% versus 34,11±2.58%, p<0,001, Δ%1-2=50,45%), while there was an inverse discrepancy in the number of ventricular extrasystoles (4,00±1,00% versus 13,50±4,50%, p<0,05, Δ%1-2=70,37%). For the unfavorable distribution of the HRTS indicator, a decrease in the SDNN value (100,43±7,23 ms versus 129,25±9,25 ms, p<0,02, Δ%1-2=22,29%) was also recorded, and there was a positive increase in the RMSSD indicator in the group with positive HRTS dynamics (160,65±27,15 ms versus 22,9±6,29 ms, p<0,001, Δ%1-2=95,74%). Changes in LVEF, depending on the distribution of HRTS (<2.5 ms/RR>), were associated with a significant increase in the odds ratio (OR: 3.11) with a 95% confidence interval (CI: 0,62-15,71; p<0,05). A reliable correlation between the increase in OR and the HRTO indicator (<0%>) was determined when comparing single-vessel damage against 2-3-vessel coronary artery damage (OR: 0,42, CI: 0,08-2,25, p<0,05). The interaction between HRTO (<0%>) and the likelihood of ventricular extrasystole (<240/24 hours) during 24-hour Holter monitoring is reflected in the OR of 3,75 (CI: 0,34-40,81; p>0,1), but not for HRTS (OR: 0,84; CI: 0,13-5,56; p>0.1).Conclusions. 1.The proposed innovative approach to the calculation of HRTO and HRTS metrics in data analysis software demonstrates significant potential for improving patient status prediction and determining treatment endpoints in the model of precision cardiology. 2. In the case of an unfavorable HRTO distribution, there was a higher number of ventricular ectopic beats (p<0.05) and a negative trend of SDNN with an unfavorable HRTS distribution (p<0.02) with a positive HRTS dynamics with an increase in RMSSD (p<0.001), the correlation between HRTO and the duration of ST depression in the Pearson and Spearman distributions is statistically significant (p<0.01), the increase in the maximum speed indicator of the differentiated ECG is associated with a risk reduction according to the HRTO decrease (p>0.1), comparing the changes in single-vessel damage against 2-3 vessel coronary artery damage with HRTO shows an odds ratio of 0.42 (p<0.05). 3. At the same time, correlation analysis showed a weak positive relationship between HRTO and HRTS.