Statistical analysis of coronary blood flow monitoring data for hemodynamic assessment of the degree of coronary artery stenosis

Abstract

Statistical relationships between the pressure curves P_a(t), P_d(t) and blood flow velocity V_a(t), recorded in vivo in the coronary arteries of patients before and after stenosis, as part of the standard clinical procedure for calculating dynamic indices FFR, HSR, CFR, and a number of other ones generally accepted in surgical practice are studied. It is shown that in the case of insignificant stenosis that does not require surgical intervention, there is a correlation between the curves, and their spectrum is represented by three main harmonics. In the case of significant stenosis requiring immediate stenting, the positive correlation between P_a(t) and P_d(t) is less pronounced, and there is a negative correlation with the V_a(t) curve. The spectrum of the curves is much more complex and contains high-frequency harmonics. For patients from the so-called “gray zone”, an expert decision on the need for stenting can be made based on the appearance of additional harmonics in the spectrum and a negative correlation between the P_a(t), P_d(t) and V_a(t) curves. The proposed approach can be used for automatic decision-making based on machine learning and the development of appropriate mathematical models.