Coronary Artery Disease and Inflammatory Activation Interfere with Peripheral Tissue Electrical Impedance Spectroscopy Characteristics—Initial Report

Abstract

Background: The electrical properties of cells and tissues in relation to energy exposure have been investigated, presenting their resistance and capacitance characteristics. The dielectric response to radiofrequency fields exhibits polarization heterogeneity under pathological conditions. The aim of the study was to analyze the differences in changes in resistance and capacitance measurements in the range from 1 kHz to 1 MHz, combined with an assessment of the correlation between the results of electrical impedance spectroscopy (EIS) and inflammatory activation. Methods: In the prospective study, EIS was performed on the non-dominant arm in 29 male patients (median (Q1–Q3) age of 69 (65–72)) with complex coronary artery disease and 10 male patients (median (Q1–Q3) age of 66 (62–69)) of the control group. Blood samples were collected for inflammatory index analysis. Results: The logistic regression analysis revealed a negative correlation with inflammatory indexes, including neutrophil to lymphocyte ratio (NLR) in the CAD group in the frequency of 30 kHz (p = 0.038, r = −0.317) regarding EIS resistance measurements and a positive correlation in CAD group in the frequency of 10 kHz (p = 0.029, r = −0.354) regarding EIS capacitance. Conclusions: The bioelectric characteristics of peripheral tissues measured by resistance and capacitance in EIS differ in patients with coronary artery disease and in the control group. Electrical impedance spectroscopy reveals a statistically significant correlation with inflammatory markers in patients with CAD.

Changing Face of Inflammatory Activation in Complex Coronary Artery Disease during the COVID-19 Pandemic

“…Our study is the first, to the best of our knowledge, to present the possible relationship between COVID-19-pandemic-related immunological activation in the human population and the possible impact on long-term prognosis. The presented optimal results, despite the increased inflammatory activation prior to surgery [79], may be explained by accurate pharmacotherapy, including statins, in the presented group, as in Azimi's study [80]. Kat-siki et al [81], in their study, presented the beneficial effects of lipid-lowering therapy in COVID-19, e.g., by proteases [82].…”

Machine learning‐directed electrical impedance tomography to predict metabolically vulnerable plaques

“…These models were chosen for their simplicity of training and predicting, as well as their incorporation of neural networks 35,36 . Logistic regression statistically evaluates the relationship between dependent and independent variables, rendering it promising for multi‐frequency EIS applications 37,38 . It often serves as a baseline to validate more advanced models 39 .…”

“… 35 , 36 Logistic regression statistically evaluates the relationship between dependent and independent variables, rendering it promising for multi‐frequency EIS applications. 37 , 38 It often serves as a baseline to validate more advanced models. 39 ResNet and DenseNet are widely regarded for image classification problems, and we speculated that they can also be generalized to predict plaque vulnerability from EIS data.…”