Assessing pressure wave components for aortic stiffness monitoring through spectral regression learning

Abstract

Aims The aging process notably induces structural changes in the arterial system, primarily manifesting as increased aortic stiffness, a precursor to cardiovascular events. While wave separation analysis is a robust tool for decomposing the components of blood pressure waveform, its relationship with cardiovascular events, such as aortic stiffening, is incompletely understood. Furthermore, its applicability has been limited due to the need for concurrent measurements of pressure and flow. Our aim in this study addresses this gap by introducing a spectral regression learning method for pressure-only wave separation analysis. Methods and results Leveraging data from the Framingham Heart Study (2,640 individuals, 55% women), we evaluate the accuracy of pressure-only estimates, their interchangeability with reference method based on ultrasound-derived flow waves, and their association with Carotid-femoral pulse wave velocity. Method-derived estimates are strongly correlated with the reference ones for forward wave amplitude (R2=0.91), backward wave amplitude (R2=0.88), reflection index (R2=0.87), and moderately correlated with time delay between forward and backward waves (R2=0.38). The proposed pressure-only method shows interchangeability with reference method through covariate analysis. Adjusting for age, sex, body size, mean blood pressure, and heart rate, results suggest that both pressure-only and pressure-flow evaluations of wave separation parameters yield similar model performance for predicting Carotid-femoral pulse wave velocity with forward wave amplitude as the only significant factor (p < 0.001; 95% confidence interval, 0.056-0.097). Conclusions We propose an interchangeable pressure-only wave separation analysis method and demonstrate its clinical applicability in capturing aortic stiffening. The proposed method provides valuable non-invasive tool for assessing cardiovascular health.