Assessment of Aortic Characteristic Impedance and Arterial Compliance from Non-invasive Carotid Pressure Waveform in The Framingham Heart Study.

The primary goal of this study was to test the hypothesis that a hybrid intrinsic frequency-machine learning (IF-ML) approach can accurately evaluate total arterial compliance (TAC) and aortic characteristic impedance (Zao) from a single noninvasive carotid pressure waveform in both women and men with heart failure (HF). TAC and Zao are cardiovascular biomarkers with established clinical significance. TAC is lower and Zao is higher in women than in men, so women are more susceptible to the consequent deleterious effects of them. Although the principles of TAC and Zao are pertinent to a multitude of cardiovascular diseases, including HF, their routine clinical use is limited because of the requirement for simultaneous measurements of flow and pressure waveforms. For this study, the data were obtained from the Framingham Heart Study (n = 6,201, 53% women). The reference values of Zao and TAC were computed from carotid pressure and aortic flow waveforms. IF parameters of carotid pressure waveform were used in ML models. IF models were developed on n = 5,168 of randomly selected data and blindly tested the remaining data (n = 1,033). The final models were evaluated in patients with HF. Correlations between IF-ML and reference values in all HF and HF with preserved ejection fraction for TAC were 0.88 and 0.90, and for Zao were 0.82 and 0.80, respectively. The classification accuracy in all HF and HF with preserved ejection fraction for TAC were 0.9 and 0.93, and for Zao were 0.81 and 0.89, respectively. In conclusion, the IF-ML method provides an accurate estimation of TAC and Zao in all subjects with HF and in the general population.