The underlying mechanism of intersite discrepancies in ejection time measurements from arterial waveforms and its validation in the Framingham Heart Study.

Radial applanation tonometry is a well-established method for clinical hemodynamic assessment and is also becoming popular in wrist-worn fitness trackers. The time difference between the foot and the dicrotic notch of the arterial pressure waveform is a well-accepted approximation for the left ventricular ejection time (ET). However, several clinical studies have shown that ET measured from the radial pressure waveform deviates from that measured centrally. In this work, we consider the systolic wave and the dicrotic wave as two independent traveling waves and hypothesize that their wave speed difference leads to the intersite differences of measured ET (ΔET). Accordingly, we derived a mathematical dicrotic wave decomposition model and identified the most influential factors on ΔET via global sensitivity analysis. In our clinical validation on a heterogeneous cohort ( N = 5,742) from the Framingham Heart Study (FHS), the local sensitivity analysis results resembled the sensitivity variation patterns of ΔET from model simulations. A regression analysis on FHS data, using morphological features of radial pressure waveforms to estimate the carotid ET, produced a root mean square error of 3.76 ms and R ² of 0.91. The proposed dicrotic wave decomposition model can explain the intersite ET measurement discrepancies observed in the clinical data of FHS and can facilitate the precise identification of ET with radial pressure waveforms. Therefore, the proposed model will improve various physics-based pulse wave analysis methods as well as prospective artificial intelligence methods for tackling the subsequent big data produced from widespread wearable radial pressure monitoring. NEW & NOTEWORTHY Based on a new understanding of pressure wave propagation, we propose a novel dicrotic wave decomposition model considering the dicrotic wave as an independent traveling component. The proposed model can explain the mechanism underlying the intersite discrepancies in ejection time measurement from arterial waveforms and then, in principle, enhance the accuracy of both classical physics-based as well as more contemporary artificial intelligence-based pulse wave analysis methods in clinical and wearable radial blood pressure monitoring applications.