Noninvasive Cardiac Output and Central Systolic Pressure From Cuff-Pressure and Pulse Wave Velocity

Abstract

Goal: We introduce a novel approach to estimate cardiac output (CO) and central systolic blood pressure (cSBP) from noninvasive measurements of peripheral cuff-pressure and carotid-to-femoral pulse wave velocity (cf-PWV). Methods: The adjustment of a previously validated one-dimensional arterial tree model is achieved via an optimization process. In the optimization loop, compliance and resistance of the generic arterial tree model as well as aortic flow are adjusted so that simulated brachial systolic and diastolic pressures and cf-PWV converge towards the measured brachial systolic and diastolic pressures and cf-PWV. The process is repeated until full convergence in terms of both brachial pressures and cf-PWV is reached. To assess the accuracy of the proposed framework, we implemented the algorithm on in vivo anonymized data from 20 subjects and compared the methodderived estimates of CO and cSBP to patient-specific measurements obtained with Mobil-O-Graph apparatus (central pressure) and two-dimensional transthoracic echocardiography (aortic blood flow). Results: Both CO and cSBP estimates were found to be in good agreement with the reference values achieving an RMSE of 0.36 L/min and 2.46 mmHg, respectively. Low biases were reported, namely-0.04±0.36 L/min for CO predictions and-0.27±2.51 mmHg for cSBP predictions. Significance: Our onedimensional model can be successfully "tuned" to partially patient-specific standards by using noninvasive, easily obtained peripheral measurement data. The in vivo evaluation demonstrated that this method can potentially be used to obtain central aortic hemodynamic parameters in a noninvasive and accurate way.

Noninvasive estimation of aortic hemodynamics and cardiac contractility using machine learning

“…The results presented in this study are also compliant with the findings of Bikia et al 49 , who suggested that brachial BP and cfPWV can be used to predict central SBP and CO (RMSE equal to 2.46 mmHg and 0.36 L min −1 , respectively). Following an inverse problem-solving approach, a generalized model of the cardiovascular system was adjusted to quasi-patient-specific standards using measurements of brSBP, brDBP, HR, and cfPWV.…”

“…Total arterial compliance was chosen within the range of [0.10, 3.80] mL mmHg −1 in order to account for a wide range of different values of arterial tree stiffness 72 , 73 . It should be noted that evidence of nonuniform aortic stiffening was integrated for the elderly and hypertensive virtual subjects, following the methodology described by Bikia et al 49 .…”

Determination of Aortic Characteristic Impedance and Total Arterial Compliance From Regional Pulse Wave Velocities Using Machine Learning: An in-silico Study

“…Total peripheral resistance and arterial compliance were altered according to the literature ( Langewouters, 1982 ; Lu and Mukkamala, 2006 ; Segers et al, 2008 ). To simulate older or hypertensive individuals, in some cases, stiffening in the aorta was considered as non-uniform and more pronounced as described in our previous works ( Pagoulatou et al, 2019 ; Bikia et al, 2020a ). For a given set of input parameters, the model provides analytical solutions of the pressure and flow at every arterial segment.…”

Estimation of Left Ventricular End-Systolic Elastance From Brachial Pressure Waveform via Deep Learning

“…The selected distributions of the input model parameters are summarized in Table 2 . The parameters of arterial distensibility and terminal compliance were altered simultaneously, while nonuniform aortic stiffening was considered for the elderly and hypertensive virtual subjects, following the methodology described in our previous work ( Bikia et al, 2019 ; Pagoulatou et al, 2019 ). Peripheral resistances were modified uniformly in order to achieve the specific value of total peripheral resistance in the selected range.…”