Estimation of Systemic Vascular Resistance Using Built-In Sensing From an Implanted Left Ventricular Assist Device

Abstract

This paper presents an approach for real-time estimation of the systemic vascular resistance (SVR) of heart failure patients who have a left ventricular assist device (LVAD). Notably, an approach is described that relies only on sensing that is built into the LVAD, so no additional sensors or measurements are required. The estimation of SVR is accomplished using a variant of the extended Kalman filter (EKF) algorithm, making use of a reduced-order systemic circulation model, and requires LVAD flowrate as an input to the systemic circulation and measurement of the LVAD differential pressure. Experiments using a hybrid mock circulatory loop (hMCL) are used to show the efficacy of this approach for both types of LVAD pumping modalities; i.e., continuous flow (CF) turbomachines and pulsatile flow (PF) positive-displacement pumps. The mock loop uses a real-time hardware-in-the-loop simulation of the cardiovascular system (CVS) where physiological parameters and particularly the SVR can be set to known values, allowing a basis for evaluating the accuracy of the estimation algorithms. It was found that SVR value estimates were accurate within 1.3% and 0.7% compared to the set model values for the continuous and PF LVADs, respectively. The use of this SVR estimation approach utilizing built-in LVAD sensing technology has potential for use in further real-time estimation endeavors, monitoring of patient physiology, and providing alerts to physicians.

Modeling Transient Cardiovascular Hemodynamics With Physiological Conscious Autoencoder

“…In terms of the diseases cases, AS affects only the left ventricular pressure, the volume, and the flow rate at both in vitro and in silico data are in accordance with the physiological findings [25]. Vascular systemic resistance increment pulled up aortic pressure to 150 mmHg but did not affect the 𝑄 𝐿𝑉 (𝑡) value in all cases.…”

“…On the other hand, Yang et al suggested an inverse problem for the seven parameters of the same model of ours together with Emax and Emin estimation based on nonlinear optimization assuming using the systemic arterial pressure [24]. The study given in [25] focuses only on vascular resistance change Rsys while [22]…”

“…[23]. When a complete statespace is not measurable, extended Kalman filter methods are utilized for the measurement from real patients, some devices such as Left Ventricular Assisted Device (LVAD), or an artificial heart [22], [24], [25].…”

Reinventing the displacement left ventricular assist device in the continuous-flow era: TORVAD, the first toroidal-flow left ventricular assist device

“…Dynamic changes in preload and afterload are sensed as forces against the magnetic pistons and trigger appropriate changes in piston cycling to automatically adjust hemodynamic support (8,9). Systemic vascular resistance is estimated to assist in patient monitoring (10). Preload sensitivity prevents potentially dangerous suction events and overpumping (8).…”

An intelligent aortic valve model for complete cardiac cycle

“…[15][16][17][18][19][20][21] These inquiries underscore the importance of incorporating these CPs, enhancing our grasp of transient physiological dynamics. [20][21][22][23] Notably, when applying the extended Kalman filter (EKF) for physiological state estimation, model-based approaches are essential for handling unknown dynamics. However, EKF performance diminishes when nonlinear AV dynamics, especially during opening and closing, are absent.…”