Abdul-Hakeem H. Alomari

Developments in control systems for rotary left ventricular assist devices for heart failure patients: a review

From the moment of creation to the moment of death, the heart works tirelessly to circulate blood, being a critical organ to sustain life. As a non-stopping pumping machine, it operates continuously to pump blood through our bodies to supply all cells with oxygen and necessary nutrients. When the heart fails, the supplement of blood to the bodys organs to meet metabolic demands will deteriorate. The treatment of the participating causes is the ideal approach to treat heart failure (HF). As this often cannot be done effectively, the medical management of HF is a difficult challenge. Implantable rotary blood pumps (IRBPs) have the potential to become a viable long-term treatment option for bridging to heart transplantation or destination therapy. This increases the potential for the patients to leave the hospital and resume normal lives. Control of IRBPs is one of the most important design goals in providing long-term alternative treatment for HF patients. Over the years, many control algorithms including invasive and non-invasive techniques have been developed in the hope of physiologically and adaptively controlling left ventricular assist devices and thus avoiding such undesired pumping states as left ventricular collapse caused by suction. In this paper, we aim to provide a comprehensive review of the developments of control systems and techniques that have been applied to control IRBPs.

Theoretical foundations of a Starling-like controller for rotary blood pumps

A clinically intuitive physiologic controller is desired to improve the interaction between implantable rotary blood pumps and the cardiovascular system. This controller should restore the Starling mechanism of the heart, thus preventing overpumping and underpumping scenarios plaguing their implementation. A linear Starling-like controller for pump flow which emulated the response of the natural left ventricle (LV) to changes in preload was then derived using pump flow pulsatility as the feedback variable. The controller could also adapt the control line gradient to accommodate longer-term changes in cardiovascular parameters, most importantly LV contractility which caused flow pulsatility to move outside predefined limits. To justify the choice of flow pulsatility, four different pulsatility measures (pump flow, speed, current, and pump head pressure) were investigated as possible surrogates for LV stroke work. Simulations using a validated numerical model were used to examine the relationships between LV stroke work and these measures. All were approximately linear (r(2) (mean ± SD) = 0.989 ± 0.013, n = 30) between the limits of ventricular suction and opening of the aortic valve. After aortic valve opening, the four measures differed greatly in sensitivity to further increases in LV stroke work. Pump flow pulsatility showed more correspondence with changes in LV stroke work before and after opening of the aortic valve and was least affected by changes in the LV and right ventricular (RV) contractility, blood volume, peripheral vascular resistance, and heart rate. The system (flow pulsatility) response to primary changes in pump flow was then demonstrated to be appropriate for stable control of the circulation. As medical practitioners have an instinctive understanding of the Starling curve, which is central to the synchronization of LV and RV outputs, the intuitiveness of the proposed Starling-like controller will promote acceptance and enable rational integration into patterns of hemodynamic management.

Non-invasive estimation of pulsatile flow and differential pressure in an implantable rotary blood pump for heart failure patients.

We propose dynamical models for pulsatile flow and head estimation in an implantable rotary blood pump. Pulsatile flow and head data were obtained using a circulatory mock loop where fluid solutions with different values of viscosities were used as a blood analogue with varying haematocrit (HCT). Noninvasive measurements of power and pump speed were used with HCT values as inputs to the flow model while the estimated flow was used with the speed as inputs to a head estimation model. Linear regression analysis between estimated and measured flows obtained from a mock loop resulted in a highly significant correlation (R2=0.982) and a mean absolute error (e) of 0.323 L min(-1), while for head, R2=0.933 and e=7.682 mmHg were obtained. R2=0.849 and e=0.584 L min(-1) were obtained when the same model derived in the mock loop was used for flow estimation in ex vivo porcine data (N=6). Furthermore, in the steady state, the solution of the presented flow model can be described by a previously designed and verified static model. The models developed herein will play a vital role in developing a robust control system of the pump flow coping with changing physiological demands.

A sliding mode-based starling-like controller for implantable rotary blood pumps.

Clinically adequate implementation of physiological control of a rotary left ventricular assist device requires a sophisticated technique such as the recently proposed method based on the Frank-Starling mechanism. In this mechanism, the stroke volume of the heart increases in response to an increase in the volume of blood filling the left ventricle at the end of diastole. To emulate this process, changes in pump speed need to automatically regulate pump flow to ensure that the combined output of the left ventricle and pump match the output of the right ventricle across changing cardiovascular states. In this approach, we exploit the linear relationship between estimated mean pump flow (Q ̅ est) and pump flow pulsatility (PIQp) in a tracking control algorithm based on sliding mode control. The immediate response of the controller was assessed using a lumped parameter model of the cardiovascular system (CVS) and pump from which could be extracted both Q ̅ est and PIQp. Two different perturbations from the resting state in the presence of left ventricular failure were tested. The first was blood loss requiring a reduction in pump flow to match the reduced output from the right ventricle and to avoid the complication of ventricular suction. The second was exercise, requiring an increase in pump flow. The sliding mode controller induced the required changes in Qp within approximately five heart beats in the blood loss simulation and eight heart beats in the exercise simulation without clinically significant transients or steady-state errors.

A method for control of an implantable rotary blood pump for heart failure patients using noninvasive measurements.

We propose a deadbeat controller for the control of pulsatile pump flow (Q(p) ) in an implantable rotary blood pump (IRBP). Noninvasive measurements of pump speed and current are used as inputs to a dynamical model of Q(p) estimation, previously developed and verified in our laboratory. The controller was tested using a lumped parameter model of the cardiovascular system (CVS), in combination with the stable dynamical models of Q(p) and differential pressure (head) estimation for the IRBP. The control algorithm was tested with both constant and sinusoidal reference Q(p) as input to the CVS model. Results showed that the controller was able to track the reference input with minimal error in the presence of model uncertainty. Furthermore, Q(p) was shown to settle to the desired reference value within a finite number of sampling periods. Our results also indicated that counterpulsation yields the minimum left ventricular stroke work, left ventricular end diastolic volume, and aortic pulse pressure, without significantly affecting mean cardiac output and aortic pressure.

Non-invasive estimation and control of inlet pressure in an implantable rotary blood pump for heart failure patients

We propose a dynamical model for mean inlet pressure estimation in an implantable rotary blood pump during the diastolic period. Non-invasive measurements of pump impeller rotational speed (ω), motor power (P), and pulse width modulation signal acquired from the pump controller were used as inputs to the model. The model was validated over a wide range of speed ramp studies, including (i) healthy (C1), variations in (ii) heart contractility (C2); (iii) afterload (C2, C3, C4), and (iv) preload (C5, C6, C7). Linear regression analysis between estimated and extracted mean inlet pressure obtained from in vivo animal data (greyhound dogs, N = 3) resulted in a highly significant correlation coefficients (R(2) = 0.957, 0.961, 0.958, 0.963, 0.940, 0.946, and 0.959) and mean absolute errors of (e = 1.604, 2.688, 3.667, 3.990, 2.791, 3.215, and 3.225 mmHg) during C1, C2, C3, C4, C5, C6, and C7, respectively. The proposed model was also used to design a controller to regulate mean diastolic pump inlet pressure using non-invasively measured ω and P. In the presence of model uncertainty, the controller was able to track and settle to the desired input within a finite number of sampling periods and minimal error (0.92 mmHg). The model developed herein will play a crucial role in developing a robust control system of the pump that detects and thus avoids undesired pumping states by regulating the inlet pressure within a predefined physiologically realistic limit.

Non-invasive measurements based model predictive control of pulsatile flow in an implantable rotary blood pump for heart failure patients

In this paper, firstly, we propose a linear time-variant (LTV) model to estimate the mean pulsatile flow (Qp) in an implantable rotary blood pump (RBP). Non-invasive measurement of mean pulse-width modulation (PWM) signal acquired from the pump controller was used as an input to estimate the mean pulsatility index of pump rotational speed (PIω) with this subsequently used to estimate the mean Qp. Secondly, the proposed LTV model was used to develop a controller to regulate and track the variations in the mean Qp and PIω. We used a model predictive control (MPC) approach to develop the controller where this allowed us to explicitly apply pre-defined practical constraints to control input PWM as well as the output and the states of the system including; Qp and PIω. The model and the controller were tested using a parameter-optimized model of the cardiovascular system-rotary blood pump under wide ranges of speed ramp experiments carried out under different operating conditions such as variations in afterload, preload and heart contractility.

Noninvasive deadbeat control of an implantable rotary blood pump: A simulation study

A deadbeat controller has been proposed for the control of pulsatile pump flow in an implantable rotary blood pump (IRBP). A lumped parameter model of the cardiovascular system, in combination with the stable dynamical models of pulsatile flow and differential pressure (head) estimation for the IRBP was used to evaluate the controller. Pump speed and current were used as the only measured variables of the control system. The control algorithm was tested using both constant and sinusoidal reference pump flow input, under healthy and heart failure conditions. Results showed that the controller is able to track the reference input with minimal error in the presence of model uncertainty.

Physiological control of implantable rotary blood pumps for heart failure patients

In general, patient variability and diverse environmental operation makes physiological control of a left ventricular assist device (LVAD) a complex and complicated problem. In this work, we implement a Starling-like controller which adjusts mean pump flow using pump flow pulsatility as the feedback parameter. The linear relationship between mean pump flow and pump flow pulsatility forms the desired flow of the Starling-like controller. A tracking control algorithm based on sliding mode control (SMC) has been implemented. The controller regulates the estimated mean pulsatile flow (Q̅p) and flow pulsatility (PIQp) generated from a model of the assist device. A lumped parameter model of the cardiovascular system (CVS) was used to test the control strategy. The immediate response of the controller was evaluated by inducing a fall in left ventricle (LV) preload following a reduction in circulating blood volume. The simulation supports the speed and robustness of the proposed strategy.

In vivo validation of pulsatile flow and differential pressure estimation models in a left ventricular assist device

Implantation of sensors to measure hemodynamic parameters such as pulsatile pump flow and differential pressure (head) in an implantable rotary pump (IRBP) requires regular in situ calibration due to measurement drift. In addition, risks associated with sensor failure and thrombus formation makes the long-term implantation in patients problematic. In our laboratory, two stable and novel dynamical models for non-invasive pulsatile flow and head estimation were proposed and tested in vitro using mock circulatory loop experiments with varying hematocrit (HCT). Noninvasive measurements of power and pump speed were used as inputs to the flow model while the estimated flow was used together with the pump rotational speed as inputs to the head estimation model. In this paper, we evaluated the performance of the proposed models using in vivo experimental data obtained from greyhound dogs (N=5). Linear regression analysis between estimated and measured pulsatile flows resulted in a highly significant correlation (R2 = 0.946) and mean absolute error (e) of 0.810 L/min, while for head, R2 = 0.951 and e = 10.13 mmHg were obtained.