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.

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 body's 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.

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.

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.