Theoretical Foundations of a Starling‐Like Controller for Rotary Blood Pumps

Abstract

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.

Exercise Studies in Patients With Rotary Blood Pumps: Cause, Effects, and Implications for Starling‐Like Control of Changes in Pump Flow

“…This study was designed to answer four central questions focusing on how the heart rate effect on pump flow reported by Akimoto et al will interact with our Starling-like controller: first, whether there is a consistent relationship between increases in heart rate and output of a rotary left ventricular device when maintained at a fixed speed; second, if so, whether heart rate has the dominant effect compared with other possible causes; third, whether the rise in pump flow is sufficient to warrant modifying our controller to capitalize on this fortuitous event; and finally, what is the magnitude of the associated changes in speed pulsatility, which would be key features in the operation of the Starling-like controller based on speed pulsatility (8).…”

Automatic control of dual left ventricular assist devices as a biventricular assist device

“…Step response tests are difficult to perform on systems with varying target value, such as those presented in [91], [127], [128]. Finally, whilst tracking experiments are easy to perform, they cannot replace plant disturbances, as they do not provide an indication of how the control system will respond to common patient scenarios.…”

“…Salamonsen and colleagues (2012) described a Frank-Starling like control system that sets a target flow rate as a function of preload [127]. This controller used flow pulsatility (difference between maximum and minimum flow rate each cardiac cycle) as a surrogate for preload which eliminated the need for an implantable pressure sensor.…”

“…Each pump's control system varied its flow rate with preload, similar to those systems proposed by Salamonsen et al [127] and Moscato et al [91]. This controller was implemented with two independent PID controllers, with the gains tuned manually.…”

“…The target flow for that operating point was calculated, and the speed of the pump adjusted to minimise the error between target and measured flow. Three different methods could be used for calculating the target flow, as described by Salamonsen and colleagues (2012) [127].…”