Detection and avoiding ventricular suction of ventricular assist devices

Evaluation of suction detection during different pumping states in an implantable rotary blood pump

In recent times, the problem of noninvasive suction detection for implantable rotary blood pumps has attracted substantial research interest. Here, we compare the performance of various suction indices for different types of suction and non-suction events based on pump speed irregularity. A total of 171 different indices that consist of previously proposed as well as newly introduced suction indices are tested using regularized logistic regression. These indices can be classified as amplitude based (derived from the mean, maximum, and minimum values of a cycle), duration based (derived from the duration of a cycle), gradient based (derived from the first order as well as higher order differences) and frequency based (derived from the power spectral density). The non-suction event data consists of ventricular ejection with or without arrhythmia and intermittent and continuous non-opening of the aortic valve. The suction event data consists of partial ventricular collapse that occurs intermittently as well as continuously with or without arrhythmia. In addition, we also attempted to minimize the usage of multiple indices by applying the sequential forward floating selection method to find which combination of indices gives the best performance. In general, the amplitude-based and gradient-based indices performed quite well while the duration-based and frequency-based indices performed poorly. By having only two indices ([i] the maximum gradient change in positive slope; and [ii] the standard deviation of the maximum value in a cycle), we were able to achieve a sensitivity of 98.9% and a specificity of 99.7%.

Frank-starling control of a left ventricular assist device

A physiological control system was developed for a rotary left ventricular assist device (LVAD) in which the target pump flow rate (LVADQ) was set as a function of left atrial pressure (LAP), mimicking the Frank-Starling mechanism. The control strategy was implemented using linear PID control and was evaluated in a pulsatile mock circulation loop using a prototyped centrifugal pump by varying pulmonary vascular resistance to alter venous return. The control strategy automatically varied pump speed (2460 to 1740 to 2700 RPM) in response to a decrease and subsequent increase in venous return. In contrast, a fixed-speed pump caused a simulated ventricular suction event during low venous return and higher ventricular volumes during high venous return. The preload sensitivity was increased from 0.011 L/min/mmHg in fixed speed mode to 0.47L/min/mmHg, a value similar to that of the native healthy heart. The sensitivity varied automatically to maintain the LAP and LVADQ within a predefined zone. This control strategy requires the implantation of a pressure sensor in the left atrium and a flow sensor around the outflow cannula of the LVAD. However, appropriate pressure sensor technology is not yet commercially available and so an alternative measure of preload such as pulsatility of pump signals should be investigated.

Automated non-invasive detection of pumping states in an implantable rotary blood pump

With respect to rotary blood pumps used as left ventricular assist devices (LVADs), it is clinically important to control pump flow to avoid complications associated with over-or under-pumping of the native heart. By employing only the non-invasive observer of instantaneous pump impeller speed to assess flow dynamics, a number of physiologically significant pumping states may be detected. Based on a number of acute animal experiments, five such states were identified: regurgitant pump flow (PR), ventricular ejection (VE), non-opening of the aortic valve (ANO), and partial collapse (intermittent and continuous) of the ventricle wall (PVC-I and PVC-C). Two broader states, normal (corresponding to VE, ANO) and suction (corresponding to PVC-I, PVC-C) were readily discernable in clinical data from human patients implanted with LVADs. Based on data from both the animal experiments (N=6) and the human patients (N=10), a strategy for the automated non-invasive detection of significant pumping states has been developed and validated. Employing a classification and regression tree (CART), this system detects pumping states with a high degree of accuracy: state VE -87.5/100.0% (sensitivity/specificity); state ANO - 98.1/92.5%; state PVC-I - 90.0/90.2%; state PVC-C - 61.2/98.0%. With a simplified binary scheme differentiating suction and normal states, both states were detected without error in data from the animal experiments, and with a sensitivity/specificity, for detecting suction, of 99.2/98.3% in the human patient data.