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.

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 (R(2) = 0.946) and mean absolute error (e) of 0.810 L/min, while for head, R(2) = 0.951 and e = 10.13 mmHg were obtained.

Non-invasive flow estimation in an implantable rotary blood pump: a study considering non-pulsatile and pulsatile flows.

Non-invasive estimation of flow was investigated in an implantable rotary blood pump (iRBP) with a hydrodynamic bearing. The effects of non-pulsatile and pulsatile flows were studied using in vitro mock loops, and acute (N = 3) and chronic (N = 6) ovine experiments. Using the non-pulsatile and pulsatile mock loops an average flow estimation algorithm was derived from root mean square (RMS) pump impeller speed and RMS input power. These algorithms were programmed into the iRBP controller for subsequent validation in vivo. In the acute experiments, venous return and systemic vascular resistance were adjusted through pharmacological intervention and exsanguination to produce an average range of pump flows from 0.0 to 2.6 l min(-1). Over this range the RMS estimation error was 88 +/- 12 ml, with a linear correlation slope of 0.992 +/- 0.006 (R2 = 0.986 +/- 0.004). In the chronic experiments, animals were monitored daily for up to three months and an average range of flows from 2.8 to 4.8 l min(-1) recorded. A linear correlation between the estimated and measured pump flows yielded a slope of 1.005 +/- 0.006 (R2 = 0.966 +/- 0.004). The RMS estimation error was 120 +/- 11 ml. Using this algorithm it is possible to effectively estimate flow in a rotary blood pump without implanting additional invasive sensors.

Sensorless flow and head estimation in the VentrAssist rotary blood pump.

Flow rate and pressure difference (or head) are key variables needed in the control of implantable rotary blood pumps. However, use of flow and/or pressure probes can decrease reliability and increase system power consumption and expense. For a given fluid viscosity, the flow state is determined by any 2 of the 4 pump variables: Flow, pressure difference, speed, and motor input power can be used. Thus, if viscosity is known or if its influence is sufficiently small, flow rate and pressure difference can be estimated from the motor speed and motor input power. For the VentrAssist centrifugal blood pump, which uses a hydrodynamic bearing, sensorless flow and pressure head estimation accuracy of 2 of our impeller designs were compared for a viscosity range of 1.2 to 4.5 mPas. This showed impeller design optimization can improve estimation accuracy. We also compared estimation accuracy using 2 blood analogues used in vitro, aqueous glycerol and red blood cells suspended in Haemaccel. The nature of the blood analogue and not only the viscosity of the fluid seems to influence estimation accuracy in our pump.