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.

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.

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.

Modeling of a gas concentration measurement system.

Energy expenditure can be calculated via measurement of oxygen consumption and carbon dioxide production. Precise measurement of expired gas concentrations and volume is required for this determination. For a given gas concentration measurement system, the establishment of a model is a good way to effectively use the equipments and achieve more accurate energy expenditure calculations. This paper proposes a simple but effective approach for the modeling of a gas concentration measurement system.

Optimal chemotherapy regimens: influence of tumours on normal cells and several toxicity constraints.

Cancer chemotherapy for the case of one drug is studied. The negative and inhibiting effect of the tumour on normal cells is taken into account. Under certain assumptions, we determine the optimal regimen that minimizes the tumour burden at the end of a fixed period of therapy while maintaining certain normal cell populations above prescribed levels. More precisely, it is demonstrated that the optimal strategy corresponds to injection of the drug at the maximal rate.