A cardiovascular parameter estimator to identify the systemic vascular parameters was developed using an extended Kalman filter (EKF) algorithm. Measurements from a ventricular assist device (VAD) and arterial pressure were used in the estimator. The systemic vascular parameters are important indices of heart condition. However, obtaining these parameters usually requires invasive measurements, which are difficult to obtain under most clinical environments. Including a VAD model into the estimator and using the signals from a VAD to identify the cardiovascular parameters for VAD patients would minimize the need for indwelling sensors. This paper illustrates the use of a Novacor left ventricular assist system (LVAS) model with a cardiovascular model in the estimator to identify the systemic vascular parameters: characteristic resistance, blood inertance at the aorta, systemic compliance, and systemic resistance. Performance of the estimator was evaluated using data from a computer simulation and from a mock circulatory system experiment. Robustness of the estimator to the available measurements was also described. The estimation results showed that the estimates converged with reasonable accuracy in a limited time when the LVAS pump volume and arterial pressure were used as measurements. These parameter estimates can provide additional diagnostic information for patient and device monitoring and can be used for future VAD control development.
Purpose:
Mismatches between pump output and venous return in a continuous-flow ventricular assist device may elicit episodes of ventricular suction. This research describes a series of in vitro experiments to characterize the operating conditions under which the EVAHEART centrifugal blood pump (Sun Medical Technology Research Corp., Nagano, Japan) can be operated with minimal concern regarding left ventricular (LV) suction.
Methods:
The pump was interposed into a pneumatically driven pulsatile mock circulatory system (MCS) in the ventricular apex to aorta configuration. Under varying conditions of preload, afterload, and systolic pressure, the speed of the pump was increased step-wise until suction was observed. Identification of suction was based on pump inlet pressure.
Results:
In the case of reduced LV systolic pressure, reduced preload (≤10 mmHg), and afterload (≤60 mmHg), suction was observed for speeds ≥2,200 rpm. However, suction did not occur at any speed (up to a maximum speed of 2,400 rpm) when preload was kept within 10–14 mmHg and afterload ≥80 mmHg. Although in vitro experiments cannot replace in vivo models, the results indicated that ventricular suction can be avoided if sufficient preload and afterload are maintained.
Conclusion:
Conditions of hypovolemia and/or hypotension may increase the risk of suction at the highest speeds, irrespective of the native ventricular systolic pressure. However, in vitro guidelines are not directly transferrable to the clinical situation; therefore, patient-specific evaluation is recommended, which can be aided by ultrasonography at various points in the course of support.
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