• Journal of the Korean Society of Manufacturing Process Engineers
• /
• v.12 no.4
• /
• pp.22-28
• /
• 2013

It is important to begin left ventricular assist device (LVAD) treatment at appropriate time for heart failure patients who expect cardiac recovery after the therapy. In order to predict the optimal timing of LVAD implantation, we predicted pumping efficacy of LVAD according to the severity of heart failure theoretically. We used LVAD-implanted cardiovascular system model which consist of 8 Windkessel compartments for the simulation study. The time-varying compliance theory was used to simulate ventricular pumping function in the model. The ventricular systolic dysfunction was implemented by increasing the end-systolic ventricular compliance. Using the mathematical model, we predicted cardiac responses such as left ventricular peak pressure, cardiac output, ejection fraction, and stroke work according to the severity of ventricular systolic dysfunction under the treatments of continuous and pulsatile LVAD. Left ventricular peak pressure, which indicates the ventricular loading condition, decreased maximally at the 1st level heart-failure under pulsatile LVAD therapy and 2nd level heart-failure under continuous LVAD therapy. We conclude that optimal timing for pulsatile LVAD treatment is 1st level heart-failure and for continuous LVAD treatment is 2nd level heart-failure when considering LVAD treatment as "bridge to recovery".

• Journal of the Korean Society for Precision Engineering
• /
• v.15 no.4
• /
• pp.83-90
• /
• 1998

In this paper, we present neural network for control of Left Ventricular Assist Device(LVAD) system with a pneumatically driven mock circulation system. Beat rate(BR), Systole-Diastole Rate(SDR) and flow rate are collected as the main variables of the LVAD system. System modeling is completed using the neural network with input variables(BR, SBR, their derivatives, actual flow) and output variable(actual flow). It is necessary to apply high perfomance control techniques, since the LVAD system represent nonlinear and time-varing characteristics. Fortunately. the neural network can be applied to control of a nonlinear dynamic system by learning capability In this study, we identify the LVAD system with neural network and control the LVAD system by PID controller and neural network feedforward controller. The ability and effectiveness of controlling the LVAD system using the proposed algorithm will be demonstrated by experiment.

• Proceedings of the Korean Society of Precision Engineering Conference
• /
• 1996.11a
• /
• pp.260-266
• /
• 1996

In this paper, we presents neural network identification and control of highly complicated nonlinear Left Ventricular Assist Device(LVAD) system with a pneumatically driven mock circulation system. Generally the LVAD system need to compensate nonlinearities. Hence, it is necessary to apply high performance control techniques. Fortunately, the neural network can be applied to control of a nonlinear dynamic system by learning capability. In this study, we identify the LVAD system with Neural Network Identification. Once the NNI has learned the dynamic model of LVAD system, the other network, called Neural Network Controller(NNC), is designed for control of a LVAD system. The ability and effectiveness of identifying and controlling a LVAD system using the proposed algorithm will be demonstrated by computer simulation.

• Journal of Chest Surgery
• /
• v.52 no.2
• /
• pp.105-108
• /
• 2019

Right heart failure is a relatively common complication after left ventricular assist device (LVAD) implantation. Severe right heart failure can be managed by temporary right ventricular assist device (RVAD) implantation. However, trans-sternal RVAD insertion requires a subsequent third sternotomy for cannula removal. Herein, we present a case of RVAD insertion via a left anterior mini-thoracotomy after LVAD implantation in a patient with alcohol-induced cardiomyopathy.

• Proceedings of the Korean Society of Precision Engineering Conference
• /
• 1997.04a
• /
• pp.150-155
• /
• 1997

In this paper,we present neural network for control of Left Ventricular Assist Device(LVAD)system with a pneumatically driven mock cirulation system. It is necessary to apply high perfomance control techniques, since the LVAD system represent nonlinear and time-varing characteristics. Fortunately, the neural network can be applied to control of a nonliner dynamic system by learning capability. In this study,we identify the LVAD system with neural network and control the LVAD system by PID controller and neural network feedforward controller. The ability and effectiveness of controlling the LVAD system using the proposed algorithm will be demonstrated by computer simulation and experiment.

• Journal of Institute of Control, Robotics and Systems
• /
• v.4 no.3
• /
• pp.315-320
• /
• 1998

In this paper, we present the PID control method for the controlling flow rate of highly complicated nonlinear Left Ventricular Assist Device(LVAD) with pneumatically driven mock circulatory system. Beat Rate (BR), Systole-Diastole Rate (SDR) and flow rate are used as the main variables of the LVAD system. System modeling is completed using the neural network with input variables (BR, SDR, their derivatives, actual flow) and an output valiable(actual flow). Then, as the basis of this model, we perform the simulation of PID control to predict the performance and tendency of the system and control the flow rate of LVAD system using the PID controller. The ability and effectiveness of identifying and controlling a LVAD system using the proposed algorithm will be demonstrated through computer simulation and experiments.

• Journal of Biomedical Engineering Research
• /
• v.19 no.1
• /
• pp.39-46
• /
• 1998

In this paper, we present a neural network identification and a control of highly complicated nonlinear left ventricular assist device(LVAD) system with a pneumatically driven mock circulation system. Generally, the LVAD system needs to compensate for nonlinearities. It is necessary to apply high performance control techniques. Fortunately, the neural network can be applied to control of a nonlinear dynamic system by learning capability. In this study, we identify the LVAD system with neural network identification(NNI). Once the NNI has learned the dynamic model of the LVAD system, the other network, called neural network controller(NNC), is designed for a control of the LVAD system. The ability and effectiveness of identifying and controlling the LVAD system using the proposed algorithm will be demonstrated by computer simulation.

• Journal of Chest Surgery
• /
• v.57 no.2
• /
• pp.184-194
• /
• 2024

Background: Left ventricular assist devices (LVADs) are widely employed as a therapeutic option for end-stage heart failure. We evaluated the outcomes associated with centrifugal-flow LVAD implantation, comparing 2 device models: the Heartmate 3 (HM3) and the Heartware Ventricular Assist Device (HVAD). Methods: Data were collected from patients who underwent LVAD implantation between June 1, 2015 and December 31, 2022. We analyzed overall survival, first rehospitalization, and early, late, and LVAD-related complications. Results: In total, 74 patients underwent LVAD implantation, with 42 receiving the HM3 and 32 the HVAD. A mild Interagency Registry for Mechanically Assisted Circulatory Support score was more common among HM3 than HVAD recipients (p=0.006), and patients receiving the HM3 exhibited lower rates of preoperative ventilator use (p=0.010) and extracorporeal membrane oxygenation (p=0.039). The overall early mortality rate was 5.4% (4 of 74 patients), with no significant difference between groups. Regarding early right ventricular (RV) failure, HM3 implantation was associated with a lower rate (13 of 42 [31.0%]) than HVAD implantation (18 of 32 [56.2%], p=0.051). The median rehospitalization-free period was longer for HM3 recipients (16.9 months) than HVAD recipients (5.3 months, p=0.013). Furthermore, HM3 recipients displayed a lower incidence of late hemorrhagic stroke (p=0.016). In the multivariable analysis, preoperative use of continuous renal replacement therapy (odds ratio, 22.31; p=0.002) was the only significant predictor of postoperative RV failure. Conclusion: The LVAD models (HM3 and HVAD) demonstrated comparable overall survival rates. However, the HM3 was associated with a lower risk of late hemorrhagic stroke.

• 제어로봇시스템학회:학술대회논문집
• /
• 1995.10a
• /
• pp.514-517
• /
• 1995

A new stroke output control algorithm with a fuzzy logic for an electrohydraulic left ventricular assist device(EH-LVAD) was developed. The EH-LVAD pumps out blood from left atrium actively. Excessive suction of blood may cause fatal damage in left atrium. The LVAD has to provide a maximal stroke output without collapse of left atrium. In this study a new fuzzy algorithm for predicting and detecting suction and doing proper action on LVAD without using an extra pressure sensor but with bellows pressure signal and motor current signal is developed. The performance of the fuzzy control algorithm is demonstrated by the results from mock circulatory experiments.

• Journal of Chest Surgery
• /
• v.47 no.4
• /
• pp.409-412
• /
• 2014

Percutaneous extracorporeal life support (P-ECLS) is a useful modality for the management of refractory cardiac or pulmonary failure. However, venoarterial P-ECLS may result in a complication of left ventricular distension. In this case report, we discuss a patient with drug-induced dilated cardiomyopathy managed with venoarterial P-ECLS and a left atrial vent catheter. The venoarterial P-ECLS was modified to a paracorporeal left ventricular assist device (LVAD) by removing the femoral venous cannula. After 28 days of hospitalization, the patient was successfully weaned from the paracorporeal LVAD and discharged home from the hospital.