So-Hyun Jansen-Park1, Sotirios Spiliopoulos2, Hongyu Deng3, Nick Greatrex1, Ulrich Steinseifer1, Dilek Guersoy4, Reiner Koerfer4, Gero Tenderich4. 1. Department of Cardiovascular Engineering, Institute of Applied Medical Engineering, Helmholtz Institute, RWTH Aachen University, Aachen, Germany. 2. Department for the Surgical Therapy of End-stage Heart Failure and Mechanical Circulatory Support, Heart- and Vascular Center Duisburg, Duisburg, Germany sotirios.spiliopolos@ejk.de. 3. ReinVAD GmbH, Dortmund, Germany. 4. Department for the Surgical Therapy of End-stage Heart Failure and Mechanical Circulatory Support, Heart- and Vascular Center Duisburg, Duisburg, Germany.
Abstract
OBJECTIVES: Real-time monitoring of the aortic valve function and the loading state of the left ventricle (LV) during mechanical circulatory support is essential. Therefore, we developed a system that determines accurately the aortic valve closing moment based on integrals derived from the pump inlet pressure and the pump power [pressure-power area (PPA)]. METHODS: A Deltastream diagonal pump was implanted in 10 healthy Rhoen sheep. Changes in ventricular volume and pressure in different assist levels were measured by a conductance catheter placed in the LV and were correlated with intrinsic pump signals, motor power, voltage and current. Measurements were obtained in the state of normal as well as decreased left ventricular contractility induced by β-blockers. RESULTS: Complete datasets were obtained in seven animals. The PPA-feedback signal reached its maximum at the speed of aortic valve closing. This was validated by pressure-volume (PV)-catheter measurements both at the baseline and in the state of decreased contractility. In both cases, zero-crossing occurred at the point of aortic valve closing speed. CONCLUSIONS: With this trial, we deliver the experimental basis for the development of an automatic feedback controller that would allow periodic speed changes in accordance with the loading state of the native ventricle and the opening state of the aortic valve. This would deliver real-time data to treating physicians and enable the establishment of a standard weaning protocol.
OBJECTIVES: Real-time monitoring of the aortic valve function and the loading state of the left ventricle (LV) during mechanical circulatory support is essential. Therefore, we developed a system that determines accurately the aortic valve closing moment based on integrals derived from the pump inlet pressure and the pump power [pressure-power area (PPA)]. METHODS: A Deltastream diagonal pump was implanted in 10 healthy Rhoen sheep. Changes in ventricular volume and pressure in different assist levels were measured by a conductance catheter placed in the LV and were correlated with intrinsic pump signals, motor power, voltage and current. Measurements were obtained in the state of normal as well as decreased left ventricular contractility induced by β-blockers. RESULTS: Complete datasets were obtained in seven animals. The PPA-feedback signal reached its maximum at the speed of aortic valve closing. This was validated by pressure-volume (PV)-catheter measurements both at the baseline and in the state of decreased contractility. In both cases, zero-crossing occurred at the point of aortic valve closing speed. CONCLUSIONS: With this trial, we deliver the experimental basis for the development of an automatic feedback controller that would allow periodic speed changes in accordance with the loading state of the native ventricle and the opening state of the aortic valve. This would deliver real-time data to treating physicians and enable the establishment of a standard weaning protocol.
Authors: Julien Guihaire; Francois Haddad; Mita Hoppenfeld; Myriam Amsallem; Jeffrey W Christle; Clark Owyang; Khizer Shaikh; Joe L Hsu Journal: Can J Cardiol Date: 2019-11-09 Impact factor: 5.223
Authors: Maciej Stapor; Adam Pilat; Andrzej Gackowski; Agnieszka Misiuda; Izabela Gorkiewicz-Kot; Michal Kaleta; Pawel Kleczynski; Krzysztof Zmudka; Jacek Legutko; Boguslaw Kapelak; Karol Wierzbicki Journal: Heart Date: 2022-06-10 Impact factor: 7.365