BACKGROUND: Axial blood pumps have been very successfully introduced into the arena of prolonged clinical support. However, they do not offer inherent load-responsive mechanisms for adjusting pumping performance to venous return and changes in physiologic requirements of the patient. To provide for these adjustments we developed an algorithm for demand-responsive pump control based on a reliable suction detection system. METHODS: A PC-based system that analyzes pump performance based on available flow, heart rate and short-term performance history was developed. The physician defines levels of "desired flow" at rest and during exercise, depending on heart rate. In case this desired flow cannot be maintained due to limited venous return, the maximal available flow level is determined from an analysis of the actual pump data (flow, speed and power consumption). An expert system continuously checks the flow signal for any indication of suction. Periodic speed variations then adapt pump performance to the patient's condition. RESULTS: First, stability and functionality were proven under various settings in vitro. The algorithms were then tested in 15 patients in intensive care, in the standard ward, and during bicycle exercise. The system reacted properly to demand changes, at exercise level, in response to coughing and at various Valsalva maneuvers. Suction could also be successfully prevented during severe arrhythmia and in patients with critical cardiac geometry. Exercise tests showed decreases in pulmonary arterial pressure (-22 +/- 9.9%) and pulmonary capillary wedge pressure (-42 +/- 18.54%), and an increase in pump flow (19 +/- 9.5%) and workload (8 +/- 6.1%), all when compared with constant-speed pumping. CONCLUSIONS: A closed-loop control system equipped with an expert system for reliable suction detection was developed that improves response to change in venous return for rotary pump recipients. The system was robust, stable and safe under a wide range of everyday living conditions.
BACKGROUND: Axial blood pumps have been very successfully introduced into the arena of prolonged clinical support. However, they do not offer inherent load-responsive mechanisms for adjusting pumping performance to venous return and changes in physiologic requirements of the patient. To provide for these adjustments we developed an algorithm for demand-responsive pump control based on a reliable suction detection system. METHODS: A PC-based system that analyzes pump performance based on available flow, heart rate and short-term performance history was developed. The physician defines levels of "desired flow" at rest and during exercise, depending on heart rate. In case this desired flow cannot be maintained due to limited venous return, the maximal available flow level is determined from an analysis of the actual pump data (flow, speed and power consumption). An expert system continuously checks the flow signal for any indication of suction. Periodic speed variations then adapt pump performance to the patient's condition. RESULTS: First, stability and functionality were proven under various settings in vitro. The algorithms were then tested in 15 patients in intensive care, in the standard ward, and during bicycle exercise. The system reacted properly to demand changes, at exercise level, in response to coughing and at various Valsalva maneuvers. Suction could also be successfully prevented during severe arrhythmia and in patients with critical cardiac geometry. Exercise tests showed decreases in pulmonary arterial pressure (-22 +/- 9.9%) and pulmonary capillary wedge pressure (-42 +/- 18.54%), and an increase in pump flow (19 +/- 9.5%) and workload (8 +/- 6.1%), all when compared with constant-speed pumping. CONCLUSIONS: A closed-loop control system equipped with an expert system for reliable suction detection was developed that improves response to change in venous return for rotary pump recipients. The system was robust, stable and safe under a wide range of everyday living conditions.
Authors: Michael Charles Stevens; Andrew P Bradley; Stephen J Wilson; David Glen Mason Journal: Med Biol Eng Comput Date: 2013-03-23 Impact factor: 2.602
Authors: Richard Severin; Ahmad Sabbahi; Cemal Ozemek; Shane Phillips; Ross Arena Journal: Expert Rev Med Devices Date: 2019-09-06 Impact factor: 3.166
Authors: Diyar Saeed; Alex L Massiello; Shanaz Shalli; Hideyuki Fumoto; Tetsuya Horai; Tomohiro Anzai; Leonard A R Golding; Kiyotaka Fukamachi Journal: J Heart Lung Transplant Date: 2010-01 Impact factor: 10.247
Authors: Martin Maw; Thomas Schlöglhofer; Christiane Marko; Philipp Aigner; Christoph Gross; Gregor Widhalm; Anne-Kristin Schaefer; Michael Schima; Franziska Wittmann; Dominik Wiedemann; Francesco Moscato; D'Anne Kudlik; Robert Stadler; Daniel Zimpfer; Heinrich Schima Journal: Front Cardiovasc Med Date: 2022-04-25