Biomedical subjects

Gino Morello

Publications and source records attributed to Gino Morello.

4 recordsLinked to original sources

PubMed2006

First clinical experience with an automatic control system for rotary blood pumps during ergometry and right-heart catheterization.

BACKGROUND: At present, most clinically implanted rotary blood pumps are operated at constant speed and adjusted by the physician. It is generally assumed that an adaptation of pump speed to the patient's physiologic requirements would be beneficial. The data provided in this paper, based on hemodynamic and spirometric data during exercise in which a pre-load-sensitive control was used, lend quantitative support to this assumption. METHODS: An automatic speed control was developed and implemented with Matlab on a dSpace controller board. The system uses pump speed, pump power, and pump flow as its only input signals. It was connected to the clinical hardware of the DeBakey VAD System. The control is pre-load-sensitive and uses an expert system to detect excessive unloading and eventual suction. This system was used to quantify the cardiovascular reaction of patients to both automatically controlled and constant pump speed. A sub-group of 5 patients underwent bicycle ergometry with Swan-Ganz catheterization and spiroergometry. RESULTS: The automatic, closed-loop speed control showed robust and stable performance. It provided an increase in pump flow (+0.94 +/- 0.5 liters/min, p < 0.05) compared with constant-speed mode in response to physical activity. Pulmonary arterial (PAP) and capillary wedge pressure (PCWP) clearly decreased (-7.4 +/- 4.1 mm Hg for PAP and -8.3 +/- 4.2 mm Hg for PCWP, p < 0.05), and venous oxygen saturation moderately increased (+5.2%). CONCLUSION: An automatic speed-control system for rotary blood pumps was developed and demonstrated by spiroergometry to be appropriately responsive to physiologic demand.

Algorithms↗

PubMed2006

Advanced suction detection for an axial flow pump.

An automatic detection system for ventricular collapse was developed and tested in a first clinical trial as part of a physiological speed control concept for axial flow pumps. From this clinical experience, and based on the acquired data during this trial, an optimization of the developed system was performed. An already-existing database of 784 individual cases was extended. For harmonization of this database an additional 412 snap files were extracted from continuous data recordings and classified manually using a standardized procedure. The already-developed and clinically tested algorithms were supplemented by one additional indicator derived from a preexisting criterion. One threshold value was replaced by application of a numerically optimized nonlinear characteristic curve dependent on heart rate. Finally, in a multidimensional optimization process of the entire suction detection system, 7 individual indicators were adjusted by using 17 independent threshold values. The optimization criteria were applied using a three-level hierarchical system. Within the final database consisting of 1196 snap shots the overall amount of maldetections could be reduced to 23 cases including 5 false positive events (0.42%) and 18 false negative decisions (1.5%). By application of the clinical experience from the first clinical trial of a physiologic control system it became possible to optimize the sensitivity and specificity of the suction detection system to unprecedented accuracy.

Algorithms↗

PubMed2005

Development of a reliable automatic speed control system for rotary blood pumps.

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.

Algorithms↗

PubMed2004

Development of a suction detection system for axial blood pumps.

Axial flow blood pumps for cardiac assistance have proven their clinical viability and benefit in recent years. However, the clinical systems to date have no direct mechanism to decrease pump speed when adequate supply is not available. This may lead to ventricular collapse or increase the probability of hemolysis and thrombotic risks. Based on various experiences with left ventricular assist device (LVAD) patients in various states of recovery, at implant, in the intensive care unit, in the standard ward, and during physical exercise, 11 different algorithms were developed for the automatic detection of ventricular suction. These detection algorithms analyze the flow pattern for the presence of distinct suction indicators. For selection and optimization of the algorithms, 1000 records from approximately 100 patients were collected. Each record contains 5 s of pump flow, current, and arterial pressure. Three experts classified these records in terms of suction probability and other abnormalities. The optimization was developed in Matlab, capable of solving a fifth-dimensional optimization problem with 256 different algorithm combinations. The optimization resulted in a set of 6 algorithms, each with specific thresholds. The system detects 100% of the known suction events with 0.28% of false-positive interpretations. If tuned to avoid any false-positive detection, 90.7% of the certain events would be detected. A strategy for the development of a robust suction detection system for axial blood pumps was found. This system will be integrated into an automatic pump speed control system to provide adequate perfusion for the LVAD recipient, without excessive unloading of the ventricle.

Algorithms↗