Automated non-invasive detection of pumping states in an implantable rotary blood pump.
With respect to rotary blood pumps used as left ventricular assist devices (LVADs), it is clinically important to control pump flow to avoid complications associated with over-or under-pumping of the native heart. By employing only the non-invasive observer of instantaneous pump impeller speed to assess flow dynamics, a number of physiologically significant pumping states may be detected. Based on a number of acute animal experiments, five such states were identified: regurgitant pump flow (PR), ventricular ejection (VE), non-opening of the aortic valve (ANO), and partial collapse (intermittent and continuous) of the ventricle wall (PVC-I and PVC-C). Two broader states, normal (corresponding to VE, ANO) and suction (corresponding to PVC-I, PVC-C) were readily discernable in clinical data from human patients implanted with LVADs. Based on data from both the animal experiments (N=6) and the human patients (N=10), a strategy for the automated non-invasive detection of significant pumping states has been developed and validated. Employing a classification and regression tree (CART), this system detects pumping states with a high degree of accuracy: state VE -87.5/100.0% (sensitivity/specificity); state ANO - 98.1/92.5%; state PVC-I - 90.0/90.2%; state PVC-C - 61.2/98.0%. With a simplified binary scheme differentiating suction and normal states, both states were detected without error in data from the animal experiments, and with a sensitivity/specificity, for detecting suction, of 99.2/98.3% in the human patient data.
Karantonis
Dean M
DM
Graduate School of Biomedical Engineering, University of New South Wales, Sydney NSW, Australia.
Cloherty
Shaun L
SL
Mason
David G
DG
Salamonsen
Robert F
RF
Ayre
Peter J
PJ
Lovell
Nigel H
NH
D000818:Animals; D001021:Aortic Valve / Q000473:pathology; D001243:Assisted Circulation / Q000295:instrumentation* / Q000379:methods*; D001331:Automation; D004867:Equipment Design; D006352:Heart Ventricles / Q000473:pathology; D006353:Heart-Assist Devices; D006801:Humans; D011673:Pulsatile Flow; D012044:Regression Analysis; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D012815:Signal Processing, Computer-Assisted; D013552:Swine; D013997:Time Factors