title: Automated non-invasive detection of pumping states in an implantable rotary blood pump.
abstract: 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.
authors.affiliation: Graduate School of Biomedical Engineering, University of New South Wales, Sydney NSW, Australia.
authors.forename: Dean M
authors.initials: DM
authors.lastname: Karantonis
authors.forename: Shaun L
authors.initials: SL
authors.lastname: Cloherty
authors.forename: David G
authors.initials: DG
authors.lastname: Mason
authors.forename: Robert F
authors.initials: RF
authors.lastname: Salamonsen
authors.forename: Peter J
authors.initials: PJ
authors.lastname: Ayre
authors.forename: Nigel H
authors.initials: NH
authors.lastname: Lovell
mesh_terms: 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