title: Novel echocardiographic parameters of aortic insufficiency in continuous-flow left ventricular assist devices and clinical outcome.
abstract: The aim of this study was to evaluate the prognostic performance of novel echocardiographic (transthoracic echocardiography, or TTE) parameters for grading aortic insufficiency (AI) severity in patients with continuous-flow left ventricular assist devices (CF-LVADs). The development of AI after CF-LVAD implantation is common, although the clinical significance remains unclear. We previously described novel TTE parameters that outperformed traditional TTE parameters in grading AI severity in these patients.
  
  
  
  CF-LVAD patients with varying degrees of AI (N = 57) underwent Doppler TTE of the LVAD outflow cannula. Patients had AI severity graded by the novel parameters (systolic/diastolic velocity ratio and the diastolic acceleration of the LVAD outflow cannula) and the traditional vena contracta. The prognostic performance of novel and traditional AI parameters was determined by comparing rates of congestive heart failure re-admission, need for aortic valve intervention, urgent transplantation and death (composite end-points) for each parameter.
  
  
  
  Grading AI severity using novel AI parameters led to reclassification of 32% of patients from trace/mild AI to moderate or greater AI (N = 18). Using traditional AI parameters, there was no difference in the occurrence of the composite end-point between the moderate or greater group and the trace/mild group (1.50 vs 1.18 events/person, p = 0.46). With the novel AI parameters, there were significantly more events in the patients with moderate or greater AI compared to those with trace/mild AI (1.57 vs 0.13 events/person, p = 0.002). Novel parameters also better predicted the need for aortic valve intervention, urgent transplantation or death than traditional methods (p = 0.024 vs p = 0.343).
  
  
  
  In patients with CF-LVADs, traditional parameters tend to underestimate AI severity and future cardiac events. Novel AI TTE parameters are better able to discriminate AI severity and predict clinically meaningful outcomes.
authors.affiliation: Department of Medicine, University of Chicago Medical Center, Chicago, Illinois, USA.
authors.forename: Jonathan
authors.initials: J
authors.lastname: Grinstein
authors.affiliation: Department of Medicine, University of Chicago Medical Center, Chicago, Illinois, USA.
authors.forename: Eric
authors.initials: E
authors.lastname: Kruse
authors.affiliation: Department of Medicine, University of Chicago Medical Center, Chicago, Illinois, USA.
authors.forename: Gabriel
authors.initials: G
authors.lastname: Sayer
authors.affiliation: Center for Medical Ethics and Health Policy, Baylor College of Medicine, Houston, Texas, USA.
authors.forename: Savitri
authors.initials: S
authors.lastname: Fedson
authors.affiliation: Department of Medicine, University of Chicago Medical Center, Chicago, Illinois, USA.
authors.forename: Gene H
authors.initials: GH
authors.lastname: Kim
authors.affiliation: Department of Medicine, University of Chicago Medical Center, Chicago, Illinois, USA.
authors.forename: Nitasha
authors.initials: N
authors.lastname: Sarswat
authors.affiliation: Department of Medicine, University of Chicago Medical Center, Chicago, Illinois, USA.
authors.forename: Sirtaz
authors.initials: S
authors.lastname: Adatya
authors.affiliation: Department of Surgery, University of Chicago Medical Center, Chicago, Illinois, USA.
authors.forename: Takeyoshi
authors.initials: T
authors.lastname: Ota
authors.affiliation: Department of Surgery, University of Chicago Medical Center, Chicago, Illinois, USA.
authors.forename: Valluvan
authors.initials: V
authors.lastname: Jeevanandam
authors.affiliation: Department of Medicine, University of Chicago Medical Center, Chicago, Illinois, USA.
authors.forename: Victor
authors.initials: V
authors.lastname: Mor-Avi
authors.affiliation: Department of Medicine, University of Chicago Medical Center, Chicago, Illinois, USA.
authors.forename: Roberto M
authors.initials: RM
authors.lastname: Lang
authors.affiliation: Department of Medicine, University of Chicago Medical Center, Chicago, Illinois, USA. Electronic address: nuriel@medicine.bsd.uchicago.edu.
authors.forename: Nir
authors.initials: N
authors.lastname: Uriel
mesh_terms: D001021:Aortic Valve; D001022:Aortic Valve Insufficiency; D004452:Echocardiography; D015150:Echocardiography, Doppler; D006333:Heart Failure; D006353:Heart-Assist Devices; D006801:Humans
keywords: aortic insufficiency; clinical outcomes; diastolic acceleration of the LVAD outflow cannula; left ventricular assist device (LVAD); systolic to diastolic velocity (S/D) ratio of the LVAD outflow cannula