Abstract 16373: Improved Prognostic Performance of Novel Parameters for Grading Aortic Insufficiency Severity in Patients With Left Ventricular Assist Devices
Background: The development of aortic insufficiency (AI) following continuous flow left ventricular assist device (CF-LVAD) implantation is common, although the clinical significance remains unclear. We previously described novel echocardiographic (TTE) parameters that outperformed traditional TTE parameters in grading AI severity in these patients. The aim of this study was to evaluate the prognostic performance of these parameters compared to traditional measurements.
Methods: CF-LVAD patients with varying degrees of AI (N=57) underwent Doppler TTE of the LVAD outflow cannula. All patients had AI severity graded by our novel parameters (Systolic/Diastolic ratio and the diastolic acceleration of the LVAD outflow cannula) and traditional vena contracta. The prognostic performance of novel and traditional AI parameters was determined by comparing rates of hospital readmission, need for aortic valve intervention and/or urgent transplantation and death (primary endpoints) for each parameter.
Results: Grading AI severity using novel AI parameters led to reclassification of 21% of patients from trace/mild AI to moderate or greater AI (N=12). Using traditional AI parameters, there was no difference in the occurrence of the primary endpoint between the trace/mild group and the moderate or greater group (1.08 vs. 0.89 events/person, p=0.39) (Figure 1A). With the novel AI parameters, there were significantly more events in the patients with moderate or greater AI compared to those with trace/mild. (1.11 vs. 0.56 events/person, p=0.02) (Figure 1B). Novel parameters better predicted the need for aortic valve intervention, urgent transplantation or death than traditional methods (p=0.049) (Figure 1C-D).
Conclusions: 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.
Author Disclosures: J. Grinstein: None. E. Kruse: None. G. Sayer: None. S. Fedson: None. G.H. Kim: None. N. Sarswat: None. S. Adatya: None. T. Ota: None. V. Jeevanandam: Consultant/Advisory Board; Modest; Thoratec. V. Mor-Avi: None. R. Lang: Speakers Bureau; Modest; Phillips. Consultant/Advisory Board; Modest; Phillips. N. Uriel: Consultant/Advisory Board; Modest; Thoratec, Heartware.
- © 2015 by American Heart Association, Inc.