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.