Noninvasive flow waveform parameters had strong correlations (r range: 0.87–0.99) with hemodynamic changes, with amplitude serving as the best preload predictor (r²=0.67).
Can noninvasive flow waveform assessment using echocardiography and HM3 Snoopy accurately estimate flow and predict hemodynamic changes in a HeartMate 3 mock loop model?
Echocardiography-derived pulsed wave Doppler and the HM3 Snoopy system can noninvasively generate flow waveform surrogates that correlate well with direct flow probe measurements and predict hemodynamic changes in a mock loop model.
Absolute Event Rate: 0% vs 0%
The instantaneous left ventricular assist device (LVAD) flow waveform of the HeartWare ventricular assist device (HVAD) device was previously used to assess hemodynamic parameters, which is not possible with the HeartMate 3 (HM3). The purpose of this study was to assess the ability of pulsed wave Doppler interrogation of the outflow graft (PWOG) and of a novel noninvasive pump data acquisition system (HM3 Snoopy) to obtain noninvasive flow waveforms (NIFW) and to determine whether these predict hemodynamic changes. The study was conducted using a fluid-filled, biventricular failure mock loop model with a flow probe (FP) placed around the distal outflow. Five different pump speeds, two distinct hematocrits, and preload and afterload changes were used to create 56 unique hemodynamic states. Noninvasive flow waveform parameters were assessed for correlation against FP-derived parameters. Subsequently, NIFW parameters were assessed for their predictive capabilities for preload and afterload changes. There was moderate correlation between NIFW and FP-derived systolic waveform parameters ( r range: 0.43–0.81), and strong correlation for diastolic parameters ( r range: 0.87–0.99). Flow waveform amplitude as obtained by echocardiography was the best preload predictor ( r 2 = 0.67). A multiple linear regression model of NIFW parameters provided adequate prediction of afterload ( r 2 = 0.85). HeartMate 3 Snoopy and PWOG are promising tools for generating flow waveform surrogates and detecting hemodynamic changes.
Deveza et al. (Mon,) reported a other. Noninvasive flow waveform parameters had strong correlations (r range: 0.87–0.99) with hemodynamic changes, with amplitude serving as the best preload predictor (r²=0.67).
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