Detailed mechanism and impact of new-onset late right heart failure during LVAD support

Encouraging Regular Aortic Valve Opening for EVAHEART 2 LVAD Support Using Virtual Patient Hemodynamic Speed Modulation Analysis

This study focuses on investigating the EVAHEART 2 left ventricular assist device (LVAD) toward designing optimal pump speed modulation (PSM) algorithms for encouraging aortic valve (AV) flow. A custom-designed virtual patient hemodynamic model incorporating the EVAHEART 2 pressure-flow curves, cardiac chambers, and the systemic and pulmonary circulations was developed and used in this study. Several PSM waveforms were tested to evaluate their influence on the mean arterial pressure (MAP), cardiac output (CO), and AV flow for representative heart failure patients. Baseline speeds were varied from 1,600 to 2,000 rpm. For each baseline speed, the following parameters were analyzed: 1) PSM ratio (reduced speed/baseline speed), 2) PSM duration (3-7 seconds), 3) native ventricle contractility, and 4) patient MAP of 70 and 80 mm Hg. More than 2,000 rpm virtual patient scenarios were explored. A lower baseline speed (1,600 and 1,700 rpm) produced more opportunities for AV opening and more AV flow. Higher baseline speeds (1,800 and 2,000 rpm) had lower or nonexistent AV flow. When analyzing PSM ratios, a larger reduction in speed (25%) over a longer PSM (5+ seconds) duration produced the most AV flow. Lower patient MAP and increased native ventricle contractility also contributed to improving AV opening frequency and flow. This study of the EVAHEART 2 LVAD is the first to focus on leveraging PSM to enhance pulsatility and encourage AV flow. Increased AV opening frequency can benefit aortic root hemodynamics, thereby improving patient outcomes.

A Computational Hemodynamics Approach to Left Ventricular Assist Device (LVAD) Optimization Validated in a Large Patient Cohort

With increasing use of left ventricular assist devices (LVAD) it is critical to devise strategies to optimize LVAD speed while controlling mean arterial pressure (MAP) and flow according to patient physiology. The complex interdependency between LVAD speed, MAP, and flow frequently makes optimization difficult under clinical conditions. We propose a method to guide this procedure in silico, narrowing the conditions to test clinically. A computational model of the circulatory network that simulates HF and LVAD support, incorporating LVAD pressure-flow curves was applied retrospectively to anonymized patient hemodynamics data from the University of Washington Medical Center. MAP management on 61 patient-specific computational models with a target of 70 mm Hg, resulting flow for a given LVAD speed was analyzed, and compared to a target output of 5 L/min. Before performing virtual MAP management, 51% had a MAP>70 mm Hg and CO>5 L/min, and 33% had a MAP>70 mm Hg and CO<5 L/min. After changing systemic resistance to meet the MAP target (without adjusting LVAD speed), 84% of cases resulted in CO higher than 5 L/min, with a median CO of 6.79 L/min, using the computational predictive model. Blood pressure management alone is insufficient in meeting both MAP and CO targets, due to the risk of hypervolemia, and requires appropriate LVAD speed optimization to achieve both targets, while preserving right heart health. Such computational tools can narrow down conditions to be tested for each patient, providing significant insight into the pump-patient interplay. LVAD hemodynamic optimization has the potential to reduce complications and improve outcomes.