Patients with a single ventricle congenital heart defect are prone to increased volume loading, which can lead to heart failure and require mechanical circulatory support. A ventricular assist device (VAD) can serve as a bridge treatment option for these patients. However, in VAD support cases, pediatric patients possessing congenital heart defects have lower survival rates than patients without and outcomes worsen further in single ventricle cases. Performance differences between pulsatile and continuous flow VADs have also been clinically observed, but the underlying mechanism remains poorly understood.
Six pediatric, stage 1 single ventricle patients (cohort mean BSA = 0.30 m2) were considered. The cardiovascular system was computationally simulated using a lumped-parameter network (LPN) tuned to patient specific data. A first set of simulations emulated current clinical implementation of VADs in single ventricle patients. A second set modified VAD settings with the goal to further improve cardiac output (CO).
For all patients, optimal CO was at least 1 L min-1 greater with the continuous flow VAD compared that of pulsatile flow (p=0.0009). The 25 and 50 mL pulsatile flow VADs exhibited incomplete filling at higher heart rates that reduced CO as much as 0.26 and 1.4 L min-1 (9.7% and 37.3%) below design expectations respectively. Optimization of pulsatile flow VAD settings to improve filling did not achieve statistically significant (p\u3c0.05) improvement. Results corroborate anecdotal clinical experience associating continuous flow VADs with superior CO and ventricular unloading in single ventricle patients. Future work should aim to improve models for ventricular suction resistance and the passive pressure-volume relationship at negative ventricular pressures.
As part of future work, the single ventricle LPN was modified to simulate resting and exercise physiologies of example adult patients with normal bi-ventricular circulations. Correlations with exercise level for key physiological parameters were developed using prior literature data. Considerations for patient fitness level and age were also incorporated as appropriate. This model produced resting physiology within tolerance of prior literature data and exercise physiologies for two example patients within 10% of prior data for CO and mean arterial pressure. This modified LPN serves as a platform for future work in computational studies of bi-ventricular patients
