Objective: Estimators to accurately monitor ventricular relaxation, cardiac contractility and cardiac output (CO) during support of a left ventricular assist device (LVAD), based on the left ventricular pressure (LVP) and pump-intrinsic signals, were developed. The aim was to continuously monitor these cardiac parameters, hereby tracking the progression of disease and ensuring effectiveness of treatment.
Methods: Ventricular relaxation was estimated using the time constant of LVP decay, τ_p. Cardiac contractility was based on the maximum derivative of LVP, dP/dt_max versus the end-diastolic pressure (EDP). The CO was estimated using a hybrid monitoring scheme, that used the estimated pump flow (PF) in cardiac cycles with a closed aortic valve and a cardiovascular model in cycles with an open valve. The states of the cardiovascular model were estimated using an extended Kalman filter (EKF). The opening status of the aortic valve (AoV) was determined using a machine learning model that employed a quadratic discriminant analysis based on features of the LVP waveform. The estimators were evaluated using in-vitro and in vivo experiments on pigs that were earlier conducted within the Product Devel- opment Group Zurich (pd|z). The experiments contained preload, afterload, contractility and relaxation variations. The CO was evaluated in experiments from three control strategies (i.e. constant speed, physiological control and multi-objective control).
Results: The index τ_p uniformly resembled changes in left-ventricular relaxation. dP/dt_max versus EDP captured variations in cardiac contractility. Both were preload and afterload independent. The CO was estimated with a mean relative error of 11% compared to the true CO.
Conclusion: The feasibility and accuracy of using LVP based cardiac indices in LVAD assisted circulations was demonstrated.
Significance: The clinical implementation could provide clinicians with valuable and reliable information of the circulation of the patient, allowing for monitoring of progression of disease and improvement of treatment.