Recursive estimation of Systemic Vascular Resistance using measurements from a left ventricular assist device

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Date

2019-05

Authors

Pawar, Suraj Rajendra

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Abstract

Cardiovascular disease is the leading cause of deaths worldwide, and one of the ways to treat patients with congestive heart failure is to perform a heart transplant. As the demand for this procedure rises, the disproportionate availability of suitable donors needs to be countered with ways to care and sustain patients who are waiting for a transplant. In this regard, the use of left ventricular assist devices (LVAD) has increased. The research conducted in this Thesis is primarily concerned with the TORVAD [superscript TM] (Windmill Cardiovascular Systems In., Austin , TX), a rotary blood pump type LVAD. The load faced by the left ventricle during ejection of blood is termed as Systemic Vascular Resistance (SVR), and is an important parameter that can indicate cardiovascular health. Abnormalities in SVR have been found to be a good indicator of hypertension, heart failure, shock and sepsis. A consistently low SVR can even be a predictor of mortality. The goal of this Thesis is to investigate ways of recursively estimating SVR in a patient, by using measurements that the TORVAD [superscript TM] provides. The Extended Kalman Filter is used to develop an estimation algorithm based on a reduced order model of the cardiovascular system. The estimation accuracy of the algorithm is tested by generating data through simulations of a computational model of the cardiovascular system, and by collecting measurements from the TORVAD [superscript TM] while it operates in a mock circulation loop. The algorithm is found to estimate SVR satisfactorily using the available measurements, and is robust to different initial conditions.

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