Designing Robust Ultrafiltration Rate Profiles Based on Identifying Fluid Volume Model Parameters During Hemodialysis

Abstract

Chronic dialysis is a necessary treatment for end-stage kidney disease (ESKD) patients in order to increase life span, with hemodialysis (HD) being the dominant modality. Despite significant advances in HD technology, only half of ESKD patients treated with this modality survive more than 3 years. Fluid management remains one of the most challenging aspects of HD care, with serious implications for morbidity and mortality. Ultrafiltration has been associated with intradialytic hypotension, also associated with adverse outcomes. Therefore, removing a specified fluid volume to achieve an adequate balance without negative outcomes remains a critical challenge to improving patient outcomes. Therefore, it has been suggested that in addition to blood pressure information, routine HD treatments should include blood volume monitoring. Sensors integrated in dialysis machines are able to track the concentration of various blood components, such as hematocrit, with high accuracy and resolution and to derive a relative blood volume (RBV) changes. In this paper, we propose a novel algorithm to design an optimal, robust ultrafiltration rate profile based on identifying the parameters of a fluid volume model of an individual patient during HD and RBV sensor. Our design achieves, if exists, an optimal ultrafiltration profile for the identified nominal model under maximum ultrafiltration and hematocrit constraints, and guarantees that these constraints are satisfied over a pre-defined set of parameter uncertainty. We demonstrate the performance of our algorithm through a combination of clinical data and simulations.

Nonlinear Parameter and State Estimation Approach for Intradialytic Measurement of Absolute Blood Volume

“…The volume kinetics during ultrafiltration is described by a previously published two-compartment extracellular fluid volume model [8,9]. Some modifications such as a constant protein mass in each compartment and a lymphatic flow component were introduced to the model [8,[10][11][12] (Fig. 1).…”

Nonlinear Parameter and State Estimation Approach in End-stage Kidney Disease Patients

“…The model is given by a validated two-compartment fluid volume model defined by nonlinear continuous-time differential equations and used for ESKD patients. Previously, we have successfully used the linearized version of this model to develop a personalized UF method [10,29,30], but here we use the 'full' nonlinear model.…”

“…In contrast, the hydrostatic capillary pressure parameters (d 1 and d 2 ) promote fluid movement from the intravascular space to interstitial space and control the fluid shift/refill across the capillary membrane wall. These parameters vary among ESKD patients [22,30].…”