Abstract
Anemia management with erythropoiesis stimulating agents is a challenging task in hemodialysis patients since their response to treatment varies highly. In general, it is difficult to achieve and maintain the predefined hemoglobin (Hgb) target levels in clinical practice. The aim of this study is to develop a fully personalizable controller scheme to stabilize Hgb levels within a narrow target window while keeping drug doses low to mitigate side effects. First in-silico results of this framework are presented in this paper. Based on a model of erythropoiesis we formulate a non-linear model predictive control (NMPC) algorithm for the individualized optimization of epoetin alfa (EPO) doses. Previous to this work, model parameters were estimated for individual patients using clinical data. The optimal control problem is formulated for a continuous drug administration. This is currently a hypothetical form of drug administration for EPO as it would require a programmable EPO pump similar to insulin pumps used to treat patients with diabetes mellitus. In each step of the NMPC method the open-loop problem is solved with a projected quasi-Newton method. The controller is successfully tested in-silico on several patient parameter sets. An appropriate control is feasible in the tested patients under the assumption that the controlled quantity is measured regularly and that continuous EPO administration is adjusted on a daily, weekly or monthly basis. Further, the controller satisfactorily handles the following challenging problems in simulations: bleedings, missed administrations and dosing errors.
Highlights
According to the 2018 United States Renal Data System annual data report (USRDS 2018), approximately 2.5 million patients were treated world-wide for end-stage renal disease in 2016
The presented non-linear model predictive control (NMPC) algorithm to correct anemia in HD patients was tested in various in-silico experiments and showed excellent performance in stabilizing simulated Hgb levels
The introduced framework uses a system of non-linear hyperbolic partial differential equations (PDEs), which previously has been adapted to individual patients using clinically measured Hgb levels to predict individual patient response to epoetin alfa (EPO) treatment (Fuertinger et al 2018)
Summary
According to the 2018 United States Renal Data System annual data report (USRDS 2018), approximately 2.5 million patients were treated world-wide for end-stage renal disease in 2016. Dialysis facilities use dosing protocols that work as follows: A starting dose gets specified and based on the resulting Hgb change and depending on whether the patient is within the Hgb target range the ESA dose gets adjusted. In 2011, Brier and Gaweda have already published an MPCbased algorithm for improved anemia management that has been tested and validated in clinical studies Unlike our approach their predictive model is based on the concept of artificial neural networks (see Barbieri et al 2016; Brier et al 2010). The presented feedback controller is tested on various patient parameter sets Throughout this manuscript we assume that EPO is administered continuously. All parameters used for simulations are presented in Appendix A
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