Hemodialysis (HD) is nowadays the most common therapy to treat renal insufficiency. However, despite the improvements made in the last years, HD is still associated with a non-negligible rate of co-morbidities, which could be reduced by means of an appropriate treatment customization. Many differential multi-compartment models have been developed to describe solute kinetics during HD, to optimize treatments, and to prevent intra-dialysis complications; however, they often refer to an average uremic patient. On the contrary, the clinical need for customization requires patient-specific models. In this work, assuming that the customization can be obtained by means of patient-specific model parameters, we propose a Bayesian approach to estimate the patient-specific parameters of a multi-compartment model and to predict the single patient’s response to the treatment, in order to prevent intra-dialysis complications. The likelihood function is obtained through a discretized version of a multi-compartment model, where the discretization is in terms of a Runge–Kutta method to guarantee the convergence, and the posterior densities of model parameters are obtained through Markov Chain Monte Carlo simulation.

Identification of patient-specific parameters in a kinetic model of fluid and mass transfer during dialysis

BIANCHI, CAMILLA;CASAGRANDE, GIUSTINA;COSTANTINO, MARIA LAURA
2017

Abstract

Hemodialysis (HD) is nowadays the most common therapy to treat renal insufficiency. However, despite the improvements made in the last years, HD is still associated with a non-negligible rate of co-morbidities, which could be reduced by means of an appropriate treatment customization. Many differential multi-compartment models have been developed to describe solute kinetics during HD, to optimize treatments, and to prevent intra-dialysis complications; however, they often refer to an average uremic patient. On the contrary, the clinical need for customization requires patient-specific models. In this work, assuming that the customization can be obtained by means of patient-specific model parameters, we propose a Bayesian approach to estimate the patient-specific parameters of a multi-compartment model and to predict the single patient’s response to the treatment, in order to prevent intra-dialysis complications. The likelihood function is obtained through a discretized version of a multi-compartment model, where the discretization is in terms of a Runge–Kutta method to guarantee the convergence, and the posterior densities of model parameters are obtained through Markov Chain Monte Carlo simulation.
Bayesian Statistics in Action
9783319540832
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11311/1032287
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