Computational modelling of peritoneal dialysis: an overview

TL;DR

This paper reviews the development and application of computational models for peritoneal dialysis, aiming to optimize and personalize treatment by understanding complex interactions in PD therapy.

Contribution

It provides a comprehensive overview of the history, construction, and existing models of computational PD, highlighting their potential in treatment optimization.

Findings

Computational models help understand PD processes at a patient-specific level.

Existing models can be used to optimize PD therapy.

Historical development of PD models is summarized.

Abstract

Peritoneal dialysis (PD) is becoming more popular as a result of a rising interest in home dialysis, lower intrusion in social life and longer preservation of residual kidney function. However, PD has several important drawbacks: small solute clearance is relatively low compared to hemodialysis and technique survival is limited. Application of continuous flow, sorbent-based dialysate regeneration and novel glucose-sparing PD solutions are some solutions proposed to address the limitations of PD. To optimize and personalize current and novel PD therapies, patient peritoneal characteristics interacting with PD techniques need to be studied together and separately as they interplay. However, considering the multitude of parameters, it would be difficult, expensive, and time consuming to optimize all parameter settings only with the help of clinical trials. Mathematical modelling is an exciting tool to dissect these interacting processes and comprehend PD techniques better at a patient specific level. In this review, we look at the history of computational PD models, explore the many ways a computational PD model can be constructed and review the various existing PD models that can be used to optimize and personalize PD treatment.