Computational modelling of hollow fibre haemodialysers: current status and future directions

Current in silico dialyser models remain accurate only under narrowly constrained conditions. Advances in computing power and increasing levels of interdisciplinary collaboration, however, now set the stage for more robust computational haemodialyser models. In this review, we survey existing modelling approaches-long used to guide haemodialyser design and clinical nephrology practice-identify key unresolved challenges, and propose computational strategies poised to accelerate the development of dialyser technologies that better meet the needs of kidney failure patients.

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Publication Details

Journal
Communications Engineering
Published
2025-11-20
DOI
https://doi.org/10.1038/s44172-025-00526-y
Primary Topic
Dialysis and Renal Disease Management
Type
article
Field-Weighted Citation Impact
0.00

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article

Computational modelling of hollow fibre haemodialysers: current status and future directions

Anne Staples, R.C. Sinha
Communications Engineering
Dialysis and Renal Disease Management
article

Computational modelling of hollow fibre haemodialysers: current status and future directions

Anne Staples, R.C. Sinha
article en

Abstract

Current in silico dialyser models remain accurate only under narrowly constrained conditions. Advances in computing power and increasing levels of interdisciplinary collaboration, however, now set the stage for more robust computational haemodialyser models. In this review, we survey existing modelling approaches-long used to guide haemodialyser design and clinical nephrology practice-identify key unresolved challenges, and propose computational strategies poised to accelerate the development of dialyser technologies that better meet the needs of kidney failure patients.

Communications EngineeringVol. 4(1)
Virginia Tech - Wake Forest University School of Biomedical Engineering & Sciences (US), Virginia Tech (US)
National Science Foundation, Division of Graduate Education
Openalex Percentile: Top 58%
Dialysis and Renal Disease Management
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