Comparing Computational Peritoneal Dialysis Models in Pigs and Patients.

Area of Science:

• Biomedical Engineering
• Computational Biology
• Nephrology

Background:

• Computational models are vital for optimizing peritoneal dialysis (PD) for kidney disease patients.
• Existing PD models require validation against in vivo data for personalized treatment development.

Purpose of the Study:

• To compare the accuracy of six computational models in predicting peritoneal dialysis outcomes.
• To assess model performance using in vivo data from pigs and humans.

Main Methods:

• Six computational models (UGM, TPM, GM, WM, UGM-18, SWM) were evaluated.
• Model predictions were compared against experimental data from PD sessions in pigs and humans.
• Dialysate concentrations of uremic toxins and electrolytes were predicted over a 4-hour dwell.

Main Results:

• The three-pore model (TPM) demonstrated enhanced physiological accuracy.
• Model predictions offer insights into inter-individual differences in ultrafiltration.
• Computational cost of TPM may limit its immediate clinical applicability.

Conclusions:

• Accurate computational models are crucial for tailoring PD regimens in kidney disease requiring dialysis (KDRD).
• Model complexity influences clinical applicability for real-time or portable systems.
• Future adaptive PD regimens could be developed based on patient-specific kinetics and fluid dynamics.