ASN's Mission

To create a world without kidney diseases, the ASN Alliance for Kidney Health elevates care by educating and informing, driving breakthroughs and innovation, and advocating for policies that create transformative changes in kidney medicine throughout the world.

learn more

Contact ASN

1401 H St, NW, Ste 900, Washington, DC 20005

email@asn-online.org

202-640-4660

The Latest on X

Kidney Week

Abstract: TH-PO324

Computational Models of Peritoneal Dialysis

Session Information

  • Home Dialysis - I
    November 02, 2023 | Location: Exhibit Hall, Pennsylvania Convention Center
    Abstract Time: 10:00 AM - 12:00 PM

Category: Dialysis

  • 802 Dialysis: Home Dialysis and Peritoneal Dialysis

Authors

  • Swapnasrita, Sangita, Universiteit Maastricht, Maastricht, Limburg, Netherlands
  • de Vries, Joost Christiaan, Universitair Medisch Centrum Utrecht, Utrecht, Utrecht, Netherlands
  • Öberg, Carl Mikael, Lunds Universitet, Lund, Sweden
  • Gerritsen, Karin G., Universitair Medisch Centrum Utrecht, Utrecht, Utrecht, Netherlands
  • Carlier, Aurélie, Universiteit Maastricht, Maastricht, Limburg, Netherlands
Background

In silico models may play a vital role in improving patient-specific kidney replacement therapies. Recent advances in mathematical modeling include models of kidney physiology (e.g. regarding sex-specific differences, solute, drug and toxin transport and their interactions). Various models have been generated for peritoneal dialysis (PD) as well, but what lacks is a benchmarking of the different models on the same (pre-)clinical dataset. Here, we look at previously published models of PD and benchmark the efficiency of the models in predicting time-dependent evolution dialysate concentrations of six solutes.

Methods

Two mechanistic models (Graff, Öberg (modified for static dwells; this model is also modular and has been applied to automated PD and continuous flow PD)) and two analytical models used in clinical practice and research (Garred, Waniewski) were chosen. The four models, in combination, encompass various mechanisms that are essential to PD (diffusion, convection, lymphatics). The dataset consisted of data from multiple static dwells (n = 16) in uremic pigs. Each model was trained by fitting the dialysate solute concentrations (in a subset of the dwells) to predict the mass transfer area coefficient (MTAC) of each solute. With the fitted MTAC, we predicted the dialysate solute concentrations in the remaining dwells.

Results

The (modified) model by Öberg appears to be the optimal model in terms of low error in solute concentration predictions, applicability of the model to multiple datasets (with different initial dialysate concentration), physiological MTAC values and reasonable ultrafiltration values in pigs. Applying the modified Öberg model to the data obtained in the uremic pig experiments showed a good predictive accuracy (Figure 1). Notably, this model accurately predicted the effects of sodium sieving, whereas other models did not.

Conclusion

The modified Öberg model provided an accurate prediction of solute concentrations throughout a static dwell in uremic pigs.

Figure 1: comparison of predicted data by the Öberg model with pig data.

Funding

  • Private Foundation Support