Predicting the Impact of Dialyzer Choice and Binder Dialysate Flow Rate on Bilirubin Removal
Novokhodko, A.; Du, N.; Hao, S.; Wang, Z.; Shu, Z.; Ahmad, S.; Gao, D.
Show abstract
Liver failure is the 12th leading cause of death worldwide. Protein bound toxins such as bilirubin are responsible for many complications of the disease. Binder dialysis systems use albumin dialysate and detoxifying sorbent columns to remove these toxins. Systems like the Molecular Adsorbent Recirculating System (MARS) and BioLogic-DT have existed since the 1990s, but survival benefit in randomized controlled trials have not been consistent. Thus, a new generation of binder dialysis systems, including Open Albumin Dialysis (OPAL) and the Advanced Multi-Organ Replacement System (AMOR) are being developed. Optimal conditions for binder dialysis have not been established. We developed and validated a computational model of bound solute dialysis using established thermodynamic theories. Our objective is to improve AMOR therapy. We confirmed our models validity by predicting the impact of changing between two benchtop dialysis setups using different polysulfone dialyzers (F3 and F6HPS). We then applied it to predict the impact of varying dialysate flow rate on toxin removal. We found that bilirubin removal is independent of dialysate flow rate within the clinically relevant range (20 mL/min - 800 mL/min), matching our models predictions. At very low dialysate flow rates (2 mL/min), bilirubin removal declines, deviating from the thermodynamic model. This model may be useful to achieving optimal clinical outcomes by setting optimal dialyzer and flow rate conditions. Further improvement is possible by accounting for toxin adsorption onto the dialyzer membrane.
Matching journals
The top 9 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Organising outpatient dialysis services during the COVID-19 pandemic. A simulation and mathematical modelling study. 92%
- Efficacy of Tenapanor in Managing Hyperphosphatemia and Constipation in Hemodialysis Patients: A Randomized Controlled Trial 91%
- Prevalence and determinants of poor glycemic control among diabetic chronic kidney disease patients on maintenance hemodialysis in Tanzania 90%
Similar papers in this journal
Similar papers in this journal
- Bridging the gap between in silico and in vivo: modeling opioid disposition in a kidney proximal tubule microphysiological system 90%
- Machine learning for classifying chronic kidney disease and predicting creatinine levels using at-home measurements 90%
- Estimation of the Hemoglobin Glycation Rate Constant 89%
Similar papers in this journal
Similar papers in this journal
- A Portable Impedance Microflow Cytometer for Measuring Cellular Response to Hypoxia 89%
- Analytical solution for a hybrid Logistic-Monod cell growth model in batch and CSTR culture 87%
- A generalized machine-learning aided method for targeted identification of industrial enzymes from metagenome: a xylanase temperature dependence case study 87%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.