Patient-oriented Dialysis Condition Setting and Treatment Outcome Prediction Software

Enables measurement of patient-specific physical data and supports easy/accurate setting of dialysis treatment conditions


  • The original algorithm can accurately predict toxin concentration, including rebound
  • Semi-automated software has already been developed
  • A program to make it easier for doctors to make rapid diagnoses and to provide dialysis care at home and remotely

Background and Technology

When kidney function is impaired due to chronic kidney disease (CKD) or its progression to chronic renal failure, dialysis is the treatment of choice to replace kidney function without kidney transplantation. When dialysis treatment is diagnosed, it requires a great deal of effort and skill in analysis for doctors to set the appropriate conditions for dialysis time, blood volume, and clearance (solute removal capacity). However, the widely used index Kt/V, which takes into account the volume of water in the body (V), clearance (K), and dialysis time (t), is inadequate in expressing physical characteristics. Furthermore, there was no analytical model that could predict the toxin concentration during and after dialysis, including rebound.

This technology is an algorithm for predicting toxin concentration trends based on dialysis conditions, and has the ability to measure the patient’s physical characteristics (body weight, mass transfer coefficient, water content ratio, toxin production rate, and recirculation rate). The program also provides highly accurate suggestions for treatment conditions (dialysis time, dialyzer blood volume, dialyzer machine, etc.) to reach the target value (toxin concentration) based on the measured intrinsic physical values. This is expected to reduce the effort of data collection and analysis for each patient and the trial-and-error process to reach the appropriate conditions, thereby supporting the doctor’s diagnosis and increasing the patient’s satisfaction with the treatment.


The predicted extracellular fluid toxin concentrations obtained by this program were shown to accurately match the clinical data. It was also shown that the rebound after dialysis (after 240 minutes) could be predicted.


Sano Yoshihiko et al. Analytical Solutions of a Two-Compartment Model Based on the Volume-Average Theory for Blood Toxin Concentration during and after Dialysis. Membrane, 2021, 11.


International application already filed


Yoshihiko Sano, Kentaro Sato (Shizuoka University), Toyomu Ugawa (Tokyo Medical and Dental University), Narutoshi Kabashima (Hibiki Clinic)

Development Stage

Currently, this program is in the stage of semi-automation (Excel).

In the future, we are looking for partners who are interested in collaborating with us to collect evidence of predictive reproducibility using clinical data, to fully automate the software, to develop software tailored to usage scenarios and products, and to develop products and services.

Product No. ON-04171

Other than Medicine


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