Abstract: SA-PO873
Personalized Ultrafiltration Profile Design Using Crit-Line
Session Information
- Dialysis: Cardiovascular, BP, Volume
October 27, 2018 | Location: Exhibit Hall, San Diego Convention Center
Abstract Time: 10:00 AM - 12:00 PM
Category: Dialysis
- 701 Dialysis: Hemodialysis and Frequent Dialysis
Authors
- Abohtyra, Rammah M., University of Massachusetts, Amherst, Massachusetts, United States
- Horowitz, Joseph, University of Massachusetts, Amherst, Massachusetts, United States
- Hollot, Christopher V., University of Massachusetts, Amherst, Massachusetts, United States
- Germain, Michael J., Renal and Transplant Assoc of New England, Hampden, Massachusetts, United States
- Chait, Yossi, University of Massachusetts, Amherst, Massachusetts, United States
Background
Intradialytic hypotension (IDH) occurs in as many as 25%-50% of patients during fluid removal by ultrafiltration. We recently presented a novel approach for the design of personalized ultrafiltration rate (UFR) profiles. Our aim was to integrate this design with a parameter estimation algorithm for a patient’s fluid volume dynamics during hemodialysis (HD) from Crit-Line hematocrit (HCT) measurements.
Methods
We used a validated fluid volume model during HD comprising intravascular and interstitial pools, microvascular refilling/filtration, and lymphatic flow. We used several 20-min segments of UFR and HCT (Fig. 2) data from an actual HD treatment (3.5L removed in 227 min) and advanced algorithms to estimate key model parameters: plasma volume, interstitial volume, red blood cell volume, and filtration coefficient. Based on the estimated parameter ranges (Fig. 1), a personalized UFR profile was designed to minimize max UFR level, maintain HCT below a critical HCT profile, and achieve same UF goal.
Results
The estimated model parameters in five segments of an HD treatment are consistent with expected physiological changes during HD (Fig. 1 left/bottom). These parameter ranges were used to design personalized UFR profile (right/top). Simulations of the fluid volume model with the designed UFR profile over the estimated range of model parameters confirmed expected outcomes: similar UF goal achieved with 17% lower max UFR and lower HCT increases (right/bottom).
Conclusion
The successful estimation of fluid volume model parameters during HD supports the design of personalized UFR profiles that could lead to reduction in the incidence of IDH events.
Fig. 1: Estimated model parameters
Fig. 2: Actual vs Designed UFR HCT dynamics
Funding
- NIDDK Support