Sabrina Casper

Senior Computational Scientist

Sabrina Casper

Sabrina Casper develops innovative research software and advanced optimization and optimal control algorithms to improve and personalize dialysis therapy. She specializes in translating computational methods into practical, patient-centered solutions, with work spanning from algorithm design and implementation to in silico testing, laboratory validation, and clinical pilot studies. Much of her work has focused on hemodialysis, including personalized dosing of erythropoiesis-stimulating agents (ESAs) for anemia treatment and feedback control of ultrafiltration. The developed algorithms have been successfully tested in clinical pilot studies in the United States.

Sabrina earned a Master’s degree in Mathematics from the University of Konstanz, Germany, in 2014, with a specialization in optimization and optimal control. Before joining Fresenius Medical Care in 2017, she worked as a research associate at the University of Konstanz and as an independent consultant, already collaborating closely with the Renal Research Institute in New York.

Recent Articles by Sabrina Casper

  • Journal of clinical medicine
    September 12, 2026
    Toward Precision Fluid Management in Hemodialysis: Adaptive Ultrafiltration Guided by Continuous Relative Blood Volume Monitoring
    Doris H Fuertinger, Felix J Meigel, Aiyuan Wang, Sabrina Casper, Sheng-Han Yueh, Kevin Ho
    Background: Ultrafiltration (UF) during hemodialysis is typically prescribed as a fixed treatment goal and delivered using a largely constant ultrafiltration rate throughout the session. We evaluated whether Adaptive Ultrafiltration (Adaptive UF), a physiological closed-loop control strategy guided by continuous relative blood volume (RBV) monitoring, could improve RBV target attainment while individualizing fluid removal within predefined treatment constraints. Methods: We performed a paired patient-specific in silico study using 119,472 historical hemodialysis treatments from 11,246 U.S. patients. Treatment-specific plasma refill rate profiles were derived from recorded RBV and ultrafiltration data. For each historical treatment, a corresponding Adaptive UF treatment was simulated and compared with the corresponding originally delivered treatment. Outcomes included the percentage of time within a predefined favorable RBV target region, UF delivery, and operational performance. Results: Mean time within the RBV target region increased from 36.3% with historically delivered treatments to 56.4% with Adaptive UF, showing an absolute improvement of 20.2 percentage points (95% CI, 19.9-20.5); target attainment improved in 90.0% of patients. Mean area outside the target range decreased from 280.7 to 139.5 RBV %-minutes. Mean achieved UF volume increased by 296.4 mL, although treatment-level UF delivery was bidirectional, increasing in 83.1% and decreasing in 16.9% of treatments. UF rates remained below 13 mL/kg/h in all simulations, and 99.4% of treatments remained within predefined UF volume limits. Conclusions: Adaptive UF improved intradialytic RBV target attainment and individualized UF delivery within predefined treatment constraints. Prospective studies are required to determine whether these physiological improvements translate into clinical benefit.

What I value most about working at RRI is the opportunity to translate mathematical and computational research into practical solutions with real clinical impact.

Sabrina Casper
Senior Computational Scientist