Nonlinear Parameter and State Estimation Approach in End-stage Kidney Disease Patients

Background There are nearly 786,000 Americans with end-stage Kidney disease (ESKD), of which 71% rely on life-saving hemodialysis (HD) treatment. HD care costs Medicare annually in excess of $70 billion. Objective One primary goal of HD treatment is to remove excess fluid via ultrafiltration. Intradialytic hypotension (IDH), a significant reduction in blood volume due to an imbalance between ultrafiltration and interstitial to intravascular fluid refilling rates, has severe implications for morbidity and mortality. However, fluid and blood volume are not accessible. It has been suggested that knowledge of extracellular fluid and absolute blood volume should improve HD outcomes. Methods The new estimation approach combines a validated physiological-based fluid volume model, the nonlinear least squares method, an uncertainty quantification technique, and the unscented Kalman filter. Results We develop a new estimation approach that estimates the underlying model parameters, bounds error estimation due to model uncertainty and measurement noise, reconstructs unmeasured fluid volume states, and predicts personalized outcomes (hematocrit). We test the performance of our approach using HD data. Conclusion The extracellular fluid volume (plasma, interstitial), absolute blood volume, and shift/refill fluid parameters can be estimated and bounded by the Fisher information matrix lower bounds. Furthermore, during ultrafiltration process, personalized predictions for the absolute blood volume evolution can be achieved by fitting the underlying parameters to a patient’s data. This estimation method, readily available for integration in current HD machines, could improve HD treatment outcomes.