A new Bayesian method to forecast and fine tune individual hemodialysis dose

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The most commonly used formulas for hemodialysis dose are based on single-pool urea kinetics; i.e., they consider the body as a single compartment and use an ad hoc adjustment for postdialysis urea rebound. We present a new urea kinetic modeling approach, individualized Bayesian urea kinetic modeling (IBKM), which incorporates prior knowledge. This method uses measurements made during previous treatments to forecast a patient's postdialysis urea rebound and clearance and provides a choice of possible dialysis parameters to achieve a desired clearance goal.


We used data from 18 patients (a total of 38 hemodialysis sessions) to build the model. All patients had been on thrice-weekly hemodialysis for at least 1 year before enrollment, and their dialysis prescription remained unchanged during the study period. Recorded variables included blood urea nitrogen (BUN) measurements and dialysis prescription parameters (dialyzer size, KoA, treatment time, blood and dialysis flow). The population distribution of urea kinetic parameters—derived from the 18 patients' data—and individual urea kinetic data (i.e., pre- and postdialysis BUN) are used in the IBKM method to make individual predictions.


Estimates (mean±SE) of population urea kinetic parameters are generation rate 0.17±0.01 mmol/min, clearance between extracellular and intracellular compartments 646±60 mL/min, and total volume of distribution 31.5±1.5 L, of which the extracellular volume is 36±4%. The effective dialysis clearance is estimated to be 9.0±1.7%, less than the expected dialyzer clearance. IBKM predictions of postdialysis equilibrated BUN concentrations are accurate: a root mean squared error of 3.4% of the “postrebound” BUN concentration at 30 min, a value in the range of urea measurement error itself.


IBKM can estimate not only the urea kinetics of an actual hemodialysis, but it can also predict a patient's target hemodialysis dose for any desired, flexible hemodialysis treatment. The method should prove useful for bedside monitoring, forecasting, and fine tuning of hemodialysis dose.

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