Urea kinetic modeling: an in vitro and in vivo comparative study

A unidimensional diffusion model applied to uremic toxin kinetics in haemodiafiltration treatments

Kinetic modelling in haemodialysis is usually based upon the resolution of volume-defined compartment models. The interaction among these compartments is described by purely diffusive processes. In this paper we present an alternative kinetic model for uremic toxins in post-dilutional haemodiafiltration treatments by means of a unidimensional diffusion equation. A wide range of solutes such as urea, creatinine, $\beta _{2}$-microglobulin, myoglobin and prolactin were studied by imposing appropriate boundary and initial conditions in a virtual [0,1] domain. The diffusivity along the domain and the extraction rate at the dialyser are the kinetic parameters which were fitted by least-squares for every studied solute. The accuracy of the presented volumeless model as well as the behavior of the proposed kinetic parameters could be an alternative to the compartment description for a variety of molecular weight uremic toxins undergoing different treatment configurations.

Correlation between Dt/V derived from ionic dialysance and blood-driven Kt/V of urea in African-American hemodialysis patients, based on body weight and ultrafiltration volume

Background

The Dt/V obtained by using ionic dialysance (D) as a surrogate for urea clearance (K) is a well-validated adjunct measure of hemodialysis adequacy, with a variable level of correlation with urea-based Kt/V. However, this correlation has not been examined based on patients’ body size and ultrafiltration (UF) volume during the dialysis session.

Methods

Simultaneous evaluations of online Dt/V and single-pool variable-volume urea Kt/V were made. Patients were categorized into three subgroups based on their weight (<60, 60–80 and ≥80 kg), body mass index (<25, 25–30 and >30 kg/m²) and UF volume (<1.5, 1.5–3 and >3 L). The correlation between Dt/V and Kt/V was evaluated for the entire cohort per dialysis session in each subgroup.

Results

Mean Kt/V was greater than the mean Dt/V (1.72 versus 1.50, P < 0.001), with an overall correlation r value of 0.602. This correlation was stronger in the medium weight group versus lower and higher weights. The correlation between Dt/V and Kt/V was inversely related to the UF volume (r = 0.698, 0.621 and 0.558 for those with UF volume of <1.5, 1.5–3.0 and >3 L, respectively). A total of 99.3% of patients with Dt/V of >1.2 also had Kt/V >1.2 and 9.5% of those with Dt/V <1.2 had their Kt/V <1.2.

Conclusions

There is a moderate degree of correlation between Dt/V and Kt/V in African-American hemodialysis patients, which is impacted by body size and UF volume. A Dt/V of >1.2 strongly predicts adequate dialysis as defined by Kt/V of >1.2.

On-Line Hemodiafiltration with Pre- and Postdilution: A Comparison of Efficacy

Since the introduction of on-line substituate preparation, high substituate rates (Qs) in pre- and postdilution for hemodiafiltration (HDF) procedures can be realized. During postdilution HDF (POD-HDF) and additional convective removal is possible, but in vivo Qs is limited to approx. 1/3Qb (bloodflow). With predilution HDF (PRD-HDF) higher Qs and therefore high convective transport rates by ultrafiltration can be reached. On the other hand the blood concentration is diminished by predilution. Further decrease of the diffusive transport is caused by reduced dialysate flow Qd due to separation of the substituate from the dialysate (Fresenius 4008 On-Line HDF, Gambro AK100 Ultra). The theoretical description of the combined diffusive-convective transport is limited to 1-dimensional models and small UF-rates. Therefore for practical and theoretical purposes the assessment of the efficacy of on-line PRD-HDF and POD-HDF in different molecular weight ranges is desirable. By means of in vitro experiments the effective clearances Keff of hemodialysis (HD, dialyzer: Fresenius F60) for urea, creatinine, vitamin B12 and inulin were compared with measured and theoretical Keff of POD- and PRD-HDF. The theoretical expectation is confirmed that Keff for small molecular weight substances decreases slightly with PRD-HDF and increases for larger molecules. In the case of POD-HDF Keff for small molecular weight substances increases slightly and strongly for larger molecules. In vivo experiments were performed to measure the real substance removal from patient's blood and to figure out the impact of dialysate flow (collection of the used dialysate during the 1. treatment hour and concentration measurements for urea, creatinine, phosphate, ß2-MG). The results show that the substraction of Qs from Qd reduces Keff for urea, creatinine and phosphate but not for ß2-MG. PRD-HDF with Qd = 500 ml/min is significantly less effective for small molecules than HD. There is no significant difference of Keff for urea, creatinine, phosphate during HD and PRD-HDF with Qd = 800 ml/min, but a significant increase of 10-15% for POD-HDF Keff for ß2-MG increases by 75% for PRD-HDF and 95% for POD-HDF compared with HD (Qd = 500 ml/min).

Nanoengineered optical urea biosensor for estimating hemodialysis parameters in spent dialysate

An optical biosensing scheme based on urease encapsulated calcium alginate microspheres which are coated with polyelectrolyte nanofilms predominantly composed of cresol red (CR) dye is demonstrated in this paper. The dye molecules within the nanofilms are deposited via the layer-by-layer (LbL) self-assembly technique on the microspheres and used as the optical transducer. A flow through cell constructed using a cuvette attached to a fiber optic spectrometer was used to determine the response of the biosensor to standard urea solutions of different concentrations. The change in pH and the absorbance ratio was monitored with time and these results were used for measurements of urea concentrations in the spent dialysate fluid. The biological parameters controlling hemodialysis such as dialyzer clearance or Kt/V and percent removed urea (PRU) have also been reported. The results demonstrate that the urea biosensor is pH reversible with a sensitivity of 0.09 pH units/min and is able to detect a change of 0.005 ratio units in urea concentration ranging 0.1-60 mg dL(-1). The response time of the sensor was calculated as 8 min while the detection range of urea covered the levels that are present in the spent dialysate fluid. The results obtained in the analysis of biological samples were in good agreement with those obtained by a reference method, showing no significant differences at a confidence level of 95%.

Artificial intelligence: a new approach for prescription and monitoring of hemodialysis therapy

The effect of dialysis on patients is conventionally predicted using a formal mathematical model. This approach requires many assumptions of the processes involved, and validation of these may be difficult. The validity of dialysis urea modeling using a formal mathematical model has been challenged. Artificial intelligence using neural networks (NNs) has been used to solve complex problems without needing a mathematical model or an understanding of the mechanisms involved. In this study, we applied an NN model to study and predict concentrations of urea during a hemodialysis session. We measured blood concentrations of urea, patient weight, and total urea removal by direct dialysate quantification (DDQ) at 30-minute intervals during the session (in 15 chronic hemodialysis patients). The NN model was trained to recognize the evolution of measured urea concentrations and was subsequently able to predict hemodialysis session time needed to reach a target solute removal index (SRI) in patients not previously studied by the NN model (in another 15 chronic hemodialysis patients). Comparing results of the NN model with the DDQ model, the prediction error was 10.9%, with a not significant difference between predicted total urea nitrogen (UN) removal and measured UN removal by DDQ. NN model predictions of time showed a not significant difference with actual intervals needed to reach the same SRI level at the same patient conditions, except for the prediction of SRI at the first 30-minute interval, which showed a significant difference (P = 0.001). This indicates the sensitivity of the NN model to what is called patient clearance time; the prediction error was 8.3%. From our results, we conclude that artificial intelligence applications in urea kinetics can give an idea of intradialysis profiling according to individual clinical needs. In theory, this approach can be extended easily to other solutes, making the NN model a step forward to achieving artificial-intelligent dialysis control.

Measurement of the delivery of dialysis in acute renal failure

BACKGROUND

Recent studies in patients with acute renal failure (ARF) have shown a relationship between the delivered dose of dialysis and patient survival. However, there is currently no consensus on the appropriate method to measure the dose of dialysis in ARF patients. In this study, the dose of dialysis was measured by blood- and dialysate-based kinetic methods in a group of ARF patients who required intermittent hemodialysis.

METHODS

Treatments were performed using a Fresenius 2008E volumetric hemodialysis machine with the ability to fractionally collect the spent dialysate. Single-, double-pool, and equilibrated Kt/V were determined from the pre-, immediate post-, and 30-minute post-blood urea nitrogen (BUN) measurements. The solute reduction index was determined from the collected dialysate, as well as the single- and double-pool Kt/V.

RESULTS

Forty-six treatments in 28 consecutive patients were analyzed. The mean prescribed Kt/V (1.11 +/- 0.32) was significantly greater than the delivered dose estimated by single-pool (0.96 +/- 0.33), equilibrated (0.84 +/- 0.28), and double-pool (0.84 +/- 0.30) Kt/V (compared with prescribed, each P < 0.001). There was no statistical difference between the equilibrated and double-pool Kt/V (P = NS). The solute removal index, as determined from the dialysate, corresponded to a Kt/V of 0.56 +/- 0.27 and was significantly lower than the single-pool and double-pool Kt/V (each P < 0.001).

CONCLUSION

Blood-based kinetics used to estimate the dose of dialysis in ARF patients on intermittent hemodialysis provide internally consistent results. However, when compared with dialysate-side kinetics, blood-based kinetics substantially overestimated the amount of solute (urea) removal.

Quasi-steadiness approximation for the two-compartment solute kinetic model

We analytically solved the equation of the variable volume, two-compartment solute kinetic model (TCSKM). From the solution, we constructed an expression of weekly concentration profiles developing in the patient's body by routine hemodialyses. Obtained formulas can be used to calculate Kt/V, solute reduction index (SRI), the solute generation rate (G) per unit distribution volume (V), and a mass transfer coefficient (MTC) between the two compartments. To estimate these parameters, the formulas only need three-point data during a dialysis, that is, pre-, one-hour, and post-dialysis solute concentrations instead of four that would otherwise be needed. A 48 hour data point is not required. The weekly concentration profiles can be easily calculated by the formulas. As examples of clinical applications, we calculated Kt/V, G/V, and SRI of urea, Cr, and uric acid using plasma data of 121 hemodialyzed patients. Then the results were compared with the single-compartment solute kinetic model (SCSKM). The obtained mean MTC/V values, that is, 1.08 (1/hr) for urea, 0.53 (1/hr) for Cr, and 1.11 (1/hr) for uric acid, were consistent with the previous works. SCSKM overestimated the mean G/V by 7.1%, 15.9%, and 10.0%, and the mean SRI by 6.7%, 18.6%, and 10.0%, for urea, Cr, and uric acid, respectively. The solute distribution volume ratio of TCSKM to SCSKM, (V)TCSKM/(V)SCSKM, depended on the value of MTC/V and the hemodialysis duration. Using pedometers, we measured the total number of steps the patients took during a week. We found that the total number of steps in a week was significantly correlated with the Cr generation rate (r = 0.285, P < 0.03), but that it was not significantly correlated with the other generation rates (r = 0.204, P > 0.09 for urea, and r = 0.209, P > 0.08 for uric acid). These data suggest that the Cr generation rate is related to the patient's physical activity. We conclude that the formulas can estimate an adequate dialysis prescription for the hemodialyzed patient.

Use of the medical differential diagnosis to achieve optimal end-stage renal disease outcomes

Compared with the general population, end-stage renal disease (ESRD) patients continue to have a higher than expected morbidity and mortality. Hypoalbuminemia, anemia, hypertension, and inadequate dialysis are all thought to contribute to the high morbidity and mortality among ESRD patients. Anemia algorithms should help to standardize the approach to anemia and the use of recombinant human erythropoietin (rHuEPO), but clinicians still must review each patient individually, searching for and treating the multitude of interrelated factors that affect rHuEPO responsiveness. Hypoalbuminemia is a very strong predictor of increased morbidity and mortality in dialysis and nondialysis patients. The causes of hypoalbuminemia are multifactorial, and diagnosis of the cause of hypoalbuminemia is usually elusive. The basis of the poorer survival in US dialysis patients remains controversial, but inadequate dialysis has been implicated. To assure adequate dialysis, the dialysis prescription must be individualized for each patient, and delivered dialysis must be routinely monitored. Hypertension is associated with left ventricular hypertrophy, which is also an important determinant of survival in ESRD patients. Hypertension should be treated in ESRD patients in conjunction with other interventions that are known to reverse left ventricular hypertrophy. Special efforts must be made in the medical management of hypoalbuminemia, anemia, hypertension, and dialysis treatment adequacy to improve survival in patients with ESRD.

Fractional direct dialysis quantification: a new approach for prescription and monitoring hemodialysis therapy

We describe a new methodology, fractional direct dialysis quantification (FDDQ) utilizing the Fresinius Dialysate Sampling Module (DSM), for quantitating total solute removal during hemodialysis (HD). Our data demonstrate that this technique and Direct Dialysis Quantification (DDQ) yield virtually identical results. FDDQ, however, obviates the practical obstacles that have limited the applicability of DDQ. We discuss the theoretical and practical advantages of this methodology, as compared to urea kinetic modeling (UKM) with Kt/V, for prescribing and monitoring dialysis therapy. FDDQ provides reliable and accurate quantitative data of dialysis function and protein catabolic rate (PCR) independent of questionable theoretical assumptions and parameters required for UKM with Kt/V. It is simple to comprehend and apply. It permits easy comparison of standard and rapid high efficiency dialyses. It also facilitates the quantitative comparison of HD and continuous therapies (peritoneal dialysis and various types of continuous hemofiltration). FDDQ permits the use of other solutes, in place of or in addition to urea, for the quantitation of HD. Because of its simplicity and probable low cost, it can be used with each HD session. It will thus provide accurate data on delivered versus prescribed therapy. These features should permit more accurate monitoring and lead to a clearer understanding of the relationship of outcomes versus delivered dialysis dose, and consequently more effective adjustment of dialysis therapy.