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Perit Dial Int 29(1): 72-80
2009
© 2009 International Society for Peritoneal Dialysis
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Clinical

KINETIC ANALYSIS OF PERITONEAL FLUID AND SOLUTE TRANSPORT WITH COMBINATION OF GLUCOSE AND ICODEXTRIN AS OSMOTIC AGENTS

Magda Galach1, Andrzej Werynski1, Jacek Waniewski1, Philippe Freida2 and Bengt Lindholm3

Institute of Biocybernetics and Biomedical Engineering,1 Polish Academy of Sciences, Warsaw, Poland; Department of Nephrology,2 Louis Pasteur District Hospital, Cherbourg, France; Divisions of Baxter Novum and Renal Medicine,3 Department of Clinical Science, Intervention and Technology, Karolinska University Hospital Huddinge, Karolinska Institutet, Stockholm, Sweden

Correspondence to: M. Galach, Institute of Biocybernetics and Biomedical Engineering, Polish Academy of Sciences, 4 Trojdena Str, 02-109 Warsaw, Poland. magda{at}ibib.waw.pl


   ABSTRACT
TOP
 ABSTRACT
MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 APPENDIX
 REFERENCES
 

{diamondsuit} Background: Controlling extracellular volume and plasma sodium concentration are two crucial objectives of dialysis therapy, as inadequate sodium and fluid removal by dialysis may result in extracellular volume overload, hypertension, and increased cardiovascular morbidity and mortality in end-stage renal disease patients. A new concept to enhance sodium and fluid removal during peritoneal dialysis (PD) is the use of dialysis solutions with two different osmotic agents.

{diamondsuit} Aim: To investigate and compare, with the help of mathematical modeling and computer simulations, fluid and solute transport during PD with conventional dialysis fluids (3.86% glucose and 7.5% icodextrin; both with standard sodium concentration) and a new combination fluid with both icodextrin and glucose (CIG; 2.6% glucose/6.8% icodextrin; low sodium concentration). In particular, this paper is devoted to improving mathematical modeling based on critical appraisal of the ability of the original three-pore model to reproduce clinical data and check its validity across different types of osmotic agents.

{diamondsuit} Methods: Theoretical investigations of possible causes of the improved fluid and sodium removal during PD with the combination solution (CIG) were carried out using the three-pore model. The results of computer simulations were compared with clinical data from dwell studies in 7 PD patients. To fit the model to the low net ultrafiltration (366 ± 234 mL) obtained after a 4-hour dwell with 3.86% glucose, some of the original parameters proposed in the three-pore model (Rippe & Levin. Kidney Int 2000; 57:2546–56) had to be modified. In particular, the aquaporin-mediated fractional contribution to hydraulic permeability was decreased by 25% and small pore radius increased by 18%.

{diamondsuit} Results: The simulations described well clinical data that showed a dramatic increase in ultrafiltration and sodium removal with the CIG fluid in comparison with the two other dialysis fluids. However, to adapt the three-pore model to the selected group of PD patients (fast transporters with small ultrafiltration capacity on average), the peritoneal pore structure had to be modified. As the mathematical model was capable of reproducing the clinical data, this shows that the enhanced ultrafiltration with the combination fluid is caused by the additive effect of the two different osmotic agents and not by a specific impact of the new dialysis fluid on the transport characteristics of the peritoneum.

KEY WORDS: Ultrafiltration; fluid status; sodium; mathematical modeling; combination dialysis solution.

Peritoneal dialysis (PD), which is used by 5% – 80% of the dialysis population in different countries (1), is a method providing smooth and continuous disposal of excess of water and small molecular waste products of metabolism without the problems of circulatory instability associated with intermittent hemodialysis. However, PD is less efficient per time unit than hemodialysis with respect to the rate of removal of fluid and low molecular weight solutes such as urea and sodium. This explains in part why fluid overload and inadequate sodium balance are frequent problems in PD patients (2,3). Thus, a main objective of PD research should be to find novel PD solutions that would allow the removal of more fluid and sodium without loosing the smooth characteristic of PD therapy.

Peritoneal dialysis fluids nowadays usually contain an osmotic agent, either glucose (crystalloid), which is suitable for the shorter dwells, or icodextrin (colloid), which is increasingly used for the long dwell. Whereas glucose is rapidly absorbed from the dialysate, resulting in fast disappearance of the osmotic gradient during the dwell time and therefore rapidly declining ultrafiltration (UF) rate, icodextrin, due to its larger size, is less rapidly absorbed and therefore leads to enhanced solute clearances and fluid removal (4). Nevertheless, fluid overload and hypertension are still commonly seen even in patients using icodextrin. Recently, various mixtures of glucose and icodextrin have been tested (5–8). These new dialysis fluids use both types of osmotic agents (colloid and crystalloid) to improve fluid removal and solute clearances, and also may have low dialysate sodium concentrations to enhance further sodium removal (3,7–10).

The main aim of the present study was an extended evaluation and theoretical validation of the effects of a combination fluid using both icodextrin and glucose in a modified version of the three-pore model (11–13) in simulations of the kinetics of fluid and solute transport. In particular, this article is focused on investigating whether the marked improvement in solute and fluid transport obtained during long (15 hours) PD dwells using a combination of both icodextrin and glucose (CIG; final mix formulation: 2.6% glucose/6.8% icodextrin, sodium concentration 121 mmol/L; Table 1) compared with conventional dialysis fluids (3.86% glucose and 7.5% icodextrin, both with standard sodium concentration; Table 1) could be confirmed theoretically by matching results obtained from computer simulations with clinical data. In addition, a new hypothesis on peritoneal pore structure was formulated for a group of patients that exhibited, on average, a fast transport pattern and low UF capacity.


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  		TABLE 1 Composition of the Peritoneal Dialysis Fluids Used in Simulations: 3.86% Glucose (G 3.86%), 7.5% Icodextrin (I 7.5%), and Combination Solution with 6.8% Icodextrin and 2.6% Glucose (CIG)

 


   MATERIALS AND METHODS
TOP
 ABSTRACT
 MATERIALS AND METHODS
RESULTS
 DISCUSSION
 APPENDIX
 REFERENCES
 
Using the three-pore model (12), simulations of PD with three different dialysis fluids (3.86% glucose, or 7.5% icodextrin, or a combination of 2.6% glucose and 6.8% icodextrin) were carried out. Details of the fluids used are described in Ref. (7). Results were compared with clinical data already published (7). The simulation methods used and the applied description of icodextrin distribution versus molecular weight of glucose polymers are described in the Appendix.

CLINICAL DATA
The clinical study was carried out at Louis Pasteur District Hospital, Cherbourg, France, in 7 stable patients (5 females/2 males). The patients were, on average, fast transporters and as a result they exhibited a reduced UF capacity, with mean (±SD) net UF of 366 ± 234 mL obtained after a 4-hour dwell with 3.86% of glucose. A detailed description of the clinical protocol and data has been provided in our previous paper (7). Written informed consent consistent with the Declaration of Helsinki was obtained from each patient and the study was approved by the Ethics Committee of the University of Caen, France.

The 7 patients were assigned to three 15-hour PD sessions using, alternatively, 2 L of hypertonic glucose-based solution (3.86% glucose; 3.86% Dianeal; Baxter Healthcare, Castlebar, Ireland), or icodextrin solution (7.5% icodextrin; 7.5% Extraneal; Baxter Healthcare), or a combination fluid containing a mixture of glucose and icodextrin (CIG) in a single mixed PD fluid (Table 1). The dialysis fluids were administered in random order. To assess the UF volume over time profile, instantaneous clearances, and total mass transfer of solutes for each of the three dialysis fluid formulations, repeated complete drainage of dialysate followed by reinfusion of the effluent was performed during the dwell period at 0, 2, 4, 8, 12, and 15 hours. The effective dwell time was slightly (by 20 minutes or less) shortened due to the time required for drainage and reinfusion. This time was not taken into account in the model. The drained fluid was weighed and dialysate samples were taken before reinfusion. Blood samples were taken at time 0, at 4 hours, and at the end of the dwell.

As data on sodium dialysate concentration at 15 hours were missing for 1 patient, sodium removal (Narem) was calculated according to the following equation:

Formula 1(1)
where MNa(0) is the infused mass of the sodium in dialysate, Formula 1Na,end is mean dialysate sodium concentration at 15 hours for 6 patients, and Formula 1D,end is mean dialysate volume at 15 hours for 7 patients.

SIMULATIONS
The description of the simulations and the abbreviations used below are provided in the Appendix.

Averaged data from the clinical investigations of the 7 PD patients were used as target values for fitting the model (7). Simulations were done in three steps with the aim to optimize the model for the description of peritoneal transport with the 3.86% glucose and 7.5% icodextrin solutions, respectively, before applying the obtained model parameters for the simulation of the CIG solution.

First, simulation of PD with the 3.86% glucose solution was performed. During this first simulation, specific parameters describing fluid, glucose, and sodium transport, that is, the membrane UF coefficient (LpS), solute permeability surface area for small pores (PSsmall,G, PSsmall,Na, PSsmall,U), and peritoneal absorption rate (QA) were adjusted to fit the model to the clinical data. The values for solute permeability surface area for large pores (PSlarge,G, PSlarge,Na, PSlarge,U) also needed to be changed in proportion to the adjusted values for PSsmall,G, PSsmall,Na, and PSsmall,U, respectively. In addition, the assumed properties of the peritoneal membrane had to be changed with respect to both small-pore radius (rs) and pore fractions ({alpha}) to obtain the best fit of the model to the clinical data.

Next, the simulation of PD with 7.5% icodextrin was carried out. During this simulation, the values of parameters obtained during the first simulation were used but, in addition, diffusive transport parameters for icodextrin fractions (PSsmall,IF, PSlarge,IF) were adjusted, as described in the Appendix.

Finally, simulations for the combination fluid were performed using all parameters obtained during the prior two simulations for 3.86% glucose and 7.5% icodextrin solutions, without any further changes to these parameters. For more details, see the Appendix.

STATISTICAL METHODS
To calculate differences between clinical data and simulation results, the average deviations (AD) and percentage errors were used. Average deviation was evaluated according to the following formula (14):

Formula 1(2)
where n is the number of sampling points and xsim and xexp are simulated and experimental (clinical) values, respectively, of the examined variable x. Percentage error was calculated as

Formula 1(3)
where Formula 1exp is the mean value of xexp.


   RESULTS
TOP
 ABSTRACT
 MATERIALS AND METHODS
 RESULTS
DISCUSSION
 APPENDIX
 REFERENCES
 
A detailed report on the clinical data used in the current study was presented in our previous paper (7). Here we describe the results of the computer simulations, which, following certain modifications of parameters in the three-pore model (as described in Table 2), showed, in general, good agreement with the clinical data.


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  		TABLE 2 Basal and Fitted Parameter Values Used in Simulations

 

The results of the simulations demonstrated that a modified (parameters adjusted according to Table 2) three-pore model was able to reproduce very well the clinical data concerning dialysate volume (Figure 1). It should be noted that, in order to provide the best possible fit of the simulated values to the clinical data, it was necessary to change the values of several of the parameters used in the model. The necessary changes were generally less than 50% of the original basal values (Table 2), except for the peritoneal absorption rate, QA, for which the modified value was about 100% higher than that assumed in the basal set of parameter values (Table 2).


Figure 1
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  		Figure 1 — Ultrafiltration volumes during peritoneal dialysis with 3.86% glucose, 7.5% icodextrin, and combination fluid (6.8% icodextrin/2.6% glucose) are compared between simulation results (lines) and clinical data (marks).

 
During the work to develop and refine the modified three pore model used for the simulations, it turned out that, to obtain a good adjustment of simulated dialysate volumes as well as dialysate solute concentrations for patients with low UF [average net UF obtained after 4-hour dwell with 3.86% of glucose was <400 mL, suggesting UF failure in some patients (15)], the assumptions regarding the peritoneal pore system had to be changed. The new set of assumptions, including that of a higher small-pore radius and a lower ultrasmall-pore fraction compared with values usually assumed in the three-pore model (Table 2), resulted in a better fit of the model to the clinical data.

Ultrafiltration simulated for the combination fluid was substantially increased compared to the two other dialysis fluids. According to the simulations, net UF volume at 15 hours increased from –99 mL with 3.86% glucose to 468 mL with 7.5% icodextrin and to 915 mL with the CIG solution, whereas the clinical data showed that mean net UF was –85 mL, 462 mL, and 990 mL, respectively (Table 3). Average deviations for dialysis volumes were less than 75 mL and PE was less than 3%.


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  		TABLE 3 Net Fluid and Sodium Removal and Urea Clearance at the End of Dialysis (15 Hours), Average Deviations (AD), and Percentage Errors (PE) with 3.86% Glucose (G 3.86%), 7.5% Icodextrin (I 7.5%), and Combination Solution with 6.8% Icodextrin and 2.6% Glucose (CIG)

 

The clinical data as well as the simulations showed that there were no significant differences between dialysate urea concentrations with the different osmotic agents (p > 0.2). The AD of simulated dialysate urea concentration versus clinical data was less than 5% (Table 3). However, when switching from 3.86% glucose-based to 7.5% icodextrin-based solution, the increase in net UF volume resulted in an increase in urea clearance (30% increase according to simulation vs 33% increase according to clinical data), and urea clearance also increased on switching from 7.5% icodextrin to the CIG solution (28% increase according to simulation vs 41% increase according to clinical data; Table 3). There was good agreement between clinical data and simulated values for dialysate urea concentrations (Figure 2).


Figure 2
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  		Figure 2 — Dialysate urea concentrations during peritoneal dialysis with 3.86% glucose, 7.5% icodextrin, and combination fluid (6.8% icodextrin/2.6% glucose) are compared between simulation results (lines) and clinical data (x's = dialysate concentration; o's = blood concentration).

 

Whereas there was good agreement between clinical data and simulated values of dialysate sodium concentration when using the 3.86% glucose and CIG fluids (Figure 3), the model was not able to describe correctly the clinical data demonstrating a slightly decreasing sodium concentration in the dialysate when using 7.5% icodextrin. Only if the sodium flow rate between dialysate and blood was assumed to be reduced in the simulations, and the sodium transport parameters were fitted to icodextrin data (thus in the second, not the first step), was it possible to obtain good agreement between clinical data and simulation results for the 7.5% icodextrin fluid (data not shown). But in this hypothetical case, the simulated dialysate sodium concentrations did not fit well to the clinical data for 3.86% glucose and the CIG fluids. However, the percentage difference between the clinical data and simulated concentrations was less than 5% (Table 3). Simulation results as well as clinical data showed also that net sodium removal increased by replacing 3.86% glucose with 7.5% icodextrin and then with the CIG solution (Table 3).


Figure 3
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  		Figure 3 — Dialysate sodium concentrations during peritoneal dialysis with 3.86% glucose, 7.5% icodextrin, and combination fluid (6.8% icodextrin/2.6% glucose) are compared between simulation results (lines) and clinical data (x's = dialysate concentration; o's = blood concentration).

 

The simulated dialysate glucose concentrations for dwells with glucose-containing solutions (both 3.86% glucose and CIG solutions) showed good conformity with clinical data (Figure 4), although the simulated dialysate glucose concentration for the CIG solution was slightly (<10%) underestimated. The simulated dialysate glucose concentration for the icodextrin solution was also underestimated but, in this case, the discrepancies were substantial (AD = 2.8 mmol/L, PE = 38%).


Figure 4
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  		Figure 4 — Dialysate glucose concentrations during peritoneal dialysis with 3.86% glucose, 7.5% icodextrin, and combination fluid (6.8% icodextrin/2.6% glucose) are compared between simulation results (lines) and clinical data (x's = dialysate concentration).

 

As the investigated patients had, on average, apparent loss of UF capacity, we analyzed to what extent the results of the simulations, and the need to adjust the parameters to obtain a good fit with the clinical data, differed between these patients and a standard patient. The clinical data with respect to net UF volumes over time (Figure 5), final net UF volumes, net sodium removal, and urea clearances during the dwells (Table 4) for patients with loss of UF capacity used in this report were different compared with simulation results done with parameters representing a standard patient (12). As could be expected, there were especially large differences in net UF volumes and net sodium removal, but also with urea clearances, especially for dwells with icodextrin, between the standard patient and the patient group analyzed here.


Figure 5
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  		Figure 5 — Ultrafiltration volumes during peritoneal dialysis with 3.86% glucose (G 3.86%), 7.5% icodextrin (I 7.5%), and combination fluid (2.6% glucose/6.8% icodextrin); comparison between simulation results for a standard patient (lines) and the mean clinical data for the investigated patients (x's = G 3.86%; {Delta}'s = I 7.5%; *'s = combination fluid).

 

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  		TABLE 4 Net Fluid and Sodium Removal and Urea Clearance at the End of Dialysis (15 Hours), Average Deviations (AD), and Percentage Errors (PE) of Dialysate Volume and Solute Concentrations with 3.86% Glucose (G 3.86%), 7.5% Icodextrin (I 7.5%), and Combination Solution with 6.8% Icodextrin and 2.6% Glucose (CIG) for Patients with Ultrafiltration Failure (UFF; Clinical Data; mean±SD) and a Standard Patient (Simulation Result)

 


   DISCUSSION
TOP
 ABSTRACT
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
APPENDIX
 REFERENCES
 
Both simulations and clinical data demonstrated that the CIG solution, in comparison with the other dialysis fluids, significantly increased UF and sodium removal. It turned out that the mathematical model was capable of reproducing clinical data concerning PD with CIG solution, indicating that the enhanced UF could be explained by the composition of the combination dialysis fluid and not by any specific impact of the new combination of the two osmotic agents acting together on the peritoneum.

The most probable explanation for the increased UF with the CIG solution is as follows: Use of glucose as an osmotic agent results in a rapid initial rise in peritoneal volume, whereas concurrent use of icodextrin as a second osmotic agent compensates for the disappearance of the glucose osmotic gradient during the latter part of the dwell. Therefore, the peritoneal dialysate volume rises during the first half of the dwell and then changes only slightly during the second half (Figure 1).

There are no clear explanations for the observed decreased dialysate concentrations of sodium or the higher than expected dialysate glucose concentrations during the dwells with icodextrin. A possible reason for the slightly lower than expected dialysate sodium concentration is that measurements of sodium were somehow influenced by the presence of icodextrin, perhaps as a consequence of the volume occupied by icodextrin as we did not express the values in relation to icodextrin-free dialysate water. An alternative explanation is that some sodium sieving did in fact occur with the icodextrin solution, perhaps as a consequence of generation of low molecular weight icodextrin metabolites in the dialysate and in adjacent tissues, resulting in some crystalloid osmosis (see below). Some sodium sieving during the early part of an exchange with icodextrin is to be expected due to the presence of low molecular weight "icodextrin" molecules present in the fresh icodextrin solution (12). However, it should be noted that the time course of dialysate sodium is not typical for what is usually seen for sodium sieving using a crystalloid osmotic agent, as the levels remained low throughout the dwell.

Possible reasons for the higher than expected measured dialysate glucose concentrations are not clear. Again, we speculate that this could be due to generation of glucose from hydrolysis of icodextrin in the dialysate or in the adjacent peritoneal tissue. An alternative explanation could be that glucose measurements were, in fact, interfered with by the presence of icodextrin or its metabolites. Glucose concentrations in blood and dialysate were measured in our study using the glucose oxidase method on a Hitachi 912 analyzer (Hitachi, Meylan, France). Some studies recommend that method, as icodextrin does not cause interference (16,17); however, other authors have demonstrated some interference by maltose (18,19). In the latter case, the presence of maltose in dialysate could result in false values for glucose concentration. As dialysis with the icodextrin solution resulted in measured values of glucose concentrations in dialysis fluid that exceeded the measured values in plasma, and whereas the simulated dialysate glucose concentration equilibrated to the measured glucose concentration in plasma, the simulated dialysate glucose concentration underestimates the clinical data.

These results underline the need in future studies for a careful evaluation of the methods used for measurements of sodium and glucose when using icodextrin solution.

Results of computer simulations (12) have been used in other studies with combination fluid (1.36% glucose, 7.5% icodextrin) (5,6); however, our study differs from the previous studies in several aspects: our study period was much longer (15 hours vs 7 – 10 hours), our patient group was more uniform (patients were fast transporters), and a novel approach was proposed for estimation of transport parameters pertaining to the glucose and icodextrin fractions using clinical data. However, in general, our findings confirm previously reported results.

The focus of the current study was a more complete evaluation and theoretical validation of the effects of the CIG solution using a modified version of the three-pore model in simulations of the kinetics of fluid and solute transport. In particular, this paper was devoted to means for improving mathematical modeling based on critical appraisal of the ability of the original three-pore model to reproduce clinical data. Thus, the appropriate values of the parameters were established, first for the 3.86% glucose solution and then for the icodextrin solution. These values (without further adjustments) were used to simulate fluid and solute transport for the CIG solution.

Based on a unique series of clinical studies in a selected group of PD patients, and using three different PD fluids, the current study provided novel information regarding several factors that could influence peritoneal transport kinetics. In particular, it should be underlined that an apparent decreased fraction of aquaporins and increased small-pore radius in these patients (fast transporters, decreased UF capacity on average) were observed compared to "average" patients described by the three-pore model (12). These two observations are of interest as they may lead to a better understanding of the mechanisms leading to UF failure.

There is controversy about how to handle the impact of direct lymphatic transport and absorption to the subperitoneal tissues on fluid and solute transport. In the simulations presented in this article, a higher value of fluid absorption rate than usually assumed in the three-pore model was used. In particular, the value 0.6 mL/minute is not consistent with the value given by Rippe and Levin (12), who interpreted this parameter as direct lymphatic absorption from the peritoneal cavity (L). However, this interpretation is in conflict with the data on absorption of labeled albumin and dextran from the peritoneal cavity (20). In addition, in the simulations performed by Rippe and Levin (12), absorption to subperitoneal tissues was accounted for by increasing the calculated theoretical PS for icodextrin fractions by 1.2 mL/minute. In our previous studies in which radio-iodinated albumin was used as a volume marker, total peritoneal absorption (i.e., the sum of absorption to subperitoneal tissues and direct lymphatic absorption) was estimated as the coefficient of volume marker elimination with an average value of 1.8 mL/minute (20,21). Furthermore, the peritoneal fluid absorption rate, QA = 0.6 mL/minute, is consistent with that used by Vonesh et al. in their model, PD Adequest v. 2.0 (Baxter Healthcare, Deerfield, IL, USA) (22,23). Therefore, in the present article, a modified version of the three-pore model wherein QA is equal to peritoneal fluid absorption from the peritoneal cavity was proposed. QA reflects, the total peritoneal absorption rate rather than only the direct lymphatic absorption rate, suggesting that, with this new approach, the usefulness of the three-pore model may be enhanced. This may have clinical implications as this model is increasingly used especially for modeling of peritoneal transport in patients on icodextrin-based dialysis fluid.

The estimated PS parameters for icodextrin fractions were higher than values calculated with the direct equation used in Ref. (12). This might result from both the higher unrestricted pore area (which is also confirmed by high permeability surface area for urea; Table 2) and hydrolysis of icodextrin by amylase (24).

In summary, we have shown that computer simulations based on a modified three-pore model accurately reflect clinical data concerning peritoneal solute and fluid transport with PD fluids containing glucose, icodextrin, and a combination of glucose and icodextrin. This approach provides theoretical validation of the clinical results and shows that the clinical results obtained were to be expected based on our current knowledge. In addition, the validity of the modified three-pore model across different types of osmotic agents was checked, and the clinical applicability of the three-pore model in modern PD treatment, which often includes icodextrin solutions, was demonstrated. Therefore, this approach can be used for predictions of transport kinetics for other combinations of glucose and icodextrin as osmotic agents.


   APPENDIX
TOP
 ABSTRACT
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 APPENDIX
REFERENCES
 
ABBREVIATIONS

SIMULATIONS
The three-pore model was solved using the ODE solver of Matlab v. R2007a software (MathWorks, Natick, MA, USA). Function ode45 is based on an explicit 4th- and 5th-order Runge–Kutta formula. It is a one-step solver, which means that, to compute a solution at time tn, it needs only the solution at the immediately preceding time point tn–1.

The parameter estimation was done using Matlab function fminsearch, which uses the Nelder–Mead type simplex search method for numerical minimization of objective function describing the differences between simulation results and clinical data. As the aim of the current study was to describe dialysate volume and dialysate sodium and glucose concentrations, this objective function took the following form:

Formula 1(A1)
where VD(Ti) is dialysate volume at time Ti, CNa,D(Ti) is dialysate sodium concentration at time Ti, CG,D(Ti) is dialysate glucose concentration at time Ti, "exp" stands for clinical data, and "sim" stands for simulation results.

Five icodextrin fractions [IF; as described in Ref. (25); see Table A1], sodium (Na), urea (U), glucose (G), and total protein (P) were taken into account in the model. The basal (initial) values of the parameters used in the simulations were taken from the literature (12) (see Table 2).


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  		TABLE A1 Description of Icodextrin Fractions in Fresh Dialysate Fluid According to Vonesh et al. (25)

 


   ACKNOWLEDGMENTS
 
This work was partially supported by Polish State Research Grant no. N518 041 31/3967.

Received 22 August 2007; accepted 23 April 2008.


   REFERENCES
TOP
 ABSTRACT
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 APPENDIX
 REFERENCES
 

  1. Moeller S, Gioberge S, Brown G. ESRD patients in 2001: global overview of patients, treatment modalities and development trends. Nephrol Dial Transplant 2002;17 : 2071-6.[Free Full Text]
  2. Ates K, Nergizoglu G, Keven K, Sen A, Kutlay S, Erturk S, et al. Effect of fluid and sodium removal on mortality in peritoneal dialysis patients. Kidney Int 2001;60 : 767-76.[Medline]
  3. Freida P. Impact of a low sodium dialysate on usual parameters of cardiovascular outcome in anuric patients during APD [Abstract]. Perit Dial Int 1999;19 (Suppl 1): S15.
  4. Posthuma N, ter Wee PM, Verbrugh HA, Oe PL, Peers E, Sayers J, et al. Icodextrin instead of glucose during the daytime dwell in CCPD increases ultrafiltration and 24-h dialysate creatinine clearance. Nephrol Dial Transplant 1997;12 : 550-3.[Abstract/Free Full Text]
  5. Dallas F, Jenkins SB, Wilkie ME. Enhanced ultrafiltration using 7.5% icodextrin/1.36% glucose combination dialysate: a pilot study. Perit Dial Int 2004;24 : 542-6.[Abstract/Free Full Text]
  6. Jenkins SB, Wilkie ME. An exploratory study of a novel peritoneal combination dialysate (1.36% glucose/7.5% icodextrin), demonstrating improved ultrafiltration compared to either component studied alone. Perit Dial Int 2003; 23:475 -80.[Abstract/Free Full Text]
  7. Freida P, Galach M, Divino Filho JC, Werynski A, Lindholm B. Combination of crystalloid (glucose) and colloid (icodextrin) osmotic agents markedly enhances peritoneal fluid and solute transport during the long PD dwell. Perit Dial Int 2007;27 : 267-76.[Abstract/Free Full Text]
  8. Freida P. Optimal fluid and sodium removal during long dwell exchange in peritoneal dialysis using bimodal ultrafiltration based on mixed crystalloid and colloid osmosis with a low sodium formulation [Abstract]. Perit Dial Int 2005;25 (Suppl 5): S15.[Abstract/Free Full Text]
  9. Imholz AL, Koomen GC, Struijk DG, Arisz L, Krediet RT. Fluid and solute transport in CAPD patients using ultralow sodium dialysate. Kidney Int 1994;46 : 333-40.[Medline]
  10. Leypoldt JK, Charney DI, Cheung AK, Naprestek CL, Akin BH, Shockley TR. Ultrafiltration and solute kinetics using low sodium peritoneal dialysate. Kidney Int 1995;48 : 1959-66.[Medline]
  11. Rippe B, Simonsen O, Stelin G. Clinical implications of a three-pore model of peritoneal transport. Adv Perit Dial 1991; 7:3 -9.[Medline]
  12. Rippe B, Levin L. Computer simulations of ultrafiltration profiles for an icodextrin-based peritoneal fluid in CAPD. Kidney Int 2000; 57:2546 -56.[Medline]
  13. Rippe B. A three-pore model of peritoneal transport. Perit Dial Int 1993;13 (Suppl 2):S35 -8.[Abstract]
  14. Waniewski J, Heimburger O, Werynski A, Lindholm B. Simple models for fluid transport during peritoneal dialysis. Int J Artif Organs 1996; 19:455 -66.[Medline]
  15. Mujais S, Nolph K, Gokal R, Blake P, Burkart J, Coles G, et al. Evaluation and management of ultrafiltration problems in peritoneal dialysis. International Society for Peritoneal Dialysis Ad Hoc Committee on Ultrafiltration Management in Peritoneal Dialysis. Perit Dial Int 2000; 20(Suppl 4):S5 -21.[Medline]
  16. Gokal R, Moberly J, Lindholm B, Mujais S. Metabolic and laboratory effects of icodextrin. Kidney Int Suppl2002; 81:S62 -71.[Medline]
  17. Wang R, Skoufos L, Martis L. Glucose monitoring for diabetic patients using icodextrin. Perit Dial Int2004; 24:296 -7.[Free Full Text]
  18. Janssen W, Harff G, Caers M, Schellekens A. Positive interference of icodextrin metabolites in some enzymatic glucose methods. Clin Chem 1998; 44:2379 -80.[Free Full Text]
  19. Mehmet S, Quan G, Thomas S, Goldsmith D. Important causes of hypoglycaemia in patients with diabetes on peritoneal dialysis. Diabet Med 2001;18 : 679-82.[Medline]
  20. Waniewski J, Debowska M, Lindholm B. How accurate is the description of transport kinetics in peritoneal dialysis according to different versions of the three-pore model? Perit Dial Int 2008; 28:53 -60.[Abstract/Free Full Text]
  21. Waniewski J, Heimburger O, Park MS, Werynski A, Lindholm B. Methods for estimation of peritoneal dialysate volume and reabsorption rate using macromolecular markers. Perit Dial Int1994; 14:8 -16.[Abstract/Free Full Text]
  22. Vonesh EF, Rippe B. Net fluid absorption under membrane transport models of peritoneal dialysis. Blood Purif1992; 10(3-4):209 -26.[Medline]
  23. Vonesh EF, Story KO, O'Neill WT. A multinational clinical validation study of PD ADEQUEST 2.0. PD ADEQUEST International Study Group. Perit Dial Int1999; 19:556 -71.[Abstract/Free Full Text]
  24. Garcia-Lopez E, Pawlaczyk K, Anderstam B, Qureshi AR, Kuzlan-Pawlaczyk M, Heimburger O, et al. Icodextrin metabolism and alpha-amylase activity in nonuremic rats undergoing chronic peritoneal dialysis. Perit Dial Int 2007;27 : 415-23.[Abstract/Free Full Text]
  25. Vonesh EF, Story KO, Douma CE, Krediet RT. Modeling of icodextrin in PD Adequest 2.0. Perit Dial Int 2006;26 : 475-81.[Abstract/Free Full Text]



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J. Waniewski, M. Debowska, and B. Lindholm
WATER AND SOLUTE TRANSPORT THROUGH DIFFERENT TYPES OF PORES IN PERITONEAL MEMBRANE IN CAPD PATIENTS WITH ULTRAFILTRATION FAILURE
Perit. Dial. Int., November 1, 2009; 29(6): 664 - 669.
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