Personal dialysis capacity (PDCTM) test: a multicentre clinical study

Wim Van Biesen, Ola Carlsson, Roberto Bergia, Michael Brauner, Anders Christensson, Sandrine Genestier, Marianne Haag-Weber, James Heaf, Preben Joffe, Ann-Cathrine Johansson, Bertrand Morel, Friedrich Prischl, Dirk Verbeelen and Andreas Vychytil



   Abstract
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 Abstract
 Introduction
 Subjects and methods
 Results
 Discussion
 Members of the PDC-study...
 References
 
Background. The assessment of the peritoneal membrane capacity and physiology of the individual patient is becoming increasingly important. It allows the prescription of an individualized peritoneal dialysis (PD)-regimen, and the monitoring of peritoneal membrane function over time. The PDCTM program offers the possibility to evaluate the peritoneal membrane characteristics and to predict solute and water removal by simulation of different treatment regimens.

Methods. This study evaluates the relevance of the PDCTM program when routinely used. The PDCTM data of 336 patients from nine different centres in Europe were evaluated.

Results. The area parameter was 20 985±7578 cm/1.73 m2 (mean±SD). The reabsorption of fluid after dissipation of glucose, JvAR, was 1.97±1.00 ml/min/1.73 m2. The large pore fluid flux, JvL, was 0.11±0.07 ml/min/1.73 m2. A multivariate model for prediction of serum albumin included dialysate protein loss, JvL, JvAR, nPCR, A0/{Delta}X, BMI and gender (R2=0.81, P<0.001). Total clearance fell with increasing PD duration (P<0.001). A negative relation between A0/{Delta}X and ultrafiltration (rho=-0.26, P<0.05), a positive relation between A0/{Delta}X and peritoneal creatinine clearance (rho=0.52, P<0.05) and urea clearance (rho=0.36, P<0.05), and a positive relation between measured peritoneal creatinine and urea clearance (rho=0.64, P<0.01) was observed.

Conclusions. In summary, the present study shows that the PDCTM program is a robust, accurate method to describe the peritoneal membrane transport characteristics. Analysis of PDCTM data of large groups of patients, especially if followed up over time, can give interesting information on the physiology of the peritoneal membrane and the impact of different parameters on it.

Keywords: PDCTM; peritoneal dialysis; PET test; renal replacement therapy



   Introduction
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 Abstract
 Introduction
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Peritoneal dialysis (PD) is a well-established technique for renal replacement therapy (RRT) [1]. The original concept of four manual exchanges of 2 l of dialysate for all patients has, however, been progressively abandoned [2]. The emergence of automated PD and the development of polyglucose solutions [3,4] force the physician to adapt the PD prescription to the individual needs of the patient. In contrast with haemodialysis (HD) membranes, where specifications such as permeability and surface area are well known, the peritoneal membrane characteristics of each PD patient are different. Hence, a tool to evaluate the ‘capacity’ or specifications of the peritoneal membrane is needed. Such a tool should have the capacity to describe the most relevant parameters of the peritoneal membrane, in order to predict the most optimal regimen for each patient. This tool might also be of help to time the ideal moment of transfer between different treatment regimens in an integrative care approach [1]. Indeed, functional changes might give a warning that the peritoneal membrane is wearing off and that a resting period on HD is indicated [5]. Regular monitoring of peritoneal transport in the individual patient over time is therefore important.

The peritoneal equilibration test (PET test) [6] is actually the most widely used test to describe the peritoneal membrane characteristics. It is however sensitive to bias induced by convective clearance, and it does not provide data on obtained clearances nor on underlying transport mechanisms. The description of ‘high’ and ‘low’ transporters is moreover confusing, as the measured parameters do not relate to the amount of toxins transported, but only to the speed of transport. Therefore, the terminology of ‘fast’ and ‘slow’ transporters seems a more accurate alternative [7]. Also, in patients with ultrafiltration failure, the PET test can fail to explain the underlying mechanism. Therefore, some adaptations, like the use of a 3.86% instead of a 2.27% glucose solution and the evaluation of sodium sieving during the PET test have been proposed [7,8]. There are three different computer programs on the market using different models for data collection and calculation of the peritoneal transport of solutes and fluid, PackPD® (Fresenius®, Walnut Creek, CA), PD-ADEQUEST® (Baxter® Health Care Corp., Round Lake, IL) and PDCTM (Gambro Lundia AB, Lund, Sweden). The most important differences between the programs are the model used for calculation and the sampling procedure. There are also some differences in the data collection between the three programs. The PDCTM program uses data collected over five exchanges with different glucose strengths and dwell times. The multiple sampling in Pack-PD® and PDCTM is a major advantage as the accuracy of the predictions increases with the amount of data used in the calculations.

The PDCTM program describes the peritoneal membrane characteristics by means of three physiological parameters (the PDC parameters): (i) the area parameter A0/{Delta}X, which determines the diffusion of small solutes; (ii) the final reabsorption rate of fluid from the abdominal cavity to blood when the glucose gradient has dissipated (JvAR); and (iii) the large pore fluid flux (JVl) which determines the loss of protein to the PD fluid [9].

The PDCTM program has been evaluated in some previous studies, both in adults and children, in a limited number of patients [10,11]. Our study is different as it includes more patients, and from different centres. Moreover, the data were collected as a routine clinical practice, without special training of the staff or patients. The PDC study group wanted: (i) to evaluate the capacity of the PDCTM test to describe peritoneal membrane characteristics in a robust and accurate way and (ii) to relate the PDCTM-derived parameters with clinical data. In a second part of the study, we will: (i) test the reliability of the program in accurately predicting alternative regimens and (ii) conduct a long-term follow-up of peritoneal membrane function in individual patients.



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PDCTM data were collected in nine different European centres (see below for the PDCTM study group members). The data of the first PDCTM test performed in the period between 1/1/96 and 31/12/97 were registered in a central database. As it was aimed to analyse the robustness of the test in routine circumstances, no special instructions were given to the participating centres, besides those available in the manual of the PDCTM program.

In brief, patients were asked to perform five exchanges during a so-called ‘PDCTM day’ (see Figure 1Go). The PDCTM day started with a short PD dwell (2–3 h), followed by two intermediate dwells (4–6 h), and another short exchange (2–3 h), and finally a long overnight dwell (Figure 1Go). The glucose concentrations are also varied so that one of the short dwells is performed with another glucose concentration than the others. This alteration is applied to obtain as much information as possible about the characteristics of the peritoneal membrane. Patients are asked to weigh the bags, mix the content carefully and take samples from all drained bags. They also note the exact weight of the bag before and after instillation of the fluid. Also, the exact time of instillation and drainage are noted on the PDCTM sheet. They also collect 24-h urine during the PDC day, to calculate total clearance and nPCR.



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Fig. 1.  Notation sheet for a PDC day.

 
The dialysate samples are analysed for urea, creatinine, glucose and albumin (protein). Urine concentrations of urea, creatinine and protein are analysed. Blood samples are taken at the beginning and the end of the test for determination of sodium, urea, creatinine, glucose and albumin (or protein). In 46 patients, only one blood sample was drawn. All laboratory evaluations were performed according to the routine standards in the local laboratories. Residual GFR was calculated as the mean of urea and creatinine clearance.

Besides these parameters, age, body weight, height, time since start of PD, diabetic status and number of episodes of peritonitis were also registered.

Statistical analysis
All statistical analysis was performed with SPSS10.0. Descriptive statistics are provided for the demographic variables and the PDCTM-derived variables. Student's t-test was used to compare means of parameters between two groups. One-way ANOVA with post-hoc testing using Scheffé test was used to compare multiple groups. Spearman's correlation analysis was used to correlate variables. Linear regression was used to relate two variables. Multivariate regression was used, either as a fixed model including predefined parameters, or in a step-forward mode to allow predictive modelling of a dependent variable in function of independent variables. Agreement between measured and predicted values was evaluated with Bland–Altmann analysis.



   Results
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In total, 336 patients were included. Descriptive data are summarized in Table 1Go.


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Table 1.  Demographic characteristics

 
Peritonitis occurred at least once in 42.6% of patients, with 1.22±2.1 episodes/patient in this subgroup. Diabetes mellitus was present in 37% of patients.

The values for the area parameter, JvAR and JvL are summarized in Table 1Go.

Residual glomerular filtration rate (GFR) was 2.0±2.0 ml/min (median 1.5, range 0.0–12.1 ml/min). There were 129 patients (38%) with a residual GFR <1 ml/min. These patients tended to be on dialysis longer as compared with patients with a residual renal function >1 ml/min (20.8±19.1 vs 10.4±11.1 months, P=0.001). Residual GFR was lower as patients were longer on PD (one-way ANOVA, P<0.001, Table 2Go). PD clearance based on the regimen of the PDCTM day did not differ between patients in function of time on PD (one-way ANOVA, P=0.33, Table 2Go). There was no difference in the PDC-derived parameters between patients with and those without a history of peritonitis (Table 3Go).


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Table 2.  GFR and peritoneal clearance in patients with different times on PD (n=336)

 

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Table 3.  Comparison of PDCTM test-derived parameters in patients with and without peritonitis

 
In diabetic patients (37%), A0/{Delta}X tended to be higher than in non-diabetics, although this difference did not reach statistical significance, probably due to lack of statistical power (23 168±9561 vs 20 460±7774 cm/1.73 m2, P=0.088). There was no difference between diabetics and non-diabetics for JvAR or JvL (1.98±1.10 vs 1.84±1.05 ml/min/1.73 m2, P=0.46 and 0.089±0.050 vs 0.097±0.038 ml/min/1.73 m2, P=0.36, respectively).

A negative correlation was found between serum albumin and JvL (Spearman's rho=-0.36, P<0.001) and between serum albumin and age (Spearman's rho=-0.14, P=0.008). There was a positive correlation between serum albumin and peritoneal protein loss (Spearman's rho=0.14, P=0.01). When JvL and peritoneal protein loss were entered together in a multivariate regression analysis, the model for albumin became: serum albumin (g/dl)=3.35–11.58 JvL+0.15xprotein loss (R2=0.58, P<0.001). There was a negative correlation between serum albumin and the area parameter (Spearman's rho=-0.32, P<0.001). When serum albumin was modelled as a function of total clearance, peritoneal clearance and residual GFR, only GFR was retained in the model (serum albumin=3.25+0.05xGFR, R2=0.026, P to enter=0.003, P to remove peritoneal and total clearance was 0.12 and 0.12, respectively). Serum albumin was lower in patients with a GFR <0.5 ml/min as compared with those with a GFR >5 ml/min (32.17±7.37 vs 34.23±6.15 ml/min, respectively, P=0.008).

In a step-forward multivariate regression, serum albumin was predicted by a model including dialysate protein loss, JvL, JvAR, nPCR, A0/{Delta}X, BMI and gender (R2=0.81, P<0.001) (Table 4Go). Age was not retained in this model.


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Table 4.  Result of the step-forward multivariate regression model for serum albumin

 
There was a negative correlation between the surface area parameter A0/{Delta}X and ultrafiltration (Spearman's rho=-0.26, P<0.001).

There was a negative correlation between nPCR and age (Spearman's rho=-0.24, P<0.001).

A positive correlation was found between the area parameter A0/{Delta}X and peritoneal creatinine clearance (Spearman's rho=0.52, P<0.001) and the peritoneal urea clearance (Spearman's rho=0.36, P<0.001).

There was a weak correlation between time on PD and A0/{Delta}X (Spearman's rho=0.14, P=0.03). The A0/{Delta}X in patients <3 months on PD was lower than in patients on PD between 3 and 36 months (one way ANOVA, P=0.04, post hoc LSD, P=0.003).

There was a positive correlation between protein loss and the area parameter (Spearman's rho=0.29, P<0.001). There was a positive correlation between nPCR and residual GFR (Spearman's rho=0.24, P<0.001). In a multivariate step-forward model, nPCR was described by both peritoneal and residual clearance (nPCR=0.197+0.171xCcrCl+0.06xGFR, R2=0.193, P=0.001).

There was a positive correlation between measured peritoneal creatinine and urea clearance (Spearman's rho=0.64, P<0.001).

There was a very high correlation between measured and predicted values for peritoneal urea clearance (Spearman's rho=0.88, P<0.001), peritoneal creatinine clearance (Spearman's rho=0.91, P<0.001) and for ultrafiltration volume (Spearman's rho=0.97, P<0.001). However, the program tends to underestimate the measured urea clearance, and to overestimate the measured creatinine clearance, as is apparent from the regression analysis (Figure 2Go) and as was shown in a Bland–Altman analysis. The Bland–Altman represents the confidence limits of agreement between standardized measured and predicted values of urea clearance, creatinine clearance and ultrafiltration. The actual standard deviation of the difference between measured and predicted values was 0.50 ml/min for urea clearance, 0.55 ml/min for creatinine clearance and 282 ml/24 h for ultrafiltration.



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Fig. 2.  Linear regression analysis of measured vs predicted peritoneal urea clearance (A), of measured vs predicted peritoneal creatinine clearance (B), and of measured vs predicted ultrafilration (C). Bland–Altman analysis of urea clearance (D), creatinine clearance (E) and ultrafiltration (F). The x-axis shows the mean of the measured and predicted value. The y-axis shows the difference between the standardized measured and the standardized predicted value.

 



   Discussion
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The results of this multi-centre, cross-sectional study demonstrate that the PDCTM test is a valuable tool in the evaluation of the peritoneal membrane function of the individual patient. Even when used in every day practice, the data collected allow accurate prediction of the membrane characteristics. This is of great importance for three different reasons. First, the correlation between adequacy and outcome and the possible relation between ultrafiltration failure and outcome have urged the nephrologists to adapt the PD regimens to the individual needs of their patients [2]. Such an adaptation is greatly facilitated if the individual characteristics of the patients can be evaluated and categorized [12]. Secondly, the peritoneal membrane function seems to deteriorate over time [5], at least in certain patients, and a timely detection of this evolution, urging a transfer to HD, might improve outcome of these patients and avoid the progress to peritoneal sclerosis. Thirdly, the physiology of the peritoneal membrane is not completely understood nor described at this moment. A tool like PDCTM can be of help to clarify the underlying mechanisms and transpose them to the clinical situation. Of course, the actual set-up of this study does not allow drawing conclusions on the accuracy of the PDCTM program to predict alternative regimens, and this will be evaluated in the second part of the study. Our registry also collects a substantial number of data on PD characteristics and on important physiologic properties of the peritoneal membrane, and will allow an insight in the evolution of these parameters.

PDCTM parameters and their interpretation
The area parameter, A0/{Delta}X, was 20 985±7578 cm/1.73 m2 (mean±SD). As A0/{Delta}X represents the unrestricted pore area available for small solute clearance, it can be considered as a more refined and accurate alternative to D/P ratios. Hence, the A0/{Delta}X value can easily be translated into a PET-classification terminology, whereby A0/{Delta}X values >28 500, between 21 000 and 28 500, between 13 500 and 21 000 and <13 500 represent fast, average-fast, average-slow and slow transporters, respectively. The A0/{Delta}X value reflects only the perfused capillaries. It also takes into account the ‘diffusion distance’, i.e. the mean distance between the capillary and the peritoneal space. Indeed, it is well accepted that this distance is different for all capillaries [13]. For this reason, it is impossible to separate the area parameter from the diffusion distance, as both the number and diameter of pores as the diffusion distance will determine at the end the diffusive properties (Figure 3Go).



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Fig. 3.  Interpretation of A0/{Delta}X. The area parameter, A0/{Delta}X, is composed of two components that cannot be separated because they both determine the diffusive properties of the membranes in an inter-related way. (A) A0 reflects the total pore area available for transport. This is determined by the total surface of the peritoneal membrane (shaded square) and the number of perfused capillaries per surface area (open circles). (B) {Delta}X is the ‘diffusion path distance’, i.e. the distance that molecules have to travel from the capillary space before they reach the dialysate compartment. It should be noted that also the ‘resistance’ of the interstitium plays a role in this ‘distance’. If the interstitium contains parts where no diffusion is possible (e.g. because of fibrosis, in the picture schematically represented by shaded area), the diffusion path distance will be longer than the actual distance between the capillary and the dialysate compartment.

 
As not all capillaries are perfused under normal circumstances, the area parameter can increase several fold by capillary recruitment during vasodilation, e.g. caused by vasoactive substances, or the introduction of an acidic hyperosmolar dialysate solution. This short-term vasodilation in the beginning is taken into account by the PDCTM program throughout the calculations. The number of perfused capillaries can also be increased by neo-angiogenesis in the peritoneal membrane, a process that has been observed both in diabetic rats and in rabbits after long-term instillation with glucose-containing PD-fluid [14]. This increase in transport rate by the increase of perfused capillary surface area should clearly be distinguished from that induced by an increased permeability (decreased selectivity) of the membrane as such. In the latter case, the hydraulic conductance of the membrane itself, mainly afflicted by the larger pores and the interstitium, is altered. The hydraulic conductance can increase as a result of inflammation, or the accumulation of substances like AGEs. It can decrease because of an increase of fibrous tissue or glycosaminoglycan content in the interstitial space. Both the PET test and the PD-Adeqest program fail to detect the difference between increased surface area and increased membrane permeability, as both will result in an increase of D/P ratios. In the PDCTM program, the two processes can be evaluated separately by the A0/{Delta}X and the JvL. A patient with a ‘fast transporting’ membrane can have a large A0/{Delta}X and a normal JvL, indicating a high number of perfused capillaries, but without inflammation, or a normal or increased A0/{Delta}X in combination with a high JvL, indicating an increased permeability by inflammation. The PET description ‘fast transport status' comprises thus two distinct conditions. The observation that only fast transporters with a concomitant inflammatory state [15] have a relation with poor outcome, also indicates that information obtained by PET might be incomplete, and can lead to erroneous conclusions.

It is also of note that the D/P values obtained by the PET test are prone to bias induced by the differences in ‘relative fill volume’. Indeed, the standardized fill volume of 2 l can induce capillary recruitment, and thus a false high D/P, in a small woman. In contrast, in a large male, the surface of the peritoneal membrane might not be covered completely, thus leading to a false low D/P. The PDCTM program allows the investigator to adapt the fill volumes to the stature of the patient. Even different fill volumes can be used during one test.

The A0/{Delta}X was higher in patients with diabetes mellitus as compared with non-diabetics (although just not statistically significant, P=0.08), whereas the JvL was not different between the two groups. This suggests that the neo-angiogenesis observed in the peritoneal membrane of diabetic rats [14] is probably also present in diabetic patients.

The area parameter tended to increase as patients were longer on PD when the PDCTM test was performed, which is in accordance with other studies [5,16].

The parameter JvAR is determined by the membrane permeability, the lymphflow and the equilibration of the Starling forces over the peritoneal membrane. After correction for A0/{Delta}X, this is thus mainly by the difference in hydraulic and osmotic pressures between the intravascular and the peritoneal space, and the lymph flow. JvAR can be visualized as the linear decline of the intraperitoneal-volume-over-time curve in the second part of the dwell. The inclination of this final part of the curve is by definition independent from the initial osmotic (glucose) strength used. From Figure 4Go, it can be understood that this value is strongly dependent upon the volume drained after the long overnight dwell. If this drainage is incomplete, the JvAR will be overestimated. This might explain why the JVAR obtained with the PDCTM program is higher than those reported by other authors [17]. As JvAR is not needed for calculations of other parameters, it can however be seen as a peculiarity of the PDCTM program without further importance.



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Fig. 4.  Determination of JvAR. JvAR is the rate of reabsorption of fluid after osmotic equilibrium has been reached. It is calculated as the slope of the final part of the intraperitoneal volume over time curve. It is of note that in the PDCTM test, the initial part of the curve is based on four data points of drained volume (the short dwells during the day, represented by ¥) whereas the final part is only based on one data point, the drained volume after the long night dwell (in the figure represented by {dagger}). Therefore, small mistakes in the drained volume after the night dwell will have a big impact on JvAR. As the most common mistake is incomplete drainage (represented in the figure by ¤), resulting in an underestimation of the intraperitoneal volume, and thus an overestimation of JvAR (dotted line). Different full-line curves represent different osmotic agents and different osmotic concentrations. Note that the slope of the final part (and thus JvAR) is independent of the osmotic agent used.

 

Adequacy and nutritional parameters
The analysis of serum albumin concentration with the different parameters leads to some interesting observations. In a multivariate analysis JvL, and protein loss in the dialysate turned out to be the main determinants of serum albumin concentration. The negative correlation coefficient for JvL is within the line of expectation, as JvL reflects large pore flux, and thus the permeability of the membrane for proteins. The positive correlation of serum albumin concentration with dialysate protein loss might seem more surprising, as one would expect that protein loss in the dialysate would lead to a lower serum albumin concentration. The multivariate analysis is consistent with a model whereby the serum albumin concentration is the driving force for transmembranous protein leakage to the dialysate, and where the extent of this leakage is related to JvL, the membrane selectivity. The low serum albumin concentration is thus probably more related to inflammation than to the protein loss per se. Other parameters included in the model, be it with a far lower impact, were JvAR, nPCR, A0/{Delta}X, BMI and gender. Surprisingly, no adequacy-related parameters were included. It has been suggested that there is a negative correlation between serum albumin and age. In our study, this relationship has also been found in the univariate correlation, but not in the multivariate regression. This implicates that the negative relation of serum albumin and age is a secondary phenomenon caused by parameters that also change with age, and which are the real driving parameters for the observed relationship. In this regard, the correlation of nPCR with age is of interest, as it is quite acceptable that food intake decreases with age, and that this might lead eventually to a decrease in serum albumin.

To avoid bias induced by mathematical coupling between Kt/V and nPCR, we used serum albumin as a parameter to correlate nutritional status with adequacy. This approach revealed a lower serum albumin in patients with a residual GFR <0.5 ml/min as compared with those with a GFR >5 ml/min. A multivariate analysis including total, peritoneal and residual renal clearance demonstrated that only residual clearance predicted serum albumin. This would lead us to the conclusion that only residual renal clearance is of importance. However, this conclusion is hampered by the fact that patients with less residual renal function tend to be on PD longer, which is on its own a risk factor, and because of the fact that the range of peritoneal clearances is small, like in some other studies. The results of the Ademex trial [18] indicate that peritoneal small solute clearance is not related to outcome. Aslam et al. [19] and Harty et al. [20] reported that they could improve the outcome of patients by increasing PD clearance as residual renal function declined. Although these studies demonstrate that peritoneal clearance does have an effect, the discussion whether 1 ml of peritoneal clearance and 1 ml of renal clearance have an equal impact on outcome remains unsolved until now. Registration of longitudinal PDCTM data and the long-term outcome of these patients, as planned by our study group, will create the opportunity to study this further in the future.

About one in three of the patients had a residual renal function of <1 ml/min. These patients tended to be on PD longer, and although their peritoneal clearances were higher, the total clearance was lower compared with the patients with a residual renal function >1 ml/min.

In patients with RRF, there is an important tubular secretion of creatinine, so that creatinine clearance overestimates true GFR. Adequacy targets are thus more difficult to obtain when expressed as peritoneal creatinine clearance for patients with no or little RRF, whereas for patients with a rather large residual renal function, total creatinine clearances may be falsely high. The difference in correlation coefficients between peritoneal clearance of creatinine and urea with A0/{Delta}X indicates that the behaviour of these two markers is different for different transport categories. It is thus wise to use both creatinine and urea clearance for the interpretation of adequacy data.

Robustness of the PDCTM program
The original publication of Haraldsson [10] showed that the PDCTM program is a useful and reliable tool to determine the peritoneal membrane characteristics of each individual patient. However, this study included only data from one centre and in a limited number of patients. The present study demonstrates that the test will give the same reliability in its predictions when used in nine different European centres, each with different experience in performing the PDCTM test. For all PDC-derived parameters, the values we found are quite comparable with those obtained in the cohort of Haraldsson. This indicates that the test delivers reliable data, even when performed as a routine procedure. This is of course crucial for the long-term follow-up of peritoneal membrane characteristics. This study only evaluates the capacity of the PDCTM program to describe the membrane characteristics, and thus the validity of the mathematical model in the tested circumstances, and does not allow to draw conclusions on the capacity of the PDCTM program to accurately predict alternative regimens. This property will be tested in the second part of the study, where long-term follow-up and evolution of membrane characteristics will also be recorded.

In conclusion, this study analysed the PDCTM program data of different European centres. It demonstrates that the PDCTM program is a helpful and reliable tool for routine evaluation of the peritoneal membrane and that it can enhance the insight in the physiology of the peritoneal membrane. In contrast with PET, PDCTM allows more accurate analysis of transport characteristics and ultrafiltration failure, and of the underlying mechanisms.

The major predictors for serum albumin were JvL and protein loss in the dialysate. Serum albumin was lower in patients without RRF. Diabetic patients were found to have a higher vascular surface area, be it just not significant due to low patient number. A longitudinal follow-up of PDCTM test-derived data can provide us with the correlation between outcome and adequacy, ultrafiltration profile, nutrition and membrane characteristics, and such a study seems thus warranted.



   Members of the PDC-study group (alphabetical order)
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 Abstract
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 Subjects and methods
 Results
 Discussion
 Members of the PDC-study...
 References
 
R. Bergia, Ospedale Provinciale ‘Degli Infermi’, Biella, Italy; M. Brauner, Humboldt Krankenhaus, Berlin-Reinickendorf, Germany; Ola Carlsson, Gambro, Sweden; A. Christensson, Malmö University Hospital, Malmö, Sweden; S. Genestier, Colmar Hospital, Colmar, France; M. Haag-Weber, KFH Straubing, Straubing, Germany; J. Heaf, Herlev Hospital, Herlev, Denmark; P. Joffe, Odense Universitetshospital, Odense, Denmark; A.C. Johansson, Sahlgrenska University Hospital, Gothenburg, Sweden; B. Morel, Chambery Hospital, Chambery, France; F. Prischl, Krankenhaus Barmherwigen Schwestern, Wels, Austria; W. Van Biesen, University Hospital, Ghent, Belgium; D. Verbeelen, University Hospital VUB, Brussels, Belgium; A. Vychytil, Universitätskliniken Wien, Wien, Austria.



   Notes
 
Correspondence and offprint requests to: W. Van Biesen, Department of Internal Medicine, Renal Division, University Hospital Gent 0K12IA, De Pintelaan 185, B-9000 Gent, Belgium. Email: wim.vanbiesen{at}rug.ac.be Back



   References
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 Abstract
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 Subjects and methods
 Results
 Discussion
 Members of the PDC-study...
 References
 

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Received for publication: 9. 9.02
Accepted in revised form: 22.10.02