Is malnutrition an independent predictor of mortality in peritoneal dialysis patients?

Sung Hee Chung1,2, Bengt Lindholm2 and Hi Bahl Lee1

1Hyonam Kidney Laboratory, Soon Chun Hyang University, Seoul, Korea and 2Divisions of Baxter Novum and Renal Medicine, Department of Clinical Science, Karolinska Institute, Huddinge University Hospital, Stockholm, Sweden

Correspondence and offprint requests to: Hi Bahl Lee, MD, Hyonam Kidney Laboratory, Soon Chun Hyang University Hospital, 657 Hannam-dong, Yongsan-ku, Seoul 140-743, Korea. Email: hblee{at}hkl.ac.kr



   Abstract
 Top
 Abstract
 Introduction
 Patients and methods
 Results
 Discussion
 References
 
Background. It has been established that malnutrition (MN) is a strong predictor of mortality in peritoneal dialysis (PD) patients. However, MN is often the consequence of co-morbid diseases (CMD), and the confounding effect of CMD on mortality in malnourished PD patients has not been clearly defined. In this study, we tested the hypothesis that MN without CMD may not be associated with significant mortality. This study was, therefore, designed to dissociate the influence of CMD on mortality in PD patients from that of MN.

Methods. A total of 153 consecutive PD patients (88 males, mean age 53.3 ± 12.3 years) were included in this study. All underwent initial assessment of nutrition, CMD survey and peritoneal equilibration test at a mean of 7 days (range 3–24 days) after beginning PD. Nutritional status was assessed by subjective global assessment (SGA) and other methods. CMD surveyed included diabetes, cardiovascular disease, liver disease and respiratory disease, and co-morbidity was graded by Davies index. Based on the nutritional status as assessed by SGA and presence of CMD, patients were divided into four groups; MN with (n = 50) or without (n = 14) CMD, and normal nutrition (NN) with (n = 53) or without (n = 36) CMD.

Results. Of 153 patients, 64 (41.8%) were malnourished and 103 (67.3%) had one or more CMD. Of the 103 patients with CMD, 48.5% had MN, and 78% of the 64 patients with MN had CMD. Patients with MN and CMD were older and had lower initial serum albumin (sAlb), serum creatinine, fat-free oedema-free body mass, percentage lean body mass and SGA score and higher initial dialysate/plasma creatinine concentration ratio at 4 h dwell (D4/P4 Cr) and co-morbidity score. On Kaplan–Meier analysis, 2-year patient survival was significantly lower in patients with MN and CMD than in the other groups (63.1, 90.9, 87.5 and 96.4% for subgroups with both MN and CMD, MN without CMD, NN with CMD and NN without CMD, respectively, P = 0.001). On Cox proportional hazards analysis, age, co-morbidity score and D4/P4 Cr, but not SGA score or sAlb concentration, were found to be independent risk factors for mortality. After adjustment for age, gender, sAlb, residual renal function and D4/P4 Cr, patients with both MN and CMD had a risk of mortality that was 3.3 times that of patients with MN but without CMD (risk ratio 9.01 vs 2.72). Patients with MN without CMD had a risk ratio of 2.72 compared with NN without CMD, but this difference was not statistically significant. In patients with NN and CMD, the risk ratio for mortality was five times that of patients with NN without CMD.

Conclusions. This study demonstrates that there is a high prevalence of MN and CMD at the start of PD and that the combined presence of CMD and MN is associated with high mortality. MN alone is associated with a statistically insignificant increase in mortality. This underlines the importance of CMD as a cause of poor clinical outcome in malnourished PD patients. However, in the present study, a relatively limited number of patients with MN but without CMD were analysed and a type two error therefore cannot be excluded.

Keywords: co-morbid diseases; malnutrition; mortality; peritoneal dialysis



   Introduction
 Top
 Abstract
 Introduction
 Patients and methods
 Results
 Discussion
 References
 
The ADEMEX study [1] shows that dialysis adequacy as determined by small solute clearance may not be as critical as previously thought for the clinical outcome of peritoneal dialysis (PD) patients. This suggests the need to focus more on other factors which could explain the high mortality in PD patients. Two factors which are known to be associated with increased mortality are malnutrition and co-morbid diseases. Protein energy malnutrition and muscle wasting are present in a large proportion of patients with chronic renal failure (CRF). This may be a consequence of uraemia per se or related to co-morbid conditions [2]. Malnutrition is a strong predictor of mortality in maintenance PD patients [3,4], and anthropometric and biochemical signs of malnutrition are associated with increased mortality in haemodialysis (HD) patients [5]. Although malnutrition is a strong predictor of mortality, malnutrition as such is not generally a direct cause of death [6]. Serum albumin is a powerful predictor of survival [3], but is not an ideal nutritional marker [7].

On the other hand, co-morbid diseases are common in CRF patients at the start of dialysis and have been shown to predict both hypoalbuminaemia and mortality [8]. Recent observations suggest that there may be different types of malnutrition in dialysis patients [9]. Stenvinkel et al. [10] proposed two types of malnutrition: one that is associated with poor nutritional intake due to uraemic syndrome per se, and the other associated with significant co-morbidity. Thus, it is possible that the high mortality rates in malnourished PD patients could be due to co-morbid diseases. However, the impact of co-morbid diseases on mortality in malnourished PD patients has not been clearly defined. It is possible that malnutrition as such in the absence of co-morbid diseases may not necessarily be associated with poor clinical outcome.

The aim of the present study was to test this hypothesis. For this purpose, PD patients were subdivided into four groups based on the presence or absence of co-morbid diseases and malnutrition at the start of PD to dissociate the influence of co-morbid diseases on mortality in PD patients from that of malnutrition.



   Patients and methods
 Top
 Abstract
 Introduction
 Patients and methods
 Results
 Discussion
 References
 
Patients
A total of 153 consecutive patients starting PD in a University Hospital between September 1994 and August 2001 were included in the present study. All patients underwent assessments of nutritional status and adequacy of dialysis, co-morbid disease survey, and peritoneal equilibration test at a mean of 7 days (range 3–24 days) after beginning PD. Of 153 patients included in the analysis, 32 patients were on conservative therapy before starting PD, 100 were on temporary HD for 6–8 weeks after the PD catheter was inserted subcutaneously, and the remaining 21 were transferred from long-term HD.

Nutritional assessment
The nutritional status of the patients was assessed by subjective global assessment (SGA), biochemical measurements, fat-free oedema-free (FFEF) body mass by creatinine kinetics, percentage lean body mass, mid-arm muscle circumference (MAMC), calculation of normalized protein equivalent of total nitrogen appearance (nPNA) and urea kinetic studies.

SGA. We used a 7-point Liekert-type scale of four items: weight loss, anorexia, subcutaneous fat and muscle mass [3]. Each item was given scores to produce a global assessment. Scores of 1–2 represented severe malnutrition; 3–5, moderate to mild malnutrition; and 6–7, normal nutrition. Based on SGA scores, patients were subdivided into three groups: normal nutrition, mild to moderate malnutrition and severe malnutrition.

Biochemical measurements. Blood urea nitrogen (BUN) and serum creatinine concentration were measured by standard techniques. Serum albumin was determined by the bromcresol green method.

Fat-free oedema-free body mass. FEEF body mass was estimated by creatinine kinetics [11]. Total daily creatinine excretion, measured as the amount of creatinine excreted in dialysate and urine plus estimated creatinine lost via the gut, was used to calculate FFEF body mass. FFEF body mass in kg was computed by the following equation [11,12]: FFEF body mass (kg) = 0.029 x total creatinine production in mg/day + 7.38.

Percentage lean body mass was estimated as FFEF body mass normalized to desirable body weight obtained from the Metropolitan Life Insurance Company height and weight table [13].

Mid-arm muscle circumference. MAMC was derived from triceps skinfold thickness (TSF) and mid-arm circumference (MAC): MAMC = MAC – {pi} x TSF. The measurements were repeated three times and the highest score was recorded.

Estimated protein intake. Dietary protein intake was estimated from the protein equivalent of total nitrogen appearance (PNA) using the Bergström equation PNA = 15.1 + 6.95 UNA (g/24 h) + protein loss (g/24 h) [14]. Urea nitrogen appearance (UNA) and protein losses were determined from the measured urea and protein excretion in dialysate and urine. PNA was normalized to desirable body weight (nPNA).

Adequacy of dialysis
Weekly total Kt/V urea and weekly total creatinine clearance were calculated from a 24-h collection of dialysate and urine. The distribution volume of urea (V), which is generally assumed to be equal to total body water, was calculated from the Watson equation [15].

Residual renal function
Residual renal function was estimated as the mean of renal urea and creatinine clearances.

Co-morbid diseases
The following categories of co-morbid diseases were recognized. Cardiovascular disease (CVD) was defined as previous or present history of congestive heart failure, myocardial infarction, angina, peripheral vascular disease or cerebrovascular disease. Respiratory disease included recent active tuberculosis, chronic lung disease or recurrent asthmatic attacks. Liver disease was defined as chronic liver disease proved on biopsy or by persistently elevated serum glutamic–pyruvic transaminase and serum glutamic–oxaloacetic transaminase. Diabetes mellitus included both types I and II.

The co-morbidity was graded by Davies index [16]. The co-morbid score for each patient is simply the number of co-morbid diseases. Fifty patients had score 0, 81 patients score 1, 14 patients score 2, six patients score 3, and two patients score 4. Grade 0 (low risk) is a 0 score, grade 1 (medium risk) is a score of 1–2, and grade 2 (high risk) a cumulative score of >=3.

Peritoneal equilibration test (PET)
A simplified PET was performed to obtain the dialysate/plasma creatinine concentration ratio at 4 h of dwell (D4/P4 creatinine) as described by Twardowski et al. [17], modified by using 4.25% glucose dialysis solution.

Clinical outcomes
The clinical outcome was measured by mortality.

Statistical analysis
Analysis of variance (ANOVA) was used to compare the difference among the four study groups. Comparison of variables between two variables was made using Fisher’s PLSD. {chi}2 test or Fisher’s exact test was used to compare the nominal variables among the different study groups. Actuarial survival rates were determined by the Kaplan–Meier method. A log-rank was used to compare the different survival curves. The patients were censored at transplantation, transfer to HD or other centres, or at the end of the observation period. Cox proportional hazards model was used to identify factors predicting patient mortality and risk ratios (RRs) for mortality in different study groups. Data are presented as mean ± SD. The difference was considered significant when the P-value was <0.05.



   Results
 Top
 Abstract
 Introduction
 Patients and methods
 Results
 Discussion
 References
 
Clinical characteristics at start of PD
Clinical characteristics of study patients at the start of PD are shown in Table 1. Eighty-eight patients were male and the mean age was 53.3 years (range: 22–79 years). A total of 103 patients had one or more co-morbid diseases for a total of 131 events; 84 patients with diabetes, 33 with CVD, nine with liver disease, and five with respiratory disease. Based on Davies index, 95 patients were classified as medium risk group and eight patients as high risk group. Sixty-four patients were considered to be malnourished as assessed by SGA; 60 patients had mild to moderate malnutrition, and four patients had severe malnutrition.


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Table 1. Clinical characteristics at start of PD (n = 153)

 
Figure 1 shows the distribution of patients in four groups according to the presence or absence of malnutrition and co-morbid diseases. A total of 67.3% of all patients had one or more co-morbid diseases. Seventy-eight percent of malnourished patients had co-morbid diseases, and 48.5% of patients with co-morbid diseases had malnutrition.



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Fig. 1. Distribution of patients according to the presence or absence of malnutrition (MN) and co-morbid diseases (CMD). NN, normal nutrition.

 
Initial variables of three different co-morbidity groups
Initial variables in the three different co-morbidity groups are shown in Table 2. There were statistically significant differences in age, initial serum albumin, serum creatinine, FFEF body mass, percentage lean body mass, residual renal function, D4/P4 creatinine and SGA score among the different co-morbidity groups. Patients in the high risk group were older and had lower serum albumin and creatinine concentrations, FFEF body mass, percentage lean body mass, residual renal function and SGA score, and higher D4/P4 creatinine compared with patients in other groups.


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Table 2. Comparison of initial variables among different co-morbidity groups

 
Initial variables of four different study groups according to nutritional status and co-morbid diseases
Initial variables in the four different study groups are shown in Table 3. There were statistically significant differences in age, initial serum albumin, serum creatinine, nPNA, FFEF body mass, percentage lean body mass, residual renal function, D4/P4 creatinine, co-morbidity score and SGA score among the four study groups. Patients with both malnutrition and co-morbid diseases had lower initial serum albumin concentration and SGA score compared with all other groups, higher D4/P4 creatinine compared with patients with malnutrition but without co-morbid diseases, and lower serum creatinine, nPNA, FFEF body mass and percentage lean body mass compared with patients with normal nutrition with or without co-morbid diseases. Patients with malnutrition but without co-morbid diseases had lower nPNA, FFEF body mass and percentage lean body mass compared with patients with normal nutrition with or without co-morbid diseases.


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Table 3. Comparison of initial variables among different study groups

 
Clinical outcome
Patient survival. At the end of the follow-up period (mean duration of PD 20.8 ± 15.2 months, range 1.0–66.8 months), 59 patients were still on PD, 31 patients had died, 47 patients transferred to HD, two patients underwent kidney transplantation, one patient withdrew from treatment, and 13 patients transferred to other units.

Those who died during follow-up were older (60.2 ± 8.6 vs 53.0 ± 12.1 years, P = 0.004) and had a higher proportion of patients with malnutrition and co-morbid diseases (51.6 vs 22.0%, P = 0.01) and lower initial serum albumin (3.5 ± 0.5 vs 3.7 ± 0.5 g/dl, P = 0.05), serum creatinine (7.8 ± 2.6 vs 9.1 ± 2.6mg/dl, P = 0.03), FFEF body mass (31.4 ± 9.6 vs 39.7 ± 10.5 kg, P = 0.0006), percentage lean body mass (54.4 ± 12.8 vs 66.0 ± 13.0, P = 0.0001) and residual renal function (1.2 ± 1.9 vs 2.1 ± 1.6 ml, P = 0.01) compared with those still on PD.

Patient survival rates are shown in Figure 2. Overall patient survival was significantly lower in patients with malnutrition (A), co-morbid diseases (B), and both malnutrition and co-morbid diseases (C), than in the other groups. The 2-year patient survival was 63.1, 90.9, 87.5 and 96.4% for subgroups with malnutrition and co-morbid diseases, malnutrition without co-morbid diseases, normal nutrition with co-morbid diseases and normal nutrition without co-morbid diseases, respectively (P = 0.001).



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Fig. 2. Probability of patient survival in different study groups. Patients with malnutrition (A), co-morbid diseases (B) and both malnutrition and co-morbid diseases (C) had significantly lower patient survival compared with patients in other groups (by log-rank test). Numbers in parentheses represent the number of patients at risk at each time point.

 
Predictors of patient survival and risk ratio for mortality. Predictors of mortality and RR for mortality are shown in Tables 4 and 5, respectively. On Cox proportional hazards univariate analysis, age, co-morbidity score, SGA score, serum albumin concentration and D4/P4 creatinine were predictors of mortality. However, on Cox proportional hazards multivariate analysis, age, co-morbidity score and D4/P4 creatinine, but not SGA score or serum albumin concentration, were found to be independent predictors of mortality (Table 4). After adjustment for age, gender, serum albumin, residual renal function and D4/P4 Cr, the RR for mortality was 9.01 in patients with both malnutrition and co-morbid diseases and 5.07 in normal nutrition with co-morbid diseases (Table 5). The RR of patients with malnutrition without co-morbid diseases was 2.72 and not statistically different from patients with normal nutrition without co-morbid diseases (RR = 1).


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Table 4. Risk factors for mortality (Cox proportional hazards analysis)

 

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Table 5. Adjusteda risk ratios for mortality in different study groups (Cox proportional hazards multivariate analysis)

 


   Discussion
 Top
 Abstract
 Introduction
 Patients and methods
 Results
 Discussion
 References
 
The present study shows that PD patients with malnutrition at the start of PD have a high prevalence of co-morbid diseases (78.1%) and that patients with both malnutrition and co-morbid diseases have a risk of mortality 3.3 times that of patients with malnutrition but without co-morbid diseases (RR 9.01 vs 2.72).

A high prevalence of co-morbid diseases in the malnourished patients in the present study suggests that malnutrition may be related to co-morbid diseases. This agrees with previous studies [5,18], which suggested that co-morbid conditions may influence the nutritional status either by reduced nutritional intake or by increased catabolism, resulting in depleted energy stores, loss of somatic protein and decreased visceral protein. Indeed, in the present study, malnourished patients with co-morbid diseases had lower initial serum albumin than all other study groups, and lower serum creatinine, nPNA, FFEF body mass and percentage lean body mass compared with patients with normal nutrition. Although lean body mass estimated by creatinine kinetics has several limitations, in large groups of patients, lean body mass estimated by creatinine kinetics has been shown to correlate with other estimates of lean body mass [7] and to be a strong predictor of clinical outcome [4,19].

Our observation that serum albumin concentration was significantly lower in malnourished patients with co-morbid diseases than in malnourished patients without co-morbid diseases is in line with previous reports [7,10]. Heimbürger et al. [7] reported that the serum albumin level did not differ significantly between well-nourished and malnourished pre-dialysis patients, whereas the presence of inflammation was associated with lower serum albumin levels. Stenvinkel et al. [10] proposed two types of malnutrition: the first type is associated with poor nutritional intake due to the uraemic syndrome per se, whereas the other type often is associated with significant co-morbidity and inflammation. It has been suggested that the serum albumin level is more closely related to inflammation than to nutritional status [7] and may reflect the presence of systemic disease at the start of PD [20]. Thus, the subgroup of patients with low serum albumin concentrations, co-morbid diseases and malnutrition in this study may represent the second type of malnutrition.

In the present study, we found that co-morbid diseases exert a powerful influence on mortality in both malnourished and normally nourished PD patients. The risks for mortality in patients with malnutrition and co-morbid diseases and in patients with normal nutrition and co-morbid diseases were nine and five times, respectively, that of patients with normal nutrition and without co-morbid diseases. The risk in patients with malnutrition but without co-morbid diseases was 2.7-fold that of patients with normal nutrition without co-morbid diseases, but the difference did not reach statistical significance. It has been shown that malnutrition is associated with increased mortality [3,4]. The CANUSA study [3] showed that mortality increased with low serum albumin concentration and poor nutritional status. We have shown previously that malnutrition as assessed by SGA and FFEF body mass are independent risk factors for mortality [4]. However, it should be noted that the confounding effect of co-morbid diseases on mortality was not dissociated from that of malnutrition in the CANUSA study [3] or our previous study [4]. Furthermore, several lines of evidence suggest that nutritional status alone does not predict mortality [21]. Although malnutrition is correlated with increased mortality, this does not prove that better nutritional intake or improved nutritional status will reduce mortality [21]. On the other hand, co-morbid diseases are strongly associated with poor clinical outcome [16]. Thus, our observation that patients with both malnutrition and co-morbid diseases have higher mortality suggests that co-morbid diseases are strongly associated with both initial malnutrition and poor clinical outcome.

Although the relationship between peritoneal transport rate, nutritional status and co-morbid diseases in PD patients remains controversial, the present study shows that patients with malnutrition and co-morbid diseases have significantly higher initial D4/P4 creatinine compared with patients with malnutrition but without co-morbid diseases, and that initial D4/P4 creatinine is an independent predictor of mortality. This finding supports our previous study [22] and that of others [23] which demonstrated that high peritoneal transport rate is associated with co-morbid diseases and high mortality.

The present study includes a relatively small number of patients with malnutrition but without co-morbid diseases, and therefore a low number of events were analysed and we can therefore not exclude a type two error. We evaluated only the effect of initial status of nutrition and co-morbid diseases and cannot comment on the effect of any changes in the status of these factors that may have occurred over time.

In conclusion, this study demonstrates that there is a high prevalence of malnutrition and co-morbid diseases at the start of PD and that the combined presence of these conditions is associated with high mortality. Malnutrition in the absence of co-morbid diseases was associated with a statistically insignificant increase in mortality. Indeed, co-morbidity score was an independent predictor of mortality, but SGA score was not. High mortality in the malnourished PD patients is, therefore, largely related to co-morbid diseases rather than to malnutrition as such. However, in the present study, a relatively limited number of patients with malnutrition but without co-morbid diseases were analysed, and a type two error therefore cannot be excluded.

Conflict of interest statement. None declared.



   References
 Top
 Abstract
 Introduction
 Patients and methods
 Results
 Discussion
 References
 

  1. Paniagua R, Amato D, Vonesh E et al. Effects of increased peritoneal clearances on mortality rates in peritoneal dialysis: ADEMEX, a prospective, randomized, controlled trial. J Am Soc Nephrol 2002; 13: 1307–1320[Abstract/Free Full Text]
  2. Bergström J. Why are dialysis patients malnourished? Am J Kidney Dis 1995; 26: 229–241[ISI][Medline]
  3. CANADA-USA (CANUSA) Peritoneal Dialysis Study Group. Adequacy of dialysis and nutrition in continuous peritoneal dialysis: association with clinical outcome. J Am Soc Nephrol 1996; 7: 198–207[Abstract]
  4. Chung SH, Lindholm B, Lee HB. Influence of initial nutritional status on continuous ambulatory peritoneal dialysis patient survival. Perit Dial Int 2000; 20: 19–26[ISI][Medline]
  5. Bergström J. Nutrition and mortality in hemodialysis. J Am Soc Nephrol 1995; 6: 1329–1341[Abstract]
  6. Bergström J, Lindholm B. Malnutrition, cardiac disease, and mortality: an integrated point of view. Am J Kidney Dis 1998; 32: 834–841[ISI][Medline]
  7. Heimbürger O, Qureshi AR, Blaner WS, Berglund L, Stenvinkel P. Hand-grip muscle strength, lean body mass, and plasma proteins as markers of nutritional status in patients with chronic renal failure close to start of dialysis therapy. Am J Kidney Dis 2000; 36: 1213–1225[ISI][Medline]
  8. Foley RN, Parfrey PS, Harnett JD et al. Hypoalbuminemia, cardiac morbidity, and mortality in end-stage renal disease. J Am Soc Nephrol 1996; 7: 728–736[Abstract]
  9. Stenvinkel P, Lindholm B, Heimbürger O. New strategies for management of malnutrition in peritoneal dialysis patients. Perit Dial Int 2000; 20: 271–275[ISI][Medline]
  10. Stenvinkel P, Heimbürger O, Lindholm B, Kaysen GA, Bergström J. Are there two types of malnutrition in chronic renal failure? Evidence for relationships between malnutrition, inflammation and atherosclerosis (MIA syndrome). Nephrol Dial Transplant 2000; 15: 953–960[Free Full Text]
  11. Keshaviah PR, Nolph KD, Moore HL et al. Lean body mass estimation by creatinine kinetics. J Am Soc Nephrol 1994; 4: 1475–1485[Abstract]
  12. Forbes GB, Bruining GJ. Urinary creatinine excretion and lean body mass. Am J Clin Nutr 1976; 29: 1359–1366[Abstract]
  13. Metropolitan Life Insurance Company. Metropolitan height and weight tables. Stat Bull 64 1983; January–June
  14. Bergström J, Heimbürger O, Lindholm B. Calculation of the protein equivalent of total nitrogen appearance from urea appearance. Which formulas should be used? Perit Dial Int 1998; 18: 467–473[ISI][Medline]
  15. Watson PE, Watson ID, Batt RD. Total body water volumes for adult males and females estimated from simple antropometric measurements. Am J Clin Nutr 1980; 33: 27–39[Abstract]
  16. Davies SJ, Phillips L, Naish PF, Russell GI. Quantifying comorbidity in peritoneal dialysis patients and its relationship to other predictors of survival. Nephrol Dial Transplant 2002; 17: 1085–1092[Abstract/Free Full Text]
  17. Twardowski ZJ, Nolph KD, Khanna R et al. Peritoneal equilibration test. Perit Dial Bull 1987; 7: 138–147[ISI]
  18. Davies SJ, Russell L, Bryan J, Phillips L, Russell GI. Comorbidity, urea kinetics, and appetite in continuous ambulatory peritoneal dialysis patients: their interrelationship and prediction of survival. Am J Kidney Dis 1995; 26: 353–361[ISI][Medline]
  19. McCusker FX, Teehan BP, Thorpe KE, Keshaviah PR, Churchill DN. How much peritoneal dialysis is required for the maintenance of a good nutritional state? Canada–USA (CANUSA) Peritoneal Dialysis Study Group. Kidney Int Suppl 1996; 56: S56–S61[Medline]
  20. Struijk DG, Krediet RT, Koomen GC, Boeschoten EW, Arisz L. The effect of serum albumin at the start of continuous ambulatory peritoneal dialysis treatment on patient survival. Perit Dial Int 1994; 14: 121–126[ISI][Medline]
  21. Kopple JD. Pathophysiology of protein-energy wasting in chronic renal failure. J Nutr 1999; 129 [1S Suppl]: 247S–251S[Abstract/Free Full Text]
  22. Chung SH, Chu WS, Lee HA et al. Peritoneal transport characteristics, comorbid diseases and survival in CAPD patients. Perit Dial Int 2000; 20: 541–547[ISI][Medline]
  23. Cueto-Manzano AM, Correa-Rotter R. Is high peritoneal transport rate an independent risk factor for CAPD mortality? Kidney Int 2000; 57: 314–320[CrossRef][ISI][Medline]
Received for publication: 30. 8.02
Accepted in revised form: 27. 4.03