Patients receiving maintenance hemodialysis with low vs high levels of nutritional risk have decreased morbidity

Patients receiving maintenance hemodialysis with low vs high levels of nutritional risk have decreased morbidity

RESEARCH Current Research Continuing Education Questionnaire, page 665 Meets Learning Need Codes 3020, 5000, 5340, and 5390 Patients Receiving Maint...

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RESEARCH Current Research

Continuing Education Questionnaire, page 665 Meets Learning Need Codes 3020, 5000, 5340, and 5390

Patients Receiving Maintenance Hemodialysis with Low vs High Levels of Nutritional Risk Have Decreased Morbidity JERRILYNN D. BURROWES, PhD, RD; SHARRON DALTON, PhD, RD; JEFFREY BACKSTRAND, PhD; NATHAN W. LEVIN, MD

ABSTRACT Objective To compare the demographic and clinical characteristics and outcomes (morbidity) of 442 patients receiving maintenance hemodialysis who are at different levels of nutritional risk. Design A retrospective, longitudinal, chart review. Setting/Subjects An urban, outpatient hemodialysis unit in New York City. Subjects were stratified according to their number of nutritional risk factors: zero to one⫽low risk, two to three⫽moderate risk, four to six⫽high risk. Main Outcome Measures Mean values for serum albumin ⬍37 g/L, creatinine ⬍884 ␮mol/L, total cholesterol ⬍4.42 mmol/L, normalized protein nitrogen appearance ⬍0.9 g/kg/day, weight change ⬎ ⫺2.5 kg, and body mass index ⬍24. Morbidity indicators were frequency and duration of hospitalizations. Statistical Analyses Descriptive statistics, analysis of variance, and ␹2 analysis were used to summarize data and to analyze mean differences between the groups and differences in categorical variables, respectively. Results Compared with the high-risk group, the majority of subjects in the low-risk group were younger, male, and did not have diabetes; fewer had two or more comorbidiJ. D. Burrowes is an assistant professor in the Department of Nutrition, C.W. Post Campus of Long Island University, Brooksville, NY. S. Dalton is an associate professor in the Department of Nutrition and Food Studies, New York University, New York. J. Backstrand is an associate professor in the Joint PhD Program in Urban Systems, University of Medicine and Dentistry of New Jersey, Newark. N. W. Levin is medical director of the Renal Research Institute, New York, NY. Address correspondence to: Jerrilynn D. Burrowes, PhD, RD, Department of Nutrition, C.W. Post Campus of Long Island University, 720 Northern Blvd, Brooksville, NY 11548. E-mail: [email protected] Copyright © 2005 by the American Dietetic Association. 0002-8223/05/10504-0010$30.00/0 doi: 10.1016/j.jada.2005.01.010

© 2005 by the American Dietetic Association

ties. The high-risk group had 75% more hospitalizations and spent 195% more days in the hospital than the lowrisk group. Conclusions Declining values of the nutritional risk factors and higher hospitalization rates were present in the highrisk group. Older subjects, those with diabetes, and those with two or more comorbidities comprised the majority of the high-risk group. More aggressive nutrition counseling and interventions may be needed for high-risk group members to determine if their risk for morbidity could be reduced. J Am Diet Assoc. 2005;105:563-572.

P

rotein-energy malnutrition (PEM) is a risk factor for morbidity and mortality in persons receiving maintenance hemodialysis (MHD) (1,2). The prevalence of PEM varies from 18% to 75%, using either single or combined measures of protein-energy nutritional status (3-5). Various factors contribute to the development of PEM, including disturbances in protein-energy metabolism, reduced dietary energy and protein intakes, and amino acid losses. Comorbid medical conditions such as diabetes, cardiovascular disease, and chronic inflammation may also contribute to PEM. Clinical, anthropometric, and biochemical measures have been used to assess nutritional status in patients receiving MHD. Low or declining nutrition indicators in MHD patients (eg, biochemical measures such as serum albumin [SAlb], serum creatinine [SCr], total serum cholesterol, and normalized protein nitrogen appearance [nPNA] and anthropometric measurements such as body weight and body mass index [BMI; calculated as kg/m2]) have been associated with poor clinical outcome (ie, increased morbidity) (5-9). Documenting the characteristics of patients receiving dialysis with low or declining nutrition indicators will alert practitioners to provide interventions that may lead to actions that lower the risk of adverse health outcomes. Therefore, the purpose of this analysis was to compare the demographic and clinical characteristics of patients receiving MHD who are at different levels of nutritional risk, and to document the

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Diagnosis category

Examples of conditions

Cardiovascular disease Pulmonary disease Cerebrovascular disease Metabolic disorder Gastrointestinal disease Cancer

Congestive heart failure, angina, ischemia, valvular heart disease, hypertension, peripheral vascular disease Chronic obstructive pulmonary disease Stroke, dementia, sensory loss, degenerative diseases, depression Diabetes mellitus Liver disease, upper and lower gastrointestinal disorders Head and neck, prostate, genitourinary, lung, kidney, and gastrointestinal tract

Figure. Diagnostic categories of comorbid diseases that could be present in Beth Israel Medical Center, New York, NY maintenance hemodialysis patients.

relationship between the level of nutritional risk and morbidity. This study was unique in relating measures of morbidity to demographic and clinical characteristics of patients receiving MHD with different levels of nutritional risk based on predefined values of biochemical and anthropometric measurements. METHODS This analysis, a retrospective, longitudinal medical chart review, included 537 adult men and women who received MHD at the Beth Israel Medical Center Dialysis Treatment Center in New York City for 5 years. Exclusion criteria included diagnosis of severe liver disease (eg, liver cirrhosis or encephalopathy), acquired immunodeficiency syndrome, metastatic cancer, inadequate dialysis (defined as a mean single pool Kt/V of less than 1.0*) and receiving MHD for ⬍3 months. All subjects were followed until a censored event (ie, time of death, kidney transplantation, transfer to another dialysis facility, withdrawal from dialysis, or to the end of the study period). Data Collection The Beth Israel Medical Center Dialysis Treatment Center maintained an electronic patient statistical profile system that included select patient data such as date of birth, date of death, sex, primary cause of end-stage kidney disease, date of initial treatment for end-stage kidney disease, frequency of dialysis treatments, frequency and duration of hospitalization, date of kidney transplantation, change in treatment modality (ie, from hemodialysis to peritoneal dialysis), comorbid medical conditions such as diabetes, height and weight, and the results of routinely measured laboratory tests. The number of comorbid diseases (from zero to six) was determined from the following categories: cardiovascular disease, pulmonary disease, cerebrovascular disease, metabolic disorder, gastrointestinal disease, and cancer (see the Figure) (10). Each comorbid condition was assigned zero points if the condition was absent and one point if the condition was present. Points were totaled for each participant to obtain a comorbidity score (1).

*During the study period, adequate dialysis was defined as a single pool Kt/V of 1.0 or greater. Kt/V is a dimensionless measure of the dose of dialysis prescribed and/or delivered. Kt/V takes into account the efficiency of the dialyzer clearance (K), the treatment time (t), and the total volume of urea distribution in the body (V).

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SAlb, SCr, total serum cholesterol, nPNA, and BMI were obtained monthly and were analyzed over a period of 59 months. A participant could have from three to 59 measurements of each nutrition indicator for the entire study period, depending on the duration of follow-up per subject. The measurements were averaged from the start of the study to a censoring event or to the end of the study period. Blood samples were obtained before dialysis from each subject and sent to Damon Clinical Laboratory (New York, NY) for analysis. Protein nitrogen appearance was calculated from results of urea kinetic modeling using validated equations (11) and normalized to actual body weight (nPNA). Urea kinetic modeling was performed monthly at the time of the laboratory determinations. Subjects were weighed before and after each hemodialysis treatment on an electronic scale and the data were recorded in the database. The pre- and postdialysis weights, which were obtained on the date of the monthly blood tests, were used in the analysis. The postdialysis weight from each treatment was subtracted from the postdialysis weight at the start of the study, and the difference was used to determine the change in body weight throughout the study period. BMI was calculated using the postdialysis weight obtained on the date of the monthly blood test. Morbidity data were collected and analyzed for the study period. Mean frequency and duration of hospitalizations and hospitalization rates (ie, frequency of hospitalizations per patient-year at risk [also referred to as admissions per year] and duration of hospitalizations per patient-year at risk [also referred to as days per year]) were analyzed. Nutritional Risk Factors A nutritional risk factor was defined as a major, identifiable factor that may increase the risk of poor outcome (ie, morbidity) and, therefore, suggested the need for special care and attention (12). The nutritional risk factors identified were mean SAlb ⬍37 g/L, mean SCr ⬍884 ␮mol/L†, mean serum total cholesterol level ⬍4.42 mmol/L‡, mean †To convert ␮mol/L creatinine to mg/dL, multiply ␮mol/L by 0.0113. To convert mg/dL creatinine to ␮mol/L, multiply mg/dL by 88.40. Creatinine of 80 ␮mol/L⫽0.90 mg/dL. ‡To convert mmol/L cholesterol to mg/dL, multiply mmol/L by 38.7. To convert mg/dL cholesterol to mmol/L, multiply mg/dL by 0.026. Cholesterol of 5.00 mmol/L⫽193 mg/dL.

Table 1. Demographic and clinical characteristics of 442 patients receiving maintenance hemodialysis at Beth Israel Medical Center, New York, NY by level of nutritional risk Level of Nutritional Risk Variable Age (y) (meanⴞSD)b Men (%) Diabetes present (%) Comorbidity score (%) Zero One Two or more Years on dialysis (meanⴞSD) Dose of dialysis (Kt/Vd) (meanⴞSD) Postdialysis weight (kg) (meanⴞSD) Duration of follow-up (mo) (meanⴞSD)

All subjects (Nⴝ442)

Low (nⴝ133)

Moderate (nⴝ224)

High (nⴝ85)

P valuea

58.2⫾15.6 51.2 35.6

55.1⫾14.3 63.2 27.1

58.6⫾15.9 49.1 36.9

62.0⫾15.8 38.6 45.8

.005 .001 .017

20.4 34.2 45.4 1.46⫾3.0 (0.23, 0.29, 1.22)c

22.6 38.3 39.1 1.55⫾2.9 (0.23, 0.31, 1.78)c

18.5 37.4 44.1 1.55⫾3.1 (0.23, 0.28, 1.2)c

21.7 20.5 57.8 1.33⫾2.7 (0.22, 0.27, 0.73)c

.01 .53

1.38⫾0.27

1.33⫾0.27

1.40⫾0.27

1.41⫾0.27

.0001

68.7⫾16.5

75.5⫾16.4

66.0⫾15.3

62.3⫾15.4

.0001

22.9⫾15.8

23.7⫾17.2

22.6⫾15.8

21.4⫾14.1

.67

P values represent differences between groups using analysis of variance for continuous data and ␹ analysis for categorical data. P⬍.05 was considered statistically significant. SD⫽standard deviation. c Numbers in parentheses represent the 25th, 50th, and 75th percentiles. d During the study period, adequate dialysis was defined as a single pool Kt/V of 1.0 or greater. Kt/V is a dimensionless measure of the dose of dialysis prescribed and/or delivered. Kt/V takes into account the efficiency of the dialyzer clearance (K), the treatment time (t), and the total volume of area distribution in the body (V). a

2

b

nPNA ⬍0.9 g/kg/day, mean change in body weight from baseline ⬎–2.5 kg, and mean BMI ⬍24. Subjects were stratified into groups according to the number of nutritional risk factors present: zero to one nutritional risk factor was categorized as low risk, two to three risk factors as moderate risk, and four to six risk factors as high risk. The study was approved by the Institutional Review Board at Beth Israel Medical Center and by the Human Subjects Committee at New York University. Statistical Analysis Data were presented as mean⫾standard deviation for continuous variables and counts and percentages for categorical variables. Subgroup analyses were performed to identify groups with different trends in the relationship between the level of nutritional risk and morbidity. Statistical significance was determined using analysis of variance for continuous variables and ␹2 analysis for categorical variables. Quartiles were presented when the standard deviation was greater than or equal to the mean value (ie, weight change, years on dialysis, and frequency and duration of hospitalizations). Frequency and duration of hospitalizations were treated as continuous variables. Hospitalizations were also calculated as the frequency and duration per patient divided by the years at risk. P⬍.05 indicated statistical significance. Analyses were performed using the Statistical Program for the Social Sciences (Windows version 10, 2001, SPSS, Inc, Chicago, IL).

RESULTS Study Cohort A complete set of data was obtained from 442 subjects. Ninety-five subjects (18%) were excluded from the analysis for the following reasons: 76 were on dialysis ⬍3 months, nine had insufficient laboratory data, five were diagnosed with acquired immunodeficiency syndrome, three were diagnosed with severe liver disease, and two were diagnosed with metastatic cancer. Table 1 presents demographic and clinical characteristics of the study cohort. The mean age of the cohort was 58.2⫾15.6 years (age range 18 to 92 years); 51.2% were men, 35.6% had diabetes, and 45.4% had a comorbidity score of two or more. The average postdialysis weight was 68.7⫾16.5 kg. Mean years on dialysis, length of follow-up, and dose of dialysis (Kt/V)* were 1.46⫾3.0 years, 22.9⫾15.8 months, and 1.38⫾0.27, respectively. The mean values of the nutrition indicators for the cohort over the 59-month follow-up period are presented in Table 2. Mean SAlb level, SCr level, serum total cholesterol level, and BMI were at or slightly above the level defined as a nutrition risk factor; nPNA and change in body weight were higher. Hospitalization rates for the study cohort are also summarized in Table 2. The overall mean number of hospital admissions and days spent in the hospital per subject were 3.2⫾3.7 and 31.0⫾47.0, respectively. The overall hospitalization rates were 1.69 admissions per year and 16.23 days per year. Sixty percent of those hospitalized had two or more admissions compared with 23.5% who were never hospitalized and 16.5% who had one hospital admission (data not shown).

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Table 2. Mean laboratory valuesa of nutrition indicators and morbidity data by level of nutritional risk for 442 patients receiving maintenance hemodialysis at Beth Israel Medical Center, New York, NY Level of Nutritional Risk Variable Nutritional risk factor indicators Serum albumin (g/L) Serum creatinine (␮mol/L)c Total serum cholesterol (mmol/L)d Normalized protein nitrogen appearance (g/kg/day) Weight change (kg) Body mass indexf Morbidity indicators Frequency of hospitalizations per subject (admissions/year) Frequency of hospitalization per patient year at risk (admissions/year) Duration of hospitalizations per subject (days/year) Duration of hospitalizations per patient year at risk (days/year)

All subjects (Nⴝ442)

Low (nⴝ133)

Moderate (nⴝ224)

High (nⴝ85) 33.7⫾3.9 705.4⫾181.2 3.90⫾0.92

P valueb ⬍.0001 ⬍.0001 ⬍.0001

37.2⫾4.1 928.2⫾284.6 4.47⫾1.11

39.0⫾3.1 1,063.4⫾267.0 4.87⫾1.08

37.0⫾3.8 903.4⫾271.4 4.37⫾1.07

1.14⫾0.34 ⫹0.14⫾5.17 (⫺1.7, 0.1, 2.7)e 24.3⫾4.7

1.17⫾0.33 ⫹1.15⫾4.48 (⫺0.7, 0.8, 3.5)c 26.3⫾4.5

1.15⫾0.34 ⫺0.11⫾5.42 (⫺2.0, 0, 2.3)e 23.3⫾4.3

1.02⫾0.35 ⫺1.37⫾5.31 (⫺4.3, ⫺0.1, 1.7)e 23.1⫾5.2

3.23⫾3.69 (1, 2, 5)e

2.47⫾2.83 (0, 2, 4)e

3.27⫾3.60 (1, 2, 5)e

4.32⫾4.77 (1, 3, 6)e

1.69 31.01⫾47.0 (1, 14, 38.5)e

1.29 17.47⫾26.48 (0, 7, 27)e

1.71 31.41⫾45.64 (3, 15, 39)e

2.26 51.63⫾65.50 (6, 36, 71)e

⬍.0001

16.23

9.15

16.44

27.03

⬍.0001

⬍.0001 ⬍.0001 ⬍.0001 ⬍.001 ⬍.001

a

Values represent mean⫾standard deviation unless otherwise noted. P values represent differences between groups using analysis of variance. P⬍.05 was considered statistically significant. c To convert ␮mol/L creatinine to mg/dL, multiply ␮mol/L by 0.0113. To convert mg/dL creatinine to ␮mol/L, multiply mg/dL by 88.40. Creatinine of 80 ␮mol/L ⫽ 0.90 mg/dL. d To convert mmol/L cholesterol to mg/dL, multiply mmol/L by 38.7. To convert mg/dL cholesterol to mmol/L, multiply mg/dL by 0.026. Cholesterol of 5.00 mmol/L ⫽ 193 mg/dL. e Numbers in parenthesis represent the 25th, 50th, and 75th percentiles. f Calculated as kg/m2. b

Table 3 presents the nutrition indicators analyzed by sex, age (⬍65 years [hereafter referred to as younger], ⱖ65 years [hereafter referred to as older]), and diabetes status (absent, present). Overall, mean SAlb and SCr levels in women and in older subjects, serum total cholesterol level in men, and BMI in older subjects and in those without diabetes were below the levels defined as nutritional risk factors (⬍37 g/L, ⬍884 ␮mol/L†, ⬍4.42 mmol/L‡, and ⬍24, respectively). Similarly, the morbidity data were analyzed by sex, age, and diabetes status (see Table 4). Overall, men had significantly fewer hospital admissions per year compared with women (P⫽.04). Subjects without diabetes also had significantly fewer hospital admissions and fewer days hospitalized per year compared with those with diabetes (P⬍.0001 and P⬍.001, respectively). In addition, younger subjects spent fewer days in the hospital than older subjects. Following is a comparative analysis of the nutrition and morbidity indicators by the level of nutritional risk (low risk, moderate risk, and high risk) for the study cohort and by sex, age, and diabetes status. Characteristics of Subjects in the Low-Risk Group Thirty percent of the cohort (n⫽133) were in the lowrisk group (zero to one nutritional risk factor) (see Table 1). These persons were younger (mean age

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55.1⫾14.3 years), men (63.2%), did not have diabetes (72.9%), and 60.9% had a comorbidity score of zero or one. On average, subjects in the low-risk group received a larger dose of dialysis than subjects in the moderate-risk and high-risk groups, although the differences were not clinically significant. An inverse relationship was observed between the level of nutritional risk and postdialysis body weight; as the level of nutritional risk increased (from low risk to moderate risk to high risk), postdialysis body weight decreased significantly (from 75.5⫾16.4 kg to 66.0⫾15.3 kg to 62.3⫾15.4 kg; P⬍.0001), respectively. The mean values of the nutritional risk factors for subjects in the low-risk group were all higher than the mean values for the entire study cohort and also significantly higher than subjects in the moderate-risk and high-risk groups (P⬍.0001) (see Table 2). In addition, subjects in the low-risk group had fewer hospital admissions and spent fewer days in the hospital than the entire cohort and the other subgroups (see Table 2). The overall hospitalization rates for subjects in the low-risk group were 1.29 admissions per year and 9.15 days per year. Thirty percent of subjects were never hospitalized compared with 22% and 16% in the moderate-risk and highrisk groups, respectively (data not shown). Significantly higher SAlb, SCr, and nPNA were observed in men, in younger subjects, and in those with-

out diabetes compared with their counterparts (see Table 3). Women had significantly higher serum total cholesterol levels, changes in body weight, and BMIs compared with men. However, the largest change in body weight was observed in subjects with diabetes (⫹2.33 kg). There were no significant differences noted in the morbidity indicators between the sexes and between those with and without diabetes (see Table 4). However, despite being in the low-risk group, older subjects had significantly more hospital admissions and hospital days per year compared with younger subjects (P⫽.013 and P⬍.001, respectively). Characteristics of Subjects in the Moderate-Risk Group Fifty-one percent of the cohort (n⫽224) were in the moderate-risk group (two to three nutritional risk factors) (see Table 1). The mean age was 58.6⫾15.9 years; 49.1% were men, 63.1% did not have diabetes, and 55.9% had a comorbidity score of zero or one. The average weight of subjects in the moderate-risk group was approximately 10 kg less than subjects in the low-risk group and 3 kg less than the entire cohort. The mean values of the nutritional risk factors for the moderate-risk group were similar to the mean values for the entire cohort with the exception of BMI and serum total cholesterol, which were at a level defined as a nutritional risk factor (see Table 2). The overall hospitalization rates were similar to that of the entire study cohort: 1.71 admissions per year and 16.44 days per year. Sixtyone percent of those hospitalized in the moderate-risk group had two or more admissions compared with 22% who were never hospitalized and 17% who had one hospital admission (data not shown). In subgroup analysis, mean SAlb and SCr in women, in older subjects, and in those with diabetes, and mean serum total cholesterol in men, in younger subjects, and in those without diabetes were all below the level defined as a nutritional risk factor (⬍37 g/L, ⬍884 ␮mol/L†, and ⬍4.42 mmol/L‡, respectively) (see Table 3). BMI in all subgroups except those with diabetes were low (⬍24). No significant differences were noted in the morbidity indicators between the sexes and between the age groups (see Table 4). However, subjects with diabetes had significantly more hospital admissions and days hospitalized per year than those without diabetes (P⫽.012 and P⫽.016, respectively). Characteristics of Subjects in the High-Risk Group Nineteen percent of subjects (n⫽85) were in the high-risk group (four to six nutritional risk factors) (see Table 1). The mean age of this group was 62.0⫾15.8 years, an average of 7 years older than subjects in the low-risk group and 4 years older than the entire study cohort. The majority of subjects were women (61%); 45.8% had diabetes, and 57.8% had a comorbidity score of two or more. The average weight of the high-risk group was 62.3⫾15.4 kg, 13 kg less than the average weight of subjects in the low-risk group (which was primarily men and younger subjects) and 6 kg less than the entire cohort. On average, the high-risk group was older and had a higher percent-

age of subjects with diabetes and two or more comorbid conditions. Moreover, the mean values of the nutritional risk factors were significantly lower than the low-risk and moderate-risk groups, and also lower than the entire cohort (P⬍.0001) (see Table 2). However, nPNA and change in body weight did not reach the level defined as a nutritional risk factor (⬍0.9 g/kg/day and ⬍⫺2.5 kg, respectively). In fact, no group (low risk, moderate risk, or high risk) had nPNA or weight change levels that met the definition of a nutritional risk factor. The mean number of hospital admissions and days spent in the hospital per subject were significantly greater in the high-risk group than in the other groups (see Table 2). Subjects in the high-risk group had 75% more hospital admissions and spent 195% more days in the hospital than subjects in the low-risk group (see Table 2). The overall hospitalization rates for subjects in the high-risk group were 2.26 admissions per year and 27.03 days per year. The majority of subjects (70%) had two or more hospital admissions compared with 16% who were never hospitalized and 14% who had one hospital admission (data not shown). Mean SAlb, SCr, and serum total cholesterol were below the level defined as a nutritional risk factor in all subgroups whereas nPNA was above the risk factor level (Table 3). BMI was low in all subgroups except in those with diabetes. The largest change in body weight was observed in men in the high-risk group (⫺2.8 kg). No significant differences were noted in the morbidity indicators between the sexes and between the age groups (see Table 4). However, subjects with diabetes had significantly more hospital days per year than those without diabetes. DISCUSSION This study relates measures of morbidity to demographic and clinical characteristics of patients receiving MHD at different levels of nutritional risk based on predefined values of biochemical and anthropometric measurements. The results strongly suggest that patients receiving MHD at high risk are more likely to be older, have diabetes, have two or more comorbid medical conditions, and be hospitalized more frequently and longer than patients at low risk. In this analysis, 70% of the study cohort had multiple nutritional risk factors. This finding was also associated with an increased frequency and duration of hospitalizations and higher hospitalization rates. The groups with multiple risk factors (ie, moderate and high risk) also experienced more frequent and longer hospital admissions than subjects in the low-risk group. Previous studies have shown significant associations between nutritional status, as measured by biochemical and anthropometric indexes, and hospital admissions (13-15). The prognostic value of protein status in MHD is well established; SAlb and nPNA have been shown to be independent indicators of outcome in patients receiving MHD (6,16-18). In the study cohort, mean SAlb, a measure of visceral protein status, was ⬍37 g/L, and nPNA, an indirect measure of dietary protein intake in stable patients receiving MHD, was less than 1.2 g/kg/day. The hypoalbuminemia observed could be related to non-nutritional factors such as chronic infection or systemic in-

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Table 3. Nutritional risk factor indicators and the level of nutritional risk by sex, age, and diabetes status (mean⫾standard deviation) of 442 patients receiving maintenance hemodialysis at Beth Israel Medical Center, New York, NY Level of Nutritional Risk Nutritional risk factor indicators

Overall (Nⴝ442)

Serum albumin (g/L) Sex Male 37.7⫾4.2 Female 36.7⫾3.7 Age ⬍65 y 37.6⫾4.1 ⱖ65 y 36.5⫾3.7 Diabetes status Absent 37.6⫾4.1 Present 36.5⫾3.7 Serum creatinine (␮mol/L)b Sex Male 1,031.6⫾297.9 Female 821.2⫾223.6 Age ⬍65 y 1,008.6⫾293.5 ⱖ65 y 785.0⫾196.2 Diabetes status Absent 983.0⫾296.1 Present 825.6⫾226.3 Total serum cholesterol (mmol/L)c Sex Male 4.19⫾1.05 Female 4.76⫾1.11 Age ⬍65 y 4.43⫾1.14 ⱖ65 y 4.54⫾1.04 Diabetes status Absent 4.46⫾1.12 Present 4.50⫾1.08 Normalized protein nitrogen appearance (g/kg/day) Sex Male 1.18⫾0.33 Female 1.09⫾0.34 Age ⬍65 y 1.16⫾0.34 ⱖ65 y 1.11⫾0.34 Diabetes status Absent 1.16⫾0.34 Present 1.10⫾0.34 Weight change (kg) Sex Male ⫺0.10⫾5.22 (⫺1.9, 0, 2.4)d Female ⫹0.39⫾5.08 (⫺1.6, 0.2, 3.1)d

P valuea

Low (nⴝ133)

P valuea

Moderate (nⴝ224)

High P valuea (nⴝ85)

P valuea

⬍.0001

39.4⫾3.3 38.4⫾2.7

⬍.0001

37.4⫾3.9 36.7⫾3.7

⬍.0001

32.5⫾4.1 34.3⫾3.7

⬍.0001

⬍.0001

39.4⫾3.2 38.1⫾2.6

⬍.0001

37.2⫾4.0 36.7⫾3.6

⬍.0001

33.5⫾4.3 33.9⫾3.6

.052

⬍.0001

39.3⫾3.1 38.3⫾3.0

⬍.0001

37.2⫾4.1 36.8⫾3.3

⬍.0001

33.9⫾4.1 33.5⫾3.8

.057

⬍.0001

1,137.7⫾267.0 953.8⫾226.3

⬍.0001

1,003.3⫾297.0 803.5⫾198.9

⬍.0001

749.6⫾188.3 682.4⫾173.3

⬍.0001

⬍.0001

1,104.1⫾279.3 950.3⫾189.2

⬍.0001

992.7⫾286.4 756.7⫾160.0

⬍.0001

754.9⫾190.9 657.5⫾153.8

⬍.0001

⬍.0001

1,093.5⫾279.3 983.9⫾213.0

⬍.0001

959.1⫾289.1 802.7⫾201.5

⬍.0001

734.6⫾189.2 673.6⫾166.2

⬍.0001

⬍.0001

4.60⫾1.05 5.28⫾1.00

⬍.0001

4.00⫾0.97 4.74⫾1.04

⬍.0001

3.54⫾0.86 4.08⫾0.89

⬍.0001

⬍.0001

4.91⫾1.11 4.76⫾1.00

⬍.001

4.18⫾1.06 4.67⫾1.03

⬍.0001

3.91⫾0.98 3.88⫾0.85

.487

.041

4.89⫾1.12 4.83⫾0.99

.151

4.27⫾1.04 4.54⫾1.10

⬍.0001

3.87⫾0.92 3.93⫾0.90

.160

⬍.0001

1.22⫾0.32 1.10⫾0.32

⬍.0001

1.17⫾0.33 1.13⫾0.35

⬍.0001

1.11⫾0.41 0.98⫾0.31

⬍.0001

⬍.0001

1.19⫾0.34 1.11⫾0.28

⬍.0001

1.17⫾0.33 1.12⫾0.35

⬍.0001

0.99⫾0.34 1.05⫾0.36

.006

⬍.0001

1.18⫾0.33 1.14⫾0.33

.004

1.18⫾0.34 1.10⫾0.33

⬍.0001

1.01⫾0.32 1.04⫾0.38

.171

⬍.0001

⫹0.81⫾4.37 (⫺0.8, 0.5, 3.0)d ⫹1.63⫾4.60 (⫺0.4, 1.1, 4.2)d

⬍.0001

⫺0.32⫾5.58 (⫺2.1, 0, 2.2)d ⫹0.11⫾5.23 (⫺1.8, 0, 2.5)d

.012

⫺2.81⫾5.48 ⬍.0001 (⫺5.8, ⫺1.4, 0.5)d ⫺0.66⫾5.07 (⫺3.4, 0, 2.4)d (continued)

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April 2005 Volume 105 Number 4

Table 3. Nutritional risk factor indicators and the level of nutritional risk by sex, age, and diabetes status (mean⫾standard deviation) of 442 patients receiving maintenance hemodialysis at Beth Israel Medical Center, New York, NY (continued) Level of Nutritional Risk Nutritional risk factor indicators Weight change (kg) Age ⬍65 y ⱖ65 y Diabetes status Absent Present Body mass indexe Sex Male Female Age ⬍65 y ⱖ65 y Diabetes status Absent Present

Overall (Nⴝ442)

⫹0.20⫾4.90 (⫺1.6, 0.1, 2.5)d ⫹0.03⫾5.59 (⫺1.9, 0.1, 2.9)d

P valuea

.156

⫹0.33⫾4.49 ⬍.0001 (⫺1.5, 0.2, 2.4)d ⫺0.19⫾6.21 (⫺2.3, 0, 3.2)d

Low (nⴝ133)

P valuea

Moderate (nⴝ224)

High P valuea (nⴝ85)

P valuea

⫹1.07⫾4.75 (⫺0.75, 0.7, 3.4)d ⫹1.37⫾3.67 (⫺0.4, 1.1, 3.8)d

.131

⫹0.05⫾4.80 (⫺1.8, 0, 2.2)d ⫺0.37⫾6.28 (⫺2.1, 0, 2.5)d

.015

⫺2.14⫾5.16 ⬍.001 (⫺4.5, ⫺0.8, 0.9)d ⫺0.60⫾5.35 (⫺3.9, 0, 3.2)d

⫹0.69⫾4.43 (⫺0.9, 0.5, 2.8)d ⫹2.33⫾4.41 (⫺0.1, 2.2, 4.9)d

⬍.0001

⫹0.31⫾4.45 (⫺1.6, 0, 2.3)d ⫺0.87⫾6.77 (⫺2.9, 0, 2.6)d

⬍.0001

⫺0.78⫾4.86 ⬍.0001 (⫺3.8, 0, 2.3)d ⫺2.06⫾5.71 (⫺5.0, ⫺0.9, 0.9)d

24.2⫾3.8 24.4⫾5.6

.013

25.8⫾3.6 27.1⫾5.4

⬍.0001

23.2⫾3.0 23.5⫾5.3

.017

23.0⫾5.5 23.1⫾5.1

.614

24.5⫾4.9 23.9⫾4.4

⬍.0001

26.5⫾4.9 25.8⫾3.2

⬍.0001

23.2⫾4.2 23.5⫾4.5

.015

23.5⫾5.6 22.7⫾4.8

.012

23.6⫾4.5 25.6⫾5.0

⬍.0001

26.1⫾4.7 27.0⫾3.8

⬍.0001

22.3⫾3.5 25.1⫾4.9

⬍.0001

21.4⫾3.6 25.0⫾6.1

⬍.0001

a

P values represent differences between groups using analysis of variance. P⬍.05 was considered statistically significant. To convert ␮mol/L creatinine to mg/dL, multiply ␮mol/L by 0.0113. To convert mg/dL creatinine to ␮mol/L, multiply mg/dL by 88.40. Creatinine of 80 ␮mol/L⫽0.90 mg/dL. c To convert mmol/L cholesterol to mg/dL, multiply mmol/L by 38.7. To convert mg/dL cholesterol to mmol/L, multiply mg/dL by 0.026. Cholesterol of 5.00 mmol/L⫽193 mg/dL. d Numbers in parenthesis represent the 25th, 50th, and 75th percentiles. e Calculated as kg/m2. b

flammation, or hydration status (19-23); however, one of the limitations of our study is that measures of systemic inflammation such as C-reactive protein were not collected during the study period. In a recent study involving a cohort of patients with severe hypoalbuminemia (SAlb ⬍32 g/L) and age- and sex-matched patients receiving MHD with normoalbuminemia (SAlb ⱖ40 g/L), elevated serum concentrations of C-reactive protein and plasma interleukin-6 levels (biomarkers of chronic inflammation) were observed in the hypoalbuminemia group. These results support the inverse association between SAlb level and acute phase inflammatory response. Systemic inflammation may also have a significant impact on serum total cholesterol levels in patients receiving MHD. A recent prospective study of 823 patients receiving hemodialysis reported that hypocholesterolemia was a significant risk factor for mortality in the presence of systemic inflammation and/or malnutrition. However, in the absence of inflammation and/or malnutrition, a strong, positive association of serum total cholesterol level and mortality was observed (24). Overhydration is common in patients receiving MHD and hemodilution may contribute to the hypoalbuminemia observed in this cohort because most dialysis facilities obtain serum chemistries predialysis. In our study, all blood work was obtained predialysis. Although some

studies have shown increases in SAlb postdialysis (21,22), the study by Kaysen and colleagues (23) demonstrated that hydration status is not a cause of large differences in SAlb concentration pre- and postdialysis. In fact, postdialysis albumin levels decrease toward their predialysis values within 30 minutes of the end of dialysis. Subjects in the low-risk group appeared to have a better nutritional status than those in the moderate-risk and high-risk groups (see Table 2). They had significantly higher mean levels of SAlb, SCr, serum total cholesterol, nPNA, and higher mean BMI, and had a more positive change in body weight than subjects in the moderate-risk and high-risk groups. These results were maintained across the subgroups (see Table 3). Striking differences were also observed between the groups and morbidity. A lower mean frequency and duration of hospitalizations were observed in the low-risk group compared with the moderate-risk and high-risk groups; this was also true of the subgroups (see Table 4). However, the cause of hospitalizations was unknown, although it may have provided invaluable information about the differences in morbidity between the groups. Underdialysis has been shown to influence outcome in patients receiving MHD (25,26). This did not appear to be the case in this study because the mean Kt/V* of the study cohort was 1.38⫾0.27, an amount considered adequate at the time of the study (27,28). In fact, the mod-

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570

Table 4. Morbidity indicators and the level of nutritional risk by sex, age, and diabetes status of 442 patients receiving maintenance hemodialysis at Beth Israel Medical Center, New York, NYa

April 2005 Volume 105 Number 4

Level of Nutritional Risk Morbidity indicators Frequency of hospitalizations per subject (admissions/y) Sex Male Female Age ⬍65 y ⱖ65 y Diabetes status Absent Present Frequency of hospitalizations per patient-year at risk (admissions/y) Sex Male Female Age ⬍65 y ⱖ65 y Diabetes status Absent Present Duration of hospitalizations per subject (days/y) Sex Male Female Age ⬍65 y ⱖ65 y Diabetes status Absent Present Duration of hospitalizations per patient-year at risk (days/y) Sex Male Female Age ⬍65 y ⱖ65 y Diabetes status Absent Present a

P valueb

Low (nⴝ133)

P valueb

Moderate (nⴝ224)

P valueb

High (nⴝ85)

P valueb

2.88⫾3.71 (0, 2, 4) 3.59⫾3.62 (1, 3, 5)c

.040

2.20⫾2.69 (0, 1.5, 3.0)c 2.94⫾3.03 (0, 2, 4)c

.149

3.04⫾3.70 (0, 2, 4)c 3.50⫾3.50 (1, 2, 5)c

.334

4.12⫾5.48 (1, 3, 5.7)c 4.45⫾4.32 (1, 3, 6)c

.764

2.99⫾3.91 (0, 2, 4)c 3.60⫾3.26 (1, 3, 5.2)c

.087

2.10⫾2.56 (0, 1, 3)c 3.47⫾3.30 (0.25, 3, 5)c

.013

3.11⫾3.89 (0, 2, 4)c 3.52⫾3.14 (1, 3, 5)c

.405

4.77⫾5.82 (1, 3, 6)c 3.91⫾3.54 (1, 3, 6)c

.411

2.26⫾2.84 (0, 1, 4)c 3.05⫾2.77 (0.25, 3, 4.7)c

.150

2.81⫾3.54 (0, 2, 4)c 4.06⫾3.58 (1, 3, 6)c

.012

3.82⫾4.97 (1, 3, 5.5)c 4.92⫾4.52 (2, 3.5, 6.2)c

.299

2.78⫾3.60 (0, 2, 4)c 4.02⫾3.70 (1, 3, 6)c

⬍.0001

1.51 1.88

.040

1.15 1.54

.149

1.47 2.16

.334

2.16 2.33

.764

1.56 1.88

.087

1.10 1.82

.013

1.63 1.84

.405

2.50 2.05

.411

1.45 2.10

⬍.0001

1.18 1.60

.150

1.47 2.12

.012

2.00 2.57

.299

26.80⫾42.31 (0, 11, 36)c 35.24⫾50.91 (4, 18, 40)c

.058

14.67⫾22.20 (0, 5.5, 22.5)c 22.29⫾32.21 (0, 12, 30.5)c

27.09⫾47.60 (0, 11, 32)c 37.02⫾45.07 (6, 22.5, 51.2)c

.030

12.99⫾19.83 (0, 5, 23)c 29.56⫾36.87 (0.75, 15.5, 49.5)c

⬍.001

⬍.001

15.67⫾26.09 (0, 6, 22.5)c 22.33⫾27.27 (0.25, 11.5, 31.7)c

24.21⫾38.30 (0, 9, 30)c 43.04⫾57.41 (5.5, 28, 53)c

29.75⫾42.82 (0, 13, 41.5)c 33.02⫾48.33 (5, 16, 37.5)c

.595

49.28⫾65.69 (5, 35, 62.7)c 53.10⫾65.99 (6, 36, 77)c

.798

28.74⫾48.56 (0, 11, 35)c 35.26⫾41.03 (6, 22, 50)c

.296

56.63⫾73.78 (6.5, 39, 76.7)c 46.98⫾57.25 (6, 33, 55)c

.506

.198

25.79⫾41.47 (0, 10.5, 29)c 41.01⫾50.83 (7.7, 28.5, 56)c

.016

38.33⫾46.22 (2.5, 25, 53.5)c 67.37⫾80.62 (14, 40, 88.2)c

.044

.110

14.03 18.45

.058

7.68 11.67

.110

15.57 17.29

.595

25.80 27.80

.798

14.18 19.38

.030

6.80 15.48

⬍.001

15.05 18.46

.296

29.64 24.60

.506

12.67 22.53

⬍.001

8.20 11.69

.198

13.50 21.47

.016

20.07 35.27

.044

Values represent mean⫾standard deviation unless otherwise noted. P values represent differences between groups using analysis of variance. P⬍.05 was considered statistically significant. Numbers in parenthesis represent the 25th, 50th, and 75th percentiles.

b c

Overall (Nⴝ442)

erate-risk and high-risk groups received a slightly higher yet clinically insignificant dose of dialysis than the lowrisk group. Therefore, inadequate dialysis was not a factor in the relationship between the level of nutritional risk and morbidity. Overall, the nutrition indicators were significantly lower in the high-risk group compared with the low-risk and moderate-risk groups and in subgroup analyses. nPNA remained greater than the level defined as a nutritional risk factor in all three groups (and subgroups), which suggests the presence of factors (especially in the high-risk group) that increase net protein degradation and produce elevated levels of nPNA, such as chronic inflammation, infection, or catabolic illnesses. Therefore, the level of nPNA observed in the high-risk group and in the subgroups may have been misleadingly high. A definite trend of higher hospitalization rates was observed with increasing level of nutrition risk (from low risk to moderate risk to high risk) in the univariate analysis, which seems to indicate that morbid events occurred more frequently in patients with multiple nutritional risk factors. In light of this finding, it seems prudent to monitor closely and to intervene with appropriate interventions in those patients with low or declining nutritional risk factors. Accordingly, patients with low levels of SAlb, SCr, serum total cholesterol, nPNA, and BMI, and those with weight loss, should be considered at nutritional risk and be treated with aggressive nutrition counseling and interventions, although there are no conclusive data on effective treatment regimens for PEM in the dialysis population. Evidence is sparse showing that improvement in protein-energy nutritional status or in measures of nutritional status will reduce morbidity rates in patients receiving MHD. The strengths of this analysis include the large sample size (N⫽442), the length of follow-up (59 months, mean 22.9⫾15.8 months), the repeated measurements of the nutrition indicators (monthly), and the large number of hospital admissions (1,423) and days hospitalized (13,634). On the other hand, this analysis had limitations. First, the methodology of a retrospective design limits the generalizability of the results to the general dialysis population and permits only inferences about the results. Second, information about race and ethnicity, socioeconomic status, lifestyles, and habits (eg, history of smoking and alcohol use) were not available. This type of information may be a confounding factor in the analysis; therefore, future research in this area should include these variables in the analyses. Third, as mentioned previously, measures of chronic inflammation that may influence SAlb, serum total cholesterol, and nPNA were not available. The prevalence of inflammation in patients receiving MHD is high, as reflected in frequently elevated concentrations of C-reactive protein (20,29). Lastly, cause of hospitalization was not available; it may have provided invaluable information about the groups. CONCLUSIONS Patients receiving MHD who are at high nutritional risk have more frequent and longer hospital admissions compared with those at low and moderate nutritional risk. Older subjects and those with diabetes and other comor-

bid diseases comprise the majority of subjects at high nutritional risk. Therefore: ●





Aggressive nutrition counseling and interventions should be instituted for high-risk patients receiving MHD to reduce their risk of morbidity. These interventions include, but are not limited to, ensuring adequate dietary protein and energy intake on a daily basis through the use of foods and/or oral supplements. A recent study in a large cohort of patients receiving MHD (N⫽1,901) showed that they consume significantly less protein and energy and frequently report poor or very poor appetites on dialysis treatment days compared with nondialysis treatment days (30). Other authors have reported a decline in nutrient intake during the interdialytic interval (31). Small, sustained differences in dietary intake may be important, especially in a population where intakes are likely to be hypocaloric. Frequent monitoring of dietary intake with the use of diaries and/or interviews will provide quantitative information about protein, energy, and other nutrients with the goal of optimizing intake. Because the incident MHD population is aging (ie, median age in 1978 was 54 years compared with 65 years in 2001, with the rate of highest increase in those aged ⱖ75 years of age [32]), attention needs to be focused on the chronic conditions that affect the general older population, such as arthritis and periodontal disease. In 1996, the Administration on Aging reported that about 50% of older Americans had arthritis (33). Another report noted that almost 40% of older Americans have periodontal disease (34). These chronic inflammatory conditions, which are known to affect biomarkers of nutritional status such as SAlb, need to be assessed and monitored in older patients receiving MHD. Further research is needed to determine whether interventions that maintain or improve the level of the nutrition indicators will reduce subsequent morbidity and, hence, improve health outcome for patients receiving MHD.

References 1. Marcen R, Teruel JL, de la Cal MA, Gamez C, and the Spanish Cooperative Study of Nutrition in Hemodialysis. The impact of malnutrition in morbidity and mortality in stable hemodialysis patients. Nephrol Dial Transplant. 1997;12:2324-2331. 2. Leavey SF, Strawderman RL, Jones CA, Port FK, Held PJ. Simple nutritional indicators as independent predictors of mortality in hemodialysis patients. Am J Kidney Dis. 1998;31:997-1006. 3. Aparicio M, Cano N, Chauveau P, Azar R, Canaud B, Flory A. Nutritional status of hemodialysis patients: A French national cooperative study. Nephrol Dial Transplant. 1999;14:1679-1686. 4. Herselman M, Moosa MR, Kotze TJ, Kritzinger M, Wuister S, Mostert D. Protein-energy malnutrition as a risk factor for increased morbidity in long-term hemodialysis patients. J Ren Nutr. 2000;10:7-15. 5. Kalantar-Zadeh K, Kopple JD. Relative contributions of nutrition and inflammation to clinical outcome in dialysis patients. Am J Kidney Dis. 2001;38:13431350.

April 2005 ● Journal of the AMERICAN DIETETIC ASSOCIATION

571

6. Lowrie EG, Lew NL. Death risk in hemodialysis patients: The predictive value of commonly measured variables and an evaluation of death rate differences between facilities. Am J Kidney Dis. 1990;15:458-482. 7. Kopple JD, Zhu X, Lew NL, Lowrie EG. Body weightfor-height relationships predict mortality in maintenance hemodialysis patients. Kidney Int. 1999;56: 1136-1148. 8. Kopple JD. Nutritional status as a predictor of morbidity and mortality in maintenance dialysis patients. ASAIO J. 1997;43:246-250. 9. Kalantar-Zadeh K, Supasyndh O, Lehn RS, McAllister CJ, Kopple JD. Normalized protein nitrogen appearance is correlated with hospitalization and mortality in hemodialysis patients with Kt/V greater than 1.20. J Ren Nutr. 2003;13:15-25. 10. Sullivan DH, Patch GA, Walls RC, Lipschitz DA. Impact of nutrition status on morbidity and mortality in a select population of geriatric rehabilitation patients. Am J Clin Nutr. 1990;51:749-758. 11. Sargent JA. Control of dialysis by a single pool urea model: The National Cooperative Dialysis Study. Kidney Int. 1983;(suppl 13):S19-S25. 12. Davies L, Knutson KC. Warning signals for malnutrition in the elderly. J Am Diet Assoc. 1991;91:14131417. 13. Jones KR. Factors associated with hospitalization in a sample of chronic hemodialysis patients. Health Service Res. 1991;26:671-699. 14. Rocco MV, Soucie MJ, Reboussin DM, McClellan WM. Risk factors for hospital utilization in chronic dialysis patients. J Am Soc Nephrol. 1996;7:889-896. 15. Kalantar-Zadeh K, Kopple JD, Block G, Humphreys MH. A malnutrition-inflammation score is correlated with morbidity and mortality in hemodialysis patients. Am J Kidney Dis. 2001;38:1251-1263. 16. National Kidney Foundation. K/DOQI clinical practice guidelines for nutrition in chronic renal failure. Am J Kidney Dis. 2000;35(suppl 6):S1-S104. 17. Goldwasser P, Mittman N, Antignani A, Burrell D, Michel MA, Collier J. Predictors of mortality in hemodialysis patients. J Am Soc Nephrol. 1993;3:16131622. 18. Owen WF, Lew NL, Liu Y, Lowrie EG, Lazarus JM. The urea reduction ratio and serum albumin concentration as predictors of mortality in patients undergoing hemodialysis. N Engl J Med. 1993;329:10011006. 19. Kaysen GA, Stevenson FT, Depner TA. Determinants of albumin concentration in hemodialysis patients. Am J Kidney Dis. 1997;29:658-668. 20. Danielski M, Ikizler TA, McMonagle E, Kane JC, Pupim L, Morrow J, Himmelfarb J. Linkage of hypoalbuminemia, inflammation, and oxidative stress in patients receiving maintenance hemodialysis therapy. Am J Kidney Dis. 2003;42:286-294. 21. Jones CH, Akbani H, Croft DC, Worth DP. The relationship between serum albumin and hydration sta-

572

April 2005 Volume 105 Number 4

22. 23.

24.

25.

26.

27. 28. 29.

30.

31.

32.

33.

34.

tus in hemodialysis patients. J Ren Nutr. 2002;12: 209-212. Dumler F. Hypoalbuminemia is a marker of overhydration in chronic maintenance patients on dialysis. ASAIO J. 2003;49:282-286. Kaysen GA, Dubin JA, Hans-Georg M, Mitch WE, Rosales LM, Levin NW, and the HEMO Study Group. Relationships among inflammation nutrition and physiologic mechanisms establishing albumin levels in hemodialysis patients. Kidney Int. 2002;61:22402249. Liu Y, Coresh J, Eustace JA, Longenecker JC, Jaar B, Fink NE, Tracy RP, Powe NR, Klag MJ. Association between cholesterol level and mortality in dialysis patients: Role of inflammation and malnutrition. JAMA. 2004;291:451-459. Lindsay RM, Heidenheim AP, Spanner E, Kortas C, Blake PG. Adequacy of hemodialysis and nutrition: Important determinants of morbidity and mortality. Kidney Int. 1994;45(suppl 4):S85-S91. Maiorca R, Brunori G, Zubani R, Cancarini GC, Manili L, Camerini C. Predictive value of dialysis adequacy and nutritional indices for mortality and morbidity in CAPD and HD patients. A longitudinal study. Nephrol Dial Transplant. 1995;10:2295-2305. Hakim RM, Breyer J, Ismail N, Schulman G. Effects of dose of dialysis on morbidity and mortality. Am J Kidney Dis. 1994;23:661-669. Held PJ, Levin NW, Bovbjerg RR, Pauly MV, Diamond LH. Mortality and duration of hemodialysis treatment. JAMA. 1991;265:871-875. Zimmerman J, Herlinger S, Pruy, A, Metzger T, Wanner C. Inflammation enhances cardiovascular risk and mortality in hemodialysis patients. Kidney Int. 1999;55:648-658. Burrowes JD, Larive B, Cockram DB, Dwyer J, Kusek JW, McLeroy S, Poole D, Rocco MV, and the Hemodialysis (HEMO) Study Group. Effects of dietary intake, appetite, and eating habits on dialysis and non-dialysis treatment days in hemodialysis patients: Cross-sectional results from the HEMO Study. J Ren Nutr. 2003;13:191-198. Bellizzi V, DiIorio BR, Terraccuabi V, Minutolo R, Iodice C, DeNicola L, Conte G. Daily nutrient intake represents a modifiable determinant of nutritional status in chronic haemodialysis patients. Nephrol Dial Transplant. 2003;18:1874-1881. United States Renal Data System: USRDS 2001 annual data report. Atlas of end stage renal disease in the United States. Bethesda, MD. Available at: http:// www.usrds.org. Accessed May 11, 2004. Administration on Aging. A profile of older Americans: 2002. Department of Health and Human Services, Washington, DC. Available at: http://www.aoa.gov/prof/ statistics/profile/12.asp. Accessed May 11, 2004. Vargas CM, Kramarow EA, Yellowitz JA. The oral health of older Americans. In: National Center for Health Statistics. Aging Trends. Hyattsville, MD: National Center for Health Statistics; 2001. No. 3.