Abstract
Objective
Markers of better nutritional status including both higher levels of serum albumin (as a measure of visceral proteins) and creatinine (as a measure of the muscle mass) are associated with lower mortality in conventional (thrice-weekly) hemodialysis patients. However, data for these associations in twice-weekly hemodialysis patients, in whom less frequent hemodialysis may confound nutritional predictors, are lacking.
Design, Settings and Subjects
We identified 1,113 twice-weekly and matched 4,448 thrice-weekly hemodialysis patients from a large national dialysis cohort of incident hemodialysis patients over 5 years (2007-2011). Mortality risk, adjusted for potential confounders, was examined across two-by-two combinations of serum creatinine (<6 mg/dl vs. ≥6 mg/dl) and albumin (<3.5 g/dl vs. ≥3.5 g/dl) for each treatment frequency yielding a total of eight groups.
Results
Patients were 70±14 years old and included 48% women, and 55% diabetics. Using the thrice-weekly hemodialysis patients with creatinine≥6mg/dl and albumin≥3.5g/dl as reference, patients with creatinine<6mg/dl and albumin<3.5g/dl had a 1.8-fold higher risk of mortality (HR: 1.75, 95%CI: 1.33-2.30) in twice-weekly and 2.2-fold increased risk of mortality (HR: 2.21, 95%CI: 1.81-2.70) in thrice-weekly hemodialysis patients, respectively in fully adjusted models adjusted for demographics, comorbidities and markers of malnutrition and inflammation. A test for interaction showed there was no significant difference in albumin creatinine mortality associations between twice-weekly and thrice-weekly hemodialysis patients (p-for-interaction 0.7667).
Conclusions
Surrogate markers of higher visceral protein and muscle mass combined may confer greatest survival in both twice-weekly and thrice-weekly hemodialysis patients.
Keywords: Creatinine, albumin, mortality, survival advantage, twice-weekly, hemodialysis
Introduction
Chronic kidney disease and end- stage renal disease (ESRD) are recognized as global public health problems (1,2). Over 400,000 people in the United States receive hemodialysis treatment for ESRD, among whom approximately 100,000 initiate hemodialysis each year (3). In ESRD patients, serum creatinine may be used as a surrogate of muscle mass (4-11) and higher serum creatinine level has been found to be associated with better survival (11-14). Serum albumin, as a measure of visceral proteins, is also a powerful predictor of outcomes in patients with ESRD (15). However, most previous studies on these factors had largely focused on patients using a thrice-weekly hemodialysis regimen. Notably, recent data has found that “infrequent dialysis” or twice-weekly regimens does not necessarily confer greater mortality risk when compared to thrice-weekly therapy (16,17). Although the survival differences associated with twice vs. thrice-weekly hemodialysis may be related to patient selection bias (16), patients on a less frequent hemodialysis regimen are also more likely to have better nutritional status, have higher serum albumin values, and possibly lower muscle mass based on serum creatinine values. Few studies have compared the clinical outcome in twice-weekly versus thrice-weekly hemodialysis patients according to concurrent nutritional status of both visceral protein and muscle mass. We hypothesized that survival advantages of higher serum albumin and creatinine combined will hold in twice-weekly hemodialysis patients.
Materials and Methods
Patients
We extracted and examined data from all patients with ESRD who received dialysis therapy from January, 2007 to December, 2011 in a large dialysis care organization in the United States (XXX Inc.). The study was approved by the institutional review committees of the XXX. Given the large sample size, anonymity of the patients studied, and nonintrusive nature of the research, the requirement for written consent was waived. During the study period, a total of 208,820 patients initiated dialysis. We excluded 46,149 patients in whom dialysis duration lasted less than 60 days total. Among the remaining 162,671 patients, we excluded 24,944 patients who had missing treatment information and 7,871 patients with missing creatinine or albumin during the first 91 days of dialysis (baseline). An additional 563 patients were excluded for missing censoring information. We then identified 1,123 twice-weekly and 117,625 thrice-weekly hemodialysis patients during the baseline quarter. We 1:4 matched twice-weekly to thrice-weekly hemodialysis patients by age, gender, race/ethnicity, diabetes status, and facility region. The final matched study cohort consisted of 1,113 twice-weekly and 4,448 thrice-weekly hemodialysis patients (Figure 1).
Figure 1.
Cohort Construction
Demographic and Clinical Measures
All data were obtained from electronic medical records at XXX. The following comorbid conditions were considered: diabetes mellitus, hypertension, atherosclerotic heart disease, congestive heart failure, cerebrovascular disease, other cardiovascular disease, chronic obstructive pulmonary disease, history of cancer, and alcohol abuse. Race and ethnicity determinations were based on self-identification of the race and ethnicity with which they most closely identified, according to the definitions set forth by the US Census Bureau and the Federal Office of Management and Budget (18,19).
Laboratory Values
In all XXX dialysis clinics, blood samples were drawn using standardized techniques and were transported to a central laboratory in XXX, typically within 24 hours, where measurements were made using automated and standardized methods. Serum creatinine, phosphorus, calcium, urea, albumin, bicarbonate and total iron binding capacity were measured monthly. Serum parathyroid hormone and ferritin were measured at least quarterly. Hemoglobin was measured weekly to bi-weekly in most patients. Delivered dialysis dose was estimated with single-pooled Kt/V using the urea kinetic model. Body mass index was calculated as post-dialysis body weight in kilogram divided by height in meter squared (kg/m2). Baseline laboratory measurements were averaged during the first 3 months (91 days) of dialysis treatment to attenuate an effect of short-term variation in laboratory measurements. We used a validated creatinine-based formula to estimate baseline lean body mass in all hemodialysis patients (20). The formula is as follows: estimated lean body mass (kg) =0.34*Serum Creatinine (mg/dl) +5.58*gender (1 if female; 0 if male) + 0.30*weight (kg) +0.67*height (inch)−0.23*Urea reduction ratio −5.75.
Patients were divided into four categories based on baseline measurements: serum creatinine < 6 mg/dl and albumin < 3.5 g/dl; serum creatinine ≥ 6.0 mg/dl and albumin < 3.5 mg/dl, serum creatinine < 6.0 mg/dl and albumin ≥ 3.5 g/dl and serum creatinine ≥ 6.0 mg/dl and albumin ≥ 3.5 g/dl. Patients with highest serum creatinine and albumin were considered as the reference group.
Statistical Methods
Descriptive data were summarized using proportions, mean (±standard deviation, SD) and medians (interquartile range, IQR) as appropriate. Data across groups were compared using ANOVA, Kruskal-Wallis or chi-square tests where appropriate. We analyzed the relationship between all-cause mortality and baseline concurrent serum creatinine and albumin, using the Cox proportional hazard models. Patients were followed from patients first date of dialysis until death or censoring due to renal transplantation, transfer to another dialysis facility, or end of the study period (December 31, 2011).
Three models of analyses were examined: 1) an unadjusted model that included only the categorical groupings of serum creatinine and albumin, the main predictor variable and calendar quarter of entry; 2) case-mix adjusted models that additionally included age, gender, race/ethnicity (non-Hispanic white, African Americans, Hispanic, Asian and others), previously listed comorbidities and primary insurance; and 3) case-mix plus malnutrition-inflammation complex syndrome (MICS) adjusted models which included all of the covariates in the case-mix model as well as 11-surrogates of nutritional and inflammatory status: body mass index, hemoglobin, serum levels of calcium, phosphorus, parathyroid hormone, potassium, ferritin, iron saturation ratio, total iron binding capacity, bicarbonate and peripheral white blood cell count. P-for-interaction was tested using Wald's test. In sensitivity analyses, associations between creatinine albumin groups with mortality were evaluated in a matched cohort, which was additionally matched for creatinine albumin exposure category groups. All analyses were carried out with SAS version 9.4 (SAS Institute, Inc., Cary, North Carolina). Matching procedures were completed using SAS (SAS Institute Inc, www.sas.com) macro Gmatch based on the greedy algorithm created by Bergstralh and Kosanke (21). Missing covariate data (under 1% for most laboratory and demographic variables) were imputed by means or medians of recorded values.
Results
Baseline demographics and clinical characteristics
The study cohort included a total of 5,561 twice and thrice-weekly hemodialysis patients after matching. Baseline characteristics stratified by each concurrent serum level of creatinine and albumin category are presented in Table 1. The mean (± SD) age was 70±14 years old and 48% were women. At baseline, 55% of hemodialysis patients had diabetes mellitus and 49% had hypertension. The mean body weight, estimated lean body mass and body mass index in 5,561 hemodialysis patients was 76±21 kg, 64±7 kg and 27±7 kg/m2, respectively. Patients with higher concurrent levels of serum creatinine and albumin were younger, male, and had higher body weight, estimated lean body mass, body mass index, serum levels of phosphorus, parathyroid hormone, and a higher prevalence of hypertension and lower prevalence of diabetes mellitus, congestive heart failure and chronic obstructive pulmonary disease.
Table 1.
Baseline characteristics stratified by concurrence of serum creatinine and albumin in total after matched hemodialysis patients (n=5,561)
| Variables | After Match Hemodialysis Patients |
|||||
|---|---|---|---|---|---|---|
| Total | Cr<6.0&ALB<3.5Cr≥6.0&ALB<3.5Cr<6.0&ALB≥3.5 Cr≥6.0&ALB≥3.5 | P value | ||||
| N | 5,561 | 1,731 | 554 | 2,222 | 1,054 | |
| Age (year) | 70±14 | 72±13 | 65±15 | 72±12 | 63±15 | <.0001 |
| Gender (female %) | 48 | 55 | 39 | 50 | 37 | <.0001 |
| Race/Ethnicity (%) | ||||||
| Non-Hispanic White | 64 | 66 | 53 | 71 | 52 | 0.0012 |
| African American | 13 | 11 | 18 | 10 | 20 | 0.0001 |
| Hispanic | 12 | 12 | 14 | 10 | 13 | 0.7321 |
| Asian | 8 | 8 | 10 | 7 | 10 | 0.5915 |
| Other | 3 | 3 | 5 | 3 | 5 | 0.2296 |
| Primary Insurance (%) | ||||||
| Medicare | 59 | 63 | 58 | 60 | 48 | <.0001 |
| Medicaid | 5 | 5 | 6 | 3 | 7 | <.0001 |
| Other | 36 | 32 | 36 | 36 | 45 | <.0001 |
| Weight (kg) | 75.6±20.7 | 73.4±20.8 | 76.8±20.0 | 75.7±20.5 | 78.6±21.2 | <.0001 |
| eLBM (kg) | 64.4±7.3 | 63.5±7.2 | 65.8±7.1 | 63.9±7.0 | 66.4±7.5 | <.0001 |
| BMI (kg/m2) | 27.0±6.6 | 26.6±6.8 | 26.9±6.5 | 27.2±6.5 | 27.4±6.6 | 0.0004 |
| Laboratory values | ||||||
| Ca (mg/dl) | 9.1±0.5 | 9.1±0.5 | 9.1±0.6 | 9.1±0.5 | 9.0±0.6 | <.0001 |
| P (mg/dl) | 4.8±1.1 | 4.5±1.0 | 5.4±1.2 | 4.5±0.9 | 5.6±1.1 | <.0001 |
| K (mEq/l) | 4.4±0.5 | 4.3±0.5 | 4.6±0.6 | 4.4±0.5 | 4.6±0.5 | <.0001 |
| PTH (pg/ml) (IQR) | 340.9 (174.5,423.0) | 291.5 (147.0,366.3) | 395.2 (210.0,479.0) | 308.0 (162.6,392.0) | 463.0 (245.8,566.7) | <.0001 |
| WBC (×103/μl) | 7.8±2.8 | 8.2±3.2 | 8.3±2.9 | 7.5±2.6 | 7.6±2.3 | <.0001 |
| HGB (g/dl) | 11.2±1.1 | 11.0±1.1 | 10.9±1.2 | 11.4±1.0 | 11.3±1.1 | <.0001 |
| ALB (g/dl) | 3.5±0.5 | 3.1±0.3 | 3.2±0.3 | 3.8±0.2 | 3.9±0.3 | <.0001 |
| CO2 (mEq/l) | 23.5±0.8 | 24.3±2.7 | 23.1±2.6 | 23.6±2.7 | 22.3±2.6 | <.0001 |
| Cr (mg/dl) | 5.2±2.1 | 4.1±1.1 | 7.4±1.3 | 4.3±1.0 | 7.9±1.8 | <.0001 |
| nPCR (g/kg/day) | 0.8±0.2 | 0.7±0.2 | 0.8±0.2 | 0.8±0.2 | 0.9±0.2 | <.0001 |
| Fe (ng/mL) (IQR) | 376.3 (166.0,465.3) | 422.6 (180.0,521.0) | 451.9 (188.5,533.5) | 342.1 (157.0,426.0) | 332.6 (151.0,406.0) | <.0001 |
| ISAT (%) | 23.0±8.9 | 22.7±9.3 | 23.9±11.2 | 22.7±8.0 | 23.4±8.9 | 0.2441 |
| TIBC (μg/dl) | 227.8±48.7 | 203.2±46.6 | 204.1±41.1 | 246.8±44.7 | 240.4±40.6 | <.0001 |
| Comorbidities (%) | ||||||
| DM | 54.5 | 59.5 | 56.1 | 53.8 | 45.0 | <.0001 |
| ASHD | 14.5 | 14.8 | 13.4 | 14.7 | 14.5 | 0.9432 |
| CHF | 35.9 | 37.8 | 38.6 | 35.9 | 31.1 | 0.001 |
| HTN | 49.4 | 46.3 | 52.3 | 49.0 | 54.1 | 0.0006 |
| CVD | 1.8 | 1.7 | 1.6 | 1.9 | 1.6 | 0.9856 |
| Other Cardiovascular Disease | 16.2 | 17.6 | 15.3 | 16.6 | 13.7 | 0.0217 |
| COPD | 4.8 | 6.2 | 4.7 | 4.1 | 4.1 | 0.0024 |
| History of cancer | 2.5 | 2.8 | 2.3 | 2.3 | 2.5 | 0.4247 |
| Alcohol Abuse | 0.1 | 0.1 | 0 | 0.1 | 0.2 | 0.6623 |
Note: Categorical variables are given as percentage; continuous variables as mean (standard deviation) or median (interquartile range) as appropriate. Laboratory measures are represented as averages of repeated measures during the first quarter of entry to cohort.
P value was estimated by one-way ANOVA or Kruskal-Wallis test for continuous variables or chi-square test for categorical variables.
Conversion factors for units: calcium in mg/dl to mmol/l, ×0.2495; phosphorus in mg/dl to mmol/l, ×0.3229; hemoglobin and albumin in g/dl to g/l, ×10; TIBC in μg/dl to μmol/l, ×0.179. No conversion necessary for intact PTH in pg/ml and ng/l, WBC count in 103/μl and 109/l, and ferritin in ng/mL and μg/l.
Abbreviations: eLBM, estimated lean body mass; BMI, body mass index; Ca, calcium; P, phosphorus; PTH, parathyroid hormone; WBC, white blood cells; HGB, hemoglobin; ALB, albumin; CO2, carbon dioxide combining power; Cr, creatinine; nPCR, normalized protein catabolic rate; K, potassium; Fe, ferritin; ISAT, iron saturation ratio; TIBC, total iron-binding capacity; DM, diabetes mellitus; ASHD, atherosclerotic heart disease; CHF, congestive heart failure; HTN, hypertension; CVD, cerebrovascular disease; COPD, chronic obstructive pulmonary disease; IQR, interquartile range.
Baseline demographics and clinical characteristics according to concurrence of serum creatinine and albumin among twice-weekly and thrice-weekly hemodialysis patients
After matching based on age, gender, race/ethnicity, diabetes status and facility region, our cohort comprised 1,113 twice-weekly and 4,448 thrice-weekly hemodialysis patients. There were no significant differences in demographics before and after matching for twice-weekly hemodialysis patients. However, compared to entire cohort of thrice-weekly hemodialysis patients, the matched thrice-weekly patients were older, more likely to be female, non-Hispanic white or Asian, and utilized Medicare as primary insurance (Table S1). They also had a lower body weight, estimated lean body mass, body mass index, serum creatinine and parathyroid hormone level, and a lower prevalence of diabetes mellitus, hypertension and congestive heart failure, but a higher prevalence of other cardiovascular disease.
In the matched cohort, and in both twice-weekly and thrice-weekly hemodialysis groups, patients with a higher concurrence of serum creatinine and albumin were more likely to be younger, male, less likely to be non-Hispanic White, less likely to utilize Medicare as primary insurance, had higher body weight, estimated lean body mass, body mass index, serum levels of phosphorus and parathyroid hormone, lower serum level of ferritin, a higher prevalence of hypertension, and lower prevalence of diabetes mellitus, congestive heart failure and chronic obstructive pulmonary disease (Table 2).
Table 2.
Baseline characteristics stratified by concurrence of serum creatinine and albumin among twice-weekly (n=1,113) and thrice-weekly (n=4,448) hemodialysis patients
| Twice-Weekly HD | Thrice-Weekly HD | ||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|
| After Matched | After Matched | ||||||||||
| Variables | Total | Cr<6.0& ALB<3.5 |
Cr≥6.0& ALB<3.5 |
Cr<6.0& ALB≥3.5 |
Cr≥6.0& ALB≥3.5 |
Total | Cr<6.0& ALB<3.5 |
Cr≥6.0& ALB<3.5 |
Cr<6.0& ALB≥3.5 |
Cr≥6.0& ALB≥3.5 |
P value |
| N | 1,113 | 285 | 55 | 580 | 193 | 4,448 | 1,446 | 499 | 1,642 | 861 | |
| Age (year) | 70±14 | 70±13 | 64±15 | 72±13 | 63±15 | 70±14 | 72±12 | 65±15 | 72±12 | 63±15 | <.0001 |
| Gender (female %) | 48 | 54 | 42 | 50 | 35 | 48 | 55 | 38 | 50 | 37 | <.0001 |
| Race/Ethnicity (%) | |||||||||||
| Non-Hispanic White | 64 | 63 | 51 | 68 | 57 | 64 | 67 | 53 | 72 | 51 | 0.0012 |
| African American | 13 | 14 | 7 | 10 | 21 | 13 | 11 | 20 | 10 | 20 | 0.0001 |
| Hispanic | 12 | 13 | 16 | 11 | 10 | 12 | 11 | 14 | 10 | 14 | 0.7321 |
| Asian | 8 | 7 | 18 | 8 | 8 | 8 | 8 | 9 | 7 | 10 | 0.5915 |
| Other | 3 | 2 | 7 | 4 | 3 | 3 | 3 | 4 | 2 | 5 | 0.2296 |
| Primary Insurance (%) | |||||||||||
| Medicare | 60 | 67 | 56 | 61 | 47 | 59 | 63 | 58 | 60 | 49 | <.0001 |
| Medicaid | 4 | 5 | 5 | 4 | 5 | 5 | 5 | 7 | 3 | 8 | <.0001 |
| Other | 36 | 28 | 38 | 36 | 48 | 36 | 33 | 35 | 36 | 44 | <.0001 |
| Weight (kg) | 74.7±20.3 | 73.0±21.9 | 73.8±20.5 | 74.2±19.4 | 79.1±20.5 | 75.8±20.8 | 73.4±20.5 | 77.2±20.0 | 76.2±20.9 | 78.5±21.3 | <.0001 |
| eLBM (kg) | 63.7±7.0 | 62.8±7.4 | 64.2±7.1 | 63.2±6.5 | 66.4±7.3 | 64.6±7.3 | 63.6±7.2 | 66.0±7.1 | 64.1±7.2 | 66.4±7.5 | <.0001 |
| BMI (kg/m2) | 26.8±6.6 | 26.8±8.0 | 26.6±6.2 | 26.7±6.2 | 27.3±5.8 | 27.1±6.6 | 26.6±6.5 | 27.0±6.5 | 27.4±6.7 | 27.4±6.8 | 0.0004 |
| Laboratory values | |||||||||||
| Ca (mg/dl) | 9.1±0.5 | 9.1±0.5 | 9.0±0.6 | 9.1±0.5 | 9.0±0.7 | 9.1±0.5 | 9.1±0.5 | 9.1±0.6 | 9.1±0.5 | 9.0±0.6 | <.0001 |
| P (mg/dl) | 4.7±1.0 | 4.6±0.9 | 5.6±1.2 | 4.5±0.8 | 5.5±1.1 | 4.8±1.1 | 4.5±1.0 | 5.4±1.2 | 4.5±0.9 | 5.6±1.1 | <.0001 |
| K (mEq/l) | 4.4±0.5 | 4.4±0.5 | 4.6±0.5 | 4.4±0.5 | 4.6±0.6 | 4.4±0.5 | 4.3±0.5 | 4.6±0.6 | 4.4±0.4 | 4.6±0.5 | <.0001 |
| PTH (pg/ml) (IQR) | 341.1 (172.0,421.0) | 320.3 (151.0,399.0) | 398.2 (218.7,512.0) | 304.3 (163.8,379.8) | 466.2 (235.0,577.6) | 340.9 (175.4,424.3) | 285.8 (146.5,361.5) | 394.8 (209.3,478.5) | 309.3 (161.2,394.0) | 462.3 (249.0,564.8) | <.0001 |
| WBC (×103/μl) | 7.5±2.8 | 7.9±3.1 | 7.5±2.0 | 7.5±3.0 | 7.1±2.0 | 7.9±2.8 | 8.2±3.2 | 8.4±3.0 | 7.5±2.5 | 7.7±2.3 | <.0001 |
| HGB (g/dl) | 11.2±1.0 | 11.1±1.0 | 10.8±0.9 | 11.3±1.0 | 11.2±1.1 | 11.2±1.1 | 11.0±1.1 | 10.9±1.2 | 11.4±1.1 | 11.3±1.1 | <.0001 |
| ALB (g/dl) | 3.7±0.4 | 3.1±0.3 | 3.2±0.2 | 3.9±0.2 | 4.0±0.3 | 3.5±0.4 | 3.1±0.3 | 3.1±0.3 | 3.8±0.2 | 3.9±0.2 | <.0001 |
| CO2 (mEq/l) | 22.8±2.8 | 23.8±2.7 | 21.6±2.6 | 22.8±2.8 | 21.4±2.4 | 23.7±2.7 | 24.4±2.7 | 23.3±2.6 | 23.8±2.6 | 22.5±2.6 | <.0001 |
| Cr (mg/dl) | 4.8±2.0 | 3.7±1.1 | 7.3±1.2 | 4.1±1.1 | 7.8±1.9 | 5.3±2.1 | 4.2±1.1 | 7.5±1.3 | 4.3±1.0 | 8.0±1.8 | <.0001 |
| nPCR (g/kg/day) | 0.8±0.2 | 0.7±0.2 | 0.8±0.2 | 0.8±0.2 | 0.8±0.2 | 0.8±0.2 | 0.7±0.2 | 0.8±0.2 | 0.8±0.2 | 0.9±0.2 | <.0001 |
| Fe (ng/mL) (IQR) | 328.9 (146.7,410.0) | 393.4 (176.3,485.7) | 407.7 (122.5,476.0) | 303.9 (138.5,389.0) | 286.2 (143.8,344.3) | 388.2 (170.7,482.0) | 428.3 (184.0,530.3) | 456.8 (190.3,542.5) | 355.6 (164.9,439.0) | 343.0 (154.5,419.5) | <.0001 |
| ISAT (%) | 22.3±7.3 | 22.4±8.0 | 23.7±9.6 | 21.9±6.8 | 22.8±7.3 | 23.1±9.3 | 22.7±9.5 | 23.9±11.3 | 23.0±8.3 | 23.5±9.2 | 0.2441 |
| TIBC (μg/dl) | 237.9±46.6 | 209.9±43.4 | 223.1±41.1 | 252.5±44.0 | 239.7±39.7 | 225.3±48.9 | 201.9±47.1 | 202.1±40.6 | 244.8±44.8 | 240.6±40.9 | <.0001 |
| Comorbidities (%) | |||||||||||
| DM | 54.5 | 64.6 | 63.6 | 52.6 | 43 | 54.5 | 58.5 | 55.3 | 54.2 | 47.9 | <.0001 |
| ASHD | 14.9 | 15.1 | 9.1 | 15.5 | 14.5 | 14.5 | 14.7 | 13.8 | 14.4 | 14.5 | 0.9432 |
| CHF | 30.8 | 30.2 | 34.5 | 32.8 | 24.9 | 37.1 | 39.3 | 39.1 | 37 | 32.5 | 0.001 |
| HTN | 52 | 48.1 | 69.1 | 50.3 | 58 | 48.8 | 45.9 | 50.5 | 48.5 | 53.2 | 0.0006 |
| CVD | 1.8 | 1.4 | 1.8 | 2.1 | 1.6 | 1.8 | 1.8 | 1.6 | 1.8 | 1.6 | 0.9856 |
| Other | |||||||||||
| Cardiovascular Disease | 14.2 | 15.8 | 14.5 | 13.6 | 13.5 | 16.7 | 18 | 15.4 | 17.6 | 13.7 | 0.0217 |
| COPD | 3.8 | 4.9 | 1.8 | 3.6 | 3.1 | 5.1 | 6.4 | 5 | 4.3 | 4.3 | 0.0024 |
| History of cancer | 2.1 | 2.8 | 0 | 2.1 | 1.6 | 2.6 | 2.8 | 2.6 | 2.4 | 2.7 | 0.4247 |
| Alcohol Abuse | 0.2 | 0.4 | 0 | 0.2 | 0 | 0.1 | 0.1 | 0 | 0.1 | 0.2 | 0.6623 |
Note: Categorical variables are given as percentage; continuous variables as mean (standard deviation) or median (interquartile range) as appropriate. Laboratory measures are represented as averages of repeated measures during the first quarter of entry to cohort.
P value was estimated by one-way ANOVA or Kruskal-Wallis test for continuous variables or chi-square test for categorical variables.
Conversion factors for units: calcium in mg/dl to mmol/l, ×0.2495; phosphorus in mg/dl to mmol/l, ×0.3229; hemoglobin and albumin in g/dl to g/l, ×10; TIBC in μg/dl to μmol/l, ×0.179. No conversion necessary for intact PTH in pg/ml and ng/l, WBC count in 103/μl and 109/l, and ferritin in ng/mL and μg/l.
Abbreviations: eLBM, estimated lean body mass; BMI, body mass index; Ca, calcium; P, phosphorus; PTH, parathyroid hormone; WBC, white blood cells; HGB, hemoglobin; ALB, albumin; CO2, carbon dioxide combining power; Cr, creatinine; nPCR, normalized protein catabolic rate; K, potassium; Fe, ferritin; ISAT, iron saturation ratio; TIBC, total iron-binding capacity; DM, diabetes mellitus; ASHD, atherosclerotic heart disease; CHF, congestive heart failure; HTN, hypertension; CVD, cerebrovascular disease; COPD, chronic obstructive pulmonary disease; IQR, interquartile range.
Compared to twice-weekly hemodialysis patients, the thrice-weekly patients had a higher mean body weight (76±21vs. 75±20 kg), estimated lean body mass (65±7 vs. 64±7 kg) and body mass index (27.1±6.6 vs. 26.8±6.6 kg/m2). Notably, this trend was also seen in each category of combined serum creatinine and albumin level.
Mortality outcome according to concurrence of serum creatinine and albumin
Figure 2, Table S2 shows the association of combined serum albumin and creatinine levels with mortality in the matched cohort, according to lower levels and then higher levels of albumin. Compared to the reference group (serum creatinine ≥ 6.0mg/dl & albumin ≥ 3.5g/dl), patients in the lowest concurrence of serum creatinine and albumin (serum creatinine < 6.0mg/dl & albumin < 3.5g/dl) had a 3.2-fold increased risk of crude mortality (HR: 3.22, 95%CI: 2.73-3.80) in unadjusted models. Associations between lower albumin and higher creatinine and then lower creatinine and higher albumin followed a linear trend toward comparatively lower mortality risk. These relationships remained robust after further adjustments in subsequent case-mix and case-mix & MICS models.
Figure 2.
Baseline survival hazard ratio over 5 years according to concurrence of serum creatinine (mg/dl) and albumin (g/dl) in total after matched hemodialysis patients (n=5,561).
The same linear trend was also present in both 1,113 twice-weekly and 4,448 thrice-weekly hemodialysis cohorts independently (p-for-interaction=0.7667). In twice-weekly hemodialysis patients, compared to the reference group (serum creatinine ≥ 6.0mg/dl & albumin ≥ 3.5g/dl), patients in the group with the lowest levels of serum creatinine and albumin (<6.0mg/dl and <3.5g/dl, respectively) had a 2.9-fold increased risk of crude mortality (HR: 2.94, 95%CI: 1.92-4.52). (Figure 3, Table S3) In thrice-weekly hemodialysis patients, compared to the reference group (serum creatinine ≥ 6.0mg/dl & albumin ≥ 3.5g/dl), patients in the lowest concurrent level of serum creatinine and albumin (<6.0mg/dl & <3.5g/dl, respectively) had a 3.3-fold increased risk of crude mortality (HR: 3.25, 95%CI: 2.72-3.89). (Figure 4, Table S4) This relationship remained robust after further adjustment for case-mix and case-mix & MICS models especially among the thrice-weekly matched hemodialysis patients.
Figure 3.
Baseline survival hazard ratio over 5 years according to concurrence of serum creatinine (mg/dl) and albumin (g/dl) in after matched twice-weekly hemodialysis patients (n=1,113).
Figure 4.
Baseline survival hazard ratio over 5 years according to concurrence of serum creatinine (mg/dl) and albumin (g/dl) in after matched thrice-weekly hemodialysis patients (n=4,448).
Mortality Outcome according to concurrence of serum creatinine and albumin in twice-weekly hemodialysis patients
Figure 5, Table S5 shows results of a combined analysis, comparing both modality and concurrence of creatinine and albumin in 8 groups, and using the thrice-weekly hemodialysis patients with the highest concurrence serum creatinine and albumin (serum creatinine ≥ 6.0mg/dl & albumin ≥ 3.5g/dl) as reference group. In this analysis, twice and thrice-weekly hemodialysis patients with the lowest concurrence of serum creatinine and albumin (<6.0mg/dl & <3.5g/dl, respectively) had a 2.3-fold (HR: 2.31, 95%CI: 1.78-3.00) and 3.3-fold (HR: 3.27, 95%CI: 2.74-3.91) increased mortality risk, respectively. Twice-weekly hemodialysis patients with highest concurrent serum of creatinine and albumin had 20% (HR: 0.80, 95%CI: 0.53-1.19) lower death risk in comparison to the reference group, albeit non-significant. After adjustment for case-mix and MICS markers, the relationship between mortality and concurrence serum of creatinine and albumin remained robust among especially among thrice-weekly hemodialysis patients.
Figure 5.
Baseline survival hazard ratio over 5 years according to concurrence of serum creatinine (mg/dl) and albumin (g/dl) among after matched twice-weekly (n=1,113) and thrice-weekly (n=4,448) hemodialysis patients.
Results for sensitivity analyses, which additionally matched for baseline concurrent serum albumin and creatinine groups, showed similar results (Table S6), p-for-interaction: 0.6949.
Discussion
In a contemporary matched diverse cohort of 5,561 hemodialysis patients dialyzed either twice or thrice weekly, we found an inverse relationship between concurrence of serum creatinine and albumin and mortality, independent of dialysis frequency. High concurrence of serum creatinine and albumin was associated with the greatest survival while the low serum creatinine and albumin had the highest mortality in both the twice-weekly and thrice-weekly hemodialysis patients. Furthermore, these inverse associations were still present after adjustment for case mix covariates as well as nutritional and inflammatory markers. Our findings present an important observation in the role of concurrence of serum creatinine and albumin in lower risk for death in twice-weekly hemodialysis patients.
Many patients with kidney disease and ESRD including those undergoing hemodialysis treatment suffer from protein-energy wasting (7, 22). Protein-energy wasting and inflammation, which are hence referred to collectively as the MICS, are associated with poorer quality of life and higher morbidity and mortality in hemodialysis patients. Several studies (23-25) have underlined the prognostic significance of protein-energy wasting as a strong predictor of morbidity and mortality independently of other risk factors in hemodialysis patients. Protein nutritional status is determined by visceral and somatic protein stores (26); and evaluation of muscle mass is an important method to assess protein nutritional status (14, 27-29). Serum creatinine level has been shown to be a reliable indicator of muscle mass and somatic protein mass, and can be a crucial and useful marker of nutritional assessment in hemodialysis patients (26). Previous studies had found that higher serum creatinine level among hemodialysis patients is associated with better survival (11-13).
Moreover, serum albumin is an indicator of visceral protein stores, and low serum albumin is an important sign of protein energy wasting. Studies have suggested that serum albumin is a reliable marker of nutritional status in ESRD patients (30, 31) as well as a prognostic variable due to its strong association with long term outcomes (32,33). Presence of a low serum albumin in patients often triggers a search for potentially reversible causes of malnutrition or inflammation and therapeutic interventions that generally consist of measures to increase the enteral protein and energy intake (34). Furthermore, both serum albumin and creatinine would benefit from increases in protein energy intake, and thereby improve survival in hemodialysis patients. The impact of combined higher creatinine and albumin levels on mortality in hemodialysis is not well known, and has not been studied in patients receiving less frequent hemodialysis treatments (twice-weekly).
Based on established guidelines and recommendations, thrice-weekly hemodialysis has been regarded as the standard renal replacement therapy for ESRD patients, and is therefore the most prevalent dialysis treatment modality in ESRD patients in the United States (22). Data from a national sample has found that approximately 4% of hemodialysis patients in the US are treated with twice per week hemodialysis (16). This study also identified factors predictive of twice-weekly hemodialysis, among which were lower serum creatinine levels and higher serum albumin. Other factors listed included older age, Caucasian race, female gender, lower body mass index, and higher residual renal function at dialysis start. In our study, patients were matched on age, race, gender, diabetes, and facility region. Baseline body mass index between the two dialysis frequency groups were also similar (Table S1). In sensitivity analysis, which presented with similar results, patients were additionally matched on baseline concurrent serum albumin and serum creatinine groups. The results of our study show that once these potential selection bias factors are balanced across groups, a higher concurrence level of serum creatinine and albumin, representing a better nutritional status and a lower inflammatory state, leads to better survival in both twice-weekly and thrice-weekly hemodialysis patients.
The strength of our study is the use of large contemporary nationally representative incident dialysis population with the ability to match 1:4 on a number of potential confounders including age, gender, race/ethnicity, diabetes, and facility region. However, some limitations should be considered. First, we could not obtain sufficient data related to residual kidney function. Secondly, we did not have comprehensive data that may affect hemodialysis patient outcomes, such as ultrafiltration rate, dialysis access type, hospitalizations and other known or unknown confounders. Models were only adjusted for known and measured confounders and therefore, we could not eliminate the possibility of residual confounding.
Conclusion
Low serum creatinine and albumin levels are associated with highest mortality, and higher visceral protein and muscle mass combined is associated with better survival in both twice-weekly and thrice-weekly hemodialysis patients. Twice-weekly hemodialysis patients have a lower risk for death according to the same levels of nutritional status and muscle mass when compared with thrice-weekly hemodialysis patients.
Practical Application
Among the thrice-weekly hemodialysis patients, higher serum albumin and creatinine have lower mortality risk; twice-weekly hemodialysis who have the same nutritional status confer a similar survival benefit.
Supplementary Material
Acknowledgement
The authors express their sincere appreciation to XXX clinics that ensure the extensive data collection on which their work is based. They thank XXX Clinical Research for their commitment to clinical research.
The abstract of this report has been presented as a poster at the XXX.
Funding Source:
The work in this manuscript has been performed with the support of the National Institute of Diabetes, Digestive and Kidney Disease of the National Institute of Health research grants R01-DK95668 (KKZ), K24-DK091419 (KKZ), R01-DK078106 (KKZ) KKZ is supported by philanthropic grants from Mr. Harold Simmons, Mr. Louis Chang, Dr. Joseph Lee and AVEO. CPK is supported by the National Institute of Diabetes, Digestive and Kidney Disease grants R01-DK096920 and U01-DK102163. CMR is supported by the National Institute of Diabetes, Digestive and Kidney Disease grant K23-DK102903.
Footnotes
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Relevant Potential Conflict of Interest:
KKZ has received honoraria and/or support from Abbott, Abbvie, Alexion, Amgen, American Society of Nephrology, Astra-Zeneca, AVEO, Chugai, DaVita, Fresenius, Genetech, Haymarket Media, Hospira, Kabi, Keryx, National Institutes of Health, National Kidney Foundation, Relypsa, Resverlogix, Sanofi, Shire, Vifor, ZS-Pharma. CPK has received honoraria from Sanofi-Aventis, Relypsa and ZS Pharma.
The Y-axis shows the logarithm of the rate ratio of all-cause mortality over 5 years, unadjusted and adjusted for case-mix and MICS. The reference group is categories creatinine ≥6.0mg/dl & albumin ≥3.5g/dl in figure 2, figure3 and figure 4, and categories creatinine ≥6.0mg/dl & albumin ≥3.5g/dl of thrice-weekly hemodialysis patients in Figure 5. See text for the list of covariates in multivariate adjustment.
References
- 1.Shah SV, Feehally J. The third World Kidney Day: looking back and thingking forward. Clin J Am Soc Nephrol. 2008;3(2):309–311. doi: 10.2215/CJN.05001107. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 2.Coresh J, Selvin E, Stevens LA, Manzi J, Kusek JW, Eggers P, Van Lente F, Levey AS. Prevalence of chronic kidney disease in the United States. JAMA. 2007;298(17):2038–2047. doi: 10.1001/jama.298.17.2038. [DOI] [PubMed] [Google Scholar]
- 3.US Renal Data System . USRDS 2012 Annual Data Report: Atlas of End-Stage Renal Disease in the United States. National Institutes of Health, National Institute of Diabetes and Digestive and Kidney Diseases; Bethesda, MD: 2012. [Google Scholar]
- 4.Keshaviah PR, Nolph KD, Moore HL, Prowant B, Emerson PF, Meyer M, Twardowski ZJ, Khanna R, Ponferrada L, Collins A. Lean body mass estimation by creatinine kinetics. J Am Soc Nephrol. 1994;4(7):1475–1485. doi: 10.1681/ASN.V471475. [DOI] [PubMed] [Google Scholar]
- 5.Schutte JE, Longhurst JC, Gaffney FA, Bastian BC, Blomqvist CG. Total plasma creatinine: an accurate measure of total striated muscle mass. J Appl Physiol Respir Environ Exerc Physiol. 1981;51(3):762–766. doi: 10.1152/jappl.1981.51.3.762. [DOI] [PubMed] [Google Scholar]
- 6.Patel SS, Molnar MZ, Tayek JA, Ix JH, Noori N, Benner D, Heymsfield S, Kopple JD, Kovesdy CP, Kalantar-Zadeh K. Serum creatinine as a marker of muscle mass in chronic kidney disease: results of a cross-sectional study and review of literature. J Cachexia Sarcopenia Muscle. 2013;4(1):19–29. doi: 10.1007/s13539-012-0079-1. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 7.Fouque D, Kalantar-Zadeh K, Kopple JD, Cano N, Chauveau P, Cuppari L, Franch H, Guarnieri G, Ikizler TA, Kaysen G, Lindholm B, Massy Z, Mitch W, Pineda E, Stenvinkel P, Treviño-Becerra A, Wanner C. A proposed nomenclature and diagnostic criteria for protein-energy wasting in acute and chronic kidney disease. Kidney Int. 2008;73(4):391–398. doi: 10.1038/sj.ki.5002585. [DOI] [PubMed] [Google Scholar]
- 8.Noori N, Kovesdy CP, Bross R, Lee M, Oreopoulos A, Benner D, Mehrotra R, Kopple JD, Kalantar-Zadeh K. Novel equations to estimate lean body mass in maintenance hemodialysis patients. Am J Kidney Dis. 2011;57(1):130–139. doi: 10.1053/j.ajkd.2010.10.003. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 9.Kaizu Y, Ohkawa S, Kumagai H. Muscle mass index in haemodialysis patients: a comparison of indices obtained by routine clinical examinations. Nephrol Dial Transplant. 2002;17(3):442–448. doi: 10.1093/ndt/17.3.442. [DOI] [PubMed] [Google Scholar]
- 10.Moreau-Gaudry X, Guebre-Egziabher F, Jean G, Genet L, Lataillade D, Legrand E, Kuentz F, Trolliet P, Fouque D. Serum creatinine improves body mass index survival prediction in hemodialysis patients: a 1-year prospective cohort analysis from the ARNOS study. J Ren Nutr. 2011;21(5):369–375. doi: 10.1053/j.jrn.2010.08.005. [DOI] [PubMed] [Google Scholar]
- 11.Walther CP, Carter CW, Low CL, Williams P, Rifkin DE, Steiner RW, Ix JH. Interdialytic creatinine change versus predialysis creatinine as indicators of nutritional status in maintenance hemodialysis. Nephrol Dial Transplant. 2012;27(2):771–776. doi: 10.1093/ndt/gfr389. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 12.Kalantar-Zadeh K, Streja E, Kovesdy CP, Oreopoulos A, Noori N, Jing J, Nissenson AR, Krishnan M, Kopple JD, Mehrotra R, Anker SD. The obesity paradox and mortality associated with surrogates of body size and muscle mass in patients receiving hemodialysis. Mayo Clin Proc. 2010;85(11):991–1001. doi: 10.4065/mcp.2010.0336. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 13.Noori N, Kovesdy CP, Dukkipati R, Feroze U, Molnar MZ, Bross R, Nissenson AR, Kopple JD, Norris KC, Kalantar-Zadeh K. Racial and ethnic differences in mortality of hemodialysis patients: role of dietary and nutritional status and inflammation. Am J Nephrol. 2011;33(2):157–167. doi: 10.1159/000323972. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 14.Kalantar-Zadeh K, Streja E, Molnar MZ, Lukowsky LR, Krishnan M, Kovesdy CP, Greenland S. Mortality prediction by surrogates of body composition: an examination of the obesity paradox in hemodialysis patients using composite ranking score analysis. Am J Epidemiol. 2012;175(8):793–803. doi: 10.1093/aje/kwr384. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 15.Mehrotra R, Kopple JD. Nutritional management of maintenance dialysis patients: why aren't we doing better? Annu Rev Nutr. 2001;21:343–379. doi: 10.1146/annurev.nutr.21.1.343. [DOI] [PubMed] [Google Scholar]
- 16.Hanson JA, Hulbert-Shearon TE, Ojo AO, Port FK, Wolfe RA, Agodoa LY, Daugirdas JT. Prescription of twice-weekly hemodialysis in the USA. Am J Nephrol. 1999;19(6):625–633. doi: 10.1159/000013533. [DOI] [PubMed] [Google Scholar]
- 17.Lin X, Yan Y, Ni Z, Gu L, Zhu M, Dai H, Zhang W, Qian J. Clinical outcome of twice-weekly hemodialysis patients in shanghai. Blood Purif. 2012;33(1-3):66–72. doi: 10.1159/000334634. [DOI] [PubMed] [Google Scholar]
- 18.US Census Bureau [18 April 2014];Version current 1 April 2014. No authors listed. Internet: http://www.census.gov.
- 19.Federal Office of Management and Budget [18 April 2014];Version current 1 April 2014. No authors listed. Internet: http://www.whitehouse.gov/omb.
- 20.Wang J, Streja E, Rhee CM, Soohoo M, Feng M, Brunelli SM, Kovesdy CP, Gillen D, Kalantar-Zadeh K, Chen JL. Lean body mass and survival in hemodialysis patients and the roles of race and ethnicity. J Ren Nutr. 2016;26(1):26–37. doi: 10.1053/j.jrn.2015.07.003. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 21.Bergstralh EJ, Kosanke JL, Jacobsen SJ. Software for optimal matching in observational studies. Epidemiology. 1996;7(3):331–332. [PubMed] [Google Scholar]
- 22.Clinical practice guidelines for nutrition in chronic renal failure. K/DOQI, National Kidney Foundation. Am J Kidney Dis. 2000;35(6 Suppl 2):S1–S140. doi: 10.1053/ajkd.2000.v35.aajkd03517. No authors listed. [DOI] [PubMed] [Google Scholar]
- 23.Combe C, Chauveau P, Laville M, Fouque D, Azar R, Cano N, Canaud B, Roth H, Leverve X, Aparicio M. Influence of nutritional factors and hemodialysis adequacy on the survival of 1,610 French patients. Am J Kidney Dis. 2001;37(1 Suppl 2):S81–88. doi: 10.1053/ajkd.2001.20756. [DOI] [PubMed] [Google Scholar]
- 24.Dwyer JT, Larive B, Leung J, Rocco MV, Greene T, Burrowes J, Chertow GM, Cockram DB, Chumlea WC, Daugirdas J, Frydrych A, Kusek JW, HEMO Study Group Are nurtitional status indicators associated with mortality in the Hemodialysis(HEMO) Study?. Kidney Int. 2005;68(4):1766–1776. doi: 10.1111/j.1523-1755.2005.00593.x. [DOI] [PubMed] [Google Scholar]
- 25.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(1):15–25. doi: 10.1053/jren.2003.50005. [DOI] [PubMed] [Google Scholar]
- 26.Pupim LB, Ikizler TA. Assessment and monitoring of uremic malnutrition. J Ren Nutr. 2004;14(1):6–19. doi: 10.1053/j.jrn.2003.10.001. [DOI] [PubMed] [Google Scholar]
- 27.Kalantar-Zadeh K, Kopple JD, Kilpatrick RD, McAllister CJ, Shinaberger CS, Gjertson DW, Greenland S. Association of morbid obesity and weight change over time with cardiovascular survival in hemodialysis population. Am J Kidney Dis. 2005;46(3):489–500. doi: 10.1053/j.ajkd.2005.05.020. [DOI] [PubMed] [Google Scholar]
- 28.Park J, Jin DC, Molnar MZ, Dukkipati R, Kim YL, Jing J, Levin NW, Nissenson AR, Lee JS, Kalantar-Zadeh K. Mortality predictability of body size and muscle mass surrogates in Asian vs white and African American hemodialysis patients. Mayo Clin Proc. 2013;88(5):479–486. doi: 10.1016/j.mayocp.2013.01.025. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 29.Noori N, Kopple JD, Kovesdy CP, Feroze U, Sim JJ, Murali SB, Luna A, Gomez M, Luna C, Bross R, Nissenson AR, Kalantar-Zadeh K. Mid-arm muscle circumference and quality of life and survival in maintenance hemodialysis patients. Clin J Am Soc Nephrol. 2010;5(12):2258–2268. doi: 10.2215/CJN.02080310. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 30.Friedman AN, Fadem SZ. Reassessment of albumin as a nutritional marker in kidney disease. J Am Soc Nephrol. 2010;21(2):223–230. doi: 10.1681/ASN.2009020213. [DOI] [PubMed] [Google Scholar]
- 31.Gama-Axelsson T, Heimbürger O, Stenvinkel P, Bárány P, Lindholm B, Qureshi AR. Serum albumin as predictor of nutritional status in patients with ESRD. Clin J Am Soc Nephrol. 2012;7(9):1446–1453. doi: 10.2215/CJN.10251011. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 32.Cooper BA, Penne EL, Bartlett LH, Pollock CA. Protein malnutrition and hypo albuminemia as predictors of vascular events and mortality in ESRD. Am J Kidney Dis. 2004;43(1):61–66. doi: 10.1053/j.ajkd.2003.08.045. [DOI] [PubMed] [Google Scholar]
- 33.Kaysen GA, Johansen KL, Cheng SC, Jin C, Chertow GM. Trends and outcomes associated with serum albumin concentration among incident dialysis patients in the United States. J Ren Nutr. 2008;18(4):323–331. doi: 10.1053/j.jrn.2008.04.002. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 34.Kalantar-Zadeh K, Cano NJ, Budde K, Chazot C, Kovesdy CP, Mak RH, Mehrotra R, Raj DS, Sehgal AR, Stenvinkel P, Ikizler TA. Diets and enteral supplements for improving outcomes in chronic kidney disease. Nat Rev Nephrol. 2011;7(7):369–384. doi: 10.1038/nrneph.2011.60. [DOI] [PMC free article] [PubMed] [Google Scholar]
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