CJASN
HOME HELP FEEDBACK SUBSCRIPTIONS ARCHIVE SEARCH TABLE OF CONTENTS
QUICK SEARCH:   [advanced]


     

Published ahead of print on October 1, 2008
Clin J Am Soc Nephrol 3: 1759-1768, 2008
© 2008 American Society of Nephrology
doi: 10.2215/CJN.00820208
This Article
Right arrow Abstract Freely available
Right arrow Full Text (PDF)
Right arrow All Versions of this Article:
CJN.00820208v1
3/6/1759    most recent
Right arrow Alert me when this article is cited
Right arrow Alert me if a correction is posted
Right arrow Citation Map
Services
Right arrow Email this article to a friend
Right arrow Similar articles in this journal
Right arrow Similar articles in PubMed
Right arrow Alert me to new issues of the journal
Right arrow Download to citation manager
Right arrow reprints & permissions
Citing Articles
Right arrow Citing Articles via Google Scholar
Google Scholar
Right arrow Articles by Spiegel, B. M.R.
Right arrow Articles by Esrailian, E.
Right arrow Search for Related Content
PubMed
Right arrow PubMed Citation
Right arrow Articles by Spiegel, B. M.R.
Right arrow Articles by Esrailian, E.

Epidemiology and Outcomes

Biomarkers and Health-Related Quality of Life in End-Stage Renal Disease: A Systematic Review

Brennan M.R. Spiegel*,{dagger},{ddagger}, Gil Melmed{ddagger},§, Sean Robbins||, and Eric Esrailian{dagger},§

* Department of Medicine, VA Greater Los Angeles Healthcare System, {dagger} Department of Medicine, David Geffen School of Medicine at University of California, Los Angeles, § Department of Medicine, University of California, Cedars-Sinai Medical Center, {ddagger} UCLA/VA Center for Outcomes Research and Education, and || Global Health Economics, Amgen, Inc., Los Angeles, California

Correspondence: Dr. Brennan M.R. Spiegel, Department of Medicine, VA Greater Los Angeles Healthcare System, David Geffen School of Medicine at UCLA, UCLA/VA Center for Outcomes Research and Education (CORE), 11301 Wilshire Boulevard, Building 115 Room 215, Los Angeles, CA 90073. Phone: 310-268-3256; Fax: 310-268-4510; E-mail: bspiegel{at}mednet.ucla.edu


   Abstract
Top
 Abstract
Introduction
 Materials and Methods
 Results
 Discussion
 Conclusions
 Technical Appendix
 Disclosures
 References
 
Background and objectives: Health-related quality of life (HRQOL) predicts mortality in ESRD, yet adoption of HRQOL monitoring is not widespread, and regulatory authorities remain predominantly concerned with monitoring traditional biologic parameters. To assist with future efforts to adopt HRQOL monitoring while acknowledging the importance of biomarkers, this study sought to establish which domains of HRQOL are most affected by ESRD and to measure the strength of evidence linking common biomarkers to HRQOL in ESRD.

Design, setting, participants, & measurements: A systematic review was performed to identify studies that measured HRQOL in ESRD. Data were abstracted according to a conceptual model regarding the measurement of HRQOL differences, and HRQOL data were converted to weighted mean effect sizes and correlation coefficients.

Results: The impact of ESRD was largest in the Short Form 36 domains of physical functioning (e.g., role-physical, vitality) and smallest in mental functioning (e.g., mental health, role-emotional). Dialysis adequacy, as measured by Kt/V, was a poor correlate for Short Form 36 scores. Similarly, mineral metabolism (e.g., calcium x phosphorous, parathyroid hormone) and inflammatory (e.g., C-reactive protein, TNF) biomarkers had small effect sizes and correlations with HRQOL. In contrast, hematocrit demonstrated small to moderate relationships with mental and physical HRQOL, and nutritional biomarkers (e.g., albumin, creatinine, body mass index) demonstrated moderate to large relationships.

Conclusions: HRQOL in ESRD is most affected in the physical domains, and nutritional biomarkers are most closely associated with these domains. In contrast, Kt/V, mineral metabolism indices, and inflammatory markers are poor HRQOL correlates.


   Introduction
Top
 Abstract
 Introduction
Materials and Methods
 Results
 Discussion
 Conclusions
 Technical Appendix
 Disclosures
 References
 
ESRD is a prevalent and expensive condition that affects more than 400,000 people in the United States at a cost of $20 billion annually (1–5). This health economic burden is amplified by the impact of ESRD on health-related quality of life (HRQOL) resulting from physical, psychological, and social stressors engendered by the disease and its treatment. The protean physical manifestations of ESRD span every organ system, range in severity from nuisance to life threatening, and negatively disrupt patients’ lifestyles without exception (6–8). Psychologically, patients with ESRD have a disproportionately high incidence of depression, often experience anticipation anxiety from the hovering "Sword of Damocles" of impending physical demise, and encounter difficulties in coping with their disease (8–10). Patients with ESRD are limited by dietary restrictions, time restrictions, and oftentimes overwhelming physical and psychological restrictions that can lead to disruptions in personal relations and social withdrawal (8–10).

Because HRQOL is a consistent predictor of mortality in ESRD (8,11–14), there is interest in measuring HRQOL in both clinical trials and everyday clinical practice. This acknowledgment that the burden of ESRD extends beyond its impact on traditional biologic measures is reflected in initiatives from the National Kidney Foundation to support efforts aimed at improving HRQOL in patients with ESRD (5); however, many clinicians are not well versed in the interpretation of HRQOL, and HRQOL may fail to resonate with clinicians in the same way as traditional biomarkers such as dialysis adequacy indices, mineral metabolism markers, or anemia markers, among others. This disconnect is widened by the practice of regulatory authorities to define "quality care" predominantly on the basis of biomarkers with less emphasis on explicit measures of the patient experience (15); however, failure to understand and interpret HRQOL data in ESRD may lead to the myopic view that biologic outcomes are of sole importance—a view that likely underestimates the true burden of illness engendered by ESRD.

In light of the disconnect between the growing importance of measuring HRQOL in ESRD and the continued emphasis on tracking biomarkers as the predominant determinant of quality care, it is important to understand the strength of evidence linking individual biomarkers to HRQOL. This is important because HRQOL remains a robust predictor of morbidity and mortality in ESRD and is an important surrogate of quality care (8,11–14). Thus, knowing which biomarkers optimally predict HRQOL may provide insight into how best to manage ESRD. We, therefore, performed a systematic review first to establish which domains of HRQOL are most affected by ESRD in general and then to establish the strength of evidence linking common biomarkers to individual HRQOL domains in ESRD.


   Materials and Methods
Top
 Abstract
 Introduction
 Materials and Methods
Results
 Discussion
 Conclusions
 Technical Appendix
 Disclosures
 References
 
Systematic Review
We performed a systematic review of MEDLINE to identify relevant English-language publications from 1990 through 2007. We sought two types of studies: (1) Studies that compared HRQOL data between patients with ESRD and non-ESRD control subjects and (2) studies that assessed variations in HRQOL across biomarkers. Refer to the Technical Appendix for a detailed description of our systematic review method.

Conceptual Framework Overview
The most clinically meaningful method of measuring HRQOL differences between groups is to anchor data to relevant clinical outcomes (16), such as biomarkers. We performed our analyses according to this conceptual framework—a method we have used in other areas of medicine (17). HRQOL differences between groups can be measured using a scaled effect size (ES). The ES for between-group HRQOL differences is calculated using the following equation:

Formula

Cohen has standardized the interpretation of the ES as follows: <0.2 is a small clinical effect, 0.5 is a moderate effect, and >0.8 is a large effect (18). The advantage of this interpretation scheme is that HRQOL outcomes in different interventions and diseases can be compared using a standardized ES, a statistic with clinical relevance that serves as an "exchange currency" across disparate fields in medicine.

Statistical Analysis
We converted HRQOL data from each study to ES data and adopted absolute values for our pooled estimates. We then calculated the weighted mean for each relevant statistic stratified by "high" versus"low" levels of each biomarker, as defined by the literature. When available, we abstracted adjusted HRQOL differences in lieu of unadjusted data. In instances in which a clinically interpretable statistic could not be calculated from the available data, we sought data regarding correlations (i.e., Pearson correlation coefficient [r values]) between HRQOL and individual parameters. For parameters with two or more studies with common correlations, we calculated the sample size–weighted pooled r value using the weighted mean as our point estimate and calculated 95% confidence intervals (CI) around the point estimate, a technique used by previous investigators linking outcomes to HRQOL in ESRD (19).


   Results
Top
 Abstract
 Introduction
 Materials and Methods
 Results
Discussion
 Conclusions
 Technical Appendix
 Disclosures
 References
 
Study Selection
The search strategy identified 2335 titles. Of these, we selected 47 studies that met our explicit inclusion criteria (interobserver agreement was high [{kappa} >0.9] for each phase of selection).

HRQOL in ESRD versus Healthy Control Subjects
We identified 20 studies that compared HRQOL in patients who had ESRD and were on dialysis with healthy control subjects (Table 1) (11,14,20–37). All 20 studies measured HRQOL with the Short Form-36 Health Survey (SF-36), the most widely used and validated measure of HRQOL in the nephrology literature (38). Eighteen studies provided data to calculate an ES for HRQOL in ESRD versus healthy non-ESRD control subjects (Table 1) (11,14,20–29,31–35,37). The largest ES for ESRD was in the physical function (weighted ES 1.4) and general health (1.2) scales, followed by the role-physical (1.0) and vitality (0.73) scales. Ten studies measured ES in the physical component score (PCS) and mental component score (MCS) (11,20–22,26–28,31,32). The weighted mean estimates were 1.2 and 0.4 for PCS and MCS, respectively.


View this table:
[in this window]
[in a new window]

  		Table 1. Studies comparing HRQOL in patients with ESRD versus healthy control subjects: Cross-sectional ES between groupsa

 
Results Stratified by Traditional Biomarkers
Dialysis Adequacy.  We identified 19 studies that included more than 1500 patients and measured HRQOL stratified by Kt/V (13,22,27,32,33,35,36,38–49).

(a) Effect Sizes.  The largest HRQOL ES for Kt/V was registered in the bodily pain and vitality scales of the SF-36 (Table 2). The ES across all other scales was ≤0.2, indicating that Kt/V had a small relationship with HRQOL. Two studies measured the relationship between Kt/V and both PCS and MCS, and the relationship with Kt/V and these domains was small (ES ≤0.1 for both) (46,47). Two studies presented data using the disease-targeted Kidney Disease Quality of Life (KDQOL) instrument, a questionnaire that includes the SF-36 as a generic core surrounded by disease-specific scales (46,48). The ES was ≤0.2 for all disease-targeted HRQOL scales (46,48).


View this table:
[in this window]
[in a new window]

  		Table 2. Summary results of HRQOL data by biomarkera

 
(b) Correlation Coefficients.  We identified 48 analyses, within 14 studies, that measured the correlation between Kt/V and SF-36 scale scores (13,22,27,32,33,35,39–45). The weighted mean r value between Kt/V and SF-36 scores was 0.1 (95% CI –0.02 to 0.22), a NS relationship. In contrast, Korevaar et al. (50) found significant correlations between Kt/V and several scales of the KDQOL, including the symptom scale (r = 0.23), effects of kidney disease scale (0.27), burden of kidney disease scale (0.36), work status scale (0.31), and cognitive function scale (0.22). There was no significant relationship between Kt/V and social, sexual, or sleep function on the KDQOL (50).

Anemia.  There are extensive data linking anemia to mortality and HRQOL decrements in hemodialysis (51–59). We identified 21 studies that includes more than 2000 patients and measured the relationship between hematocrit (Hct) levels and HRQOL (13,22,27,30–32,35,39,43,45,59–68).

(a) Effect Sizes.  Four studies provided HRQOL ES data using the SF-36 instrument (65–68). Hct had the largest HRQOL ES relationship with the mental health scale (ES 0.33), followed by the role-physical (0.24) and bodily pain/physical function (both 0.23) scales. The ES across all other scales was ≤0.2 (Table 2).

(b) Correlation Coefficients.  We identified 74 analyses, contained within 16 studies, that measured the correlation between Hct and SF-36 scale scores (13,22,27,30,31,35,36,39,43,45,59,61–64). The weighted mean r value between Hct and SF-36 scale scores was 0.15 (95% CI 0.13 to 0.17). The largest weighted mean correlations were recorded in the physical function, vitality, and physical component scores. We identified two studies that measured the correlations between Hct and KDQOL scores (9,29). Vazquez et al. (9) found that Hct correlated significantly with patient satisfaction, effects of kidney disease, and quality of social interaction scales of the KDQOL; however, Hct did not significantly correlate with the cognitive function, sexual function, sleep, social support, symptom, or burden of kidney disease scales (9). In contrast, Carmichael et al. (29) found a statistically significant adjusted correlation between Hct and the quality of social interaction scale of the KDQOL (r = 0.21) but no significant correlations with any other disease-targeted scales.

Nutritional Biomarkers.  Of the commonly measured nutritional and anthropomorphic biomarkers in ESRD, serum albumin, serum creatinine, and body mass index (BMI) have the most data linking each to HRQOL.

(a) Serum Albumin.  We identified 17 studies that included more than 4000 patients and measured the relationship between serum albumin levels and HRQOL in ESRD (10,13,21,22,26,27,30,32,36,39,40,43,45,59,69–71).

  1. Effect Sizes. We identified only one study that provided HRQOL ES data stratified by serum albumin. Allen et al. (26) documented moderate to large ES of albumin in the PCS (ES 0.8) and MCS (ES 0.4) when dichotomizing albumin as <3.5 versus >3.5 g/dl.
  2. Correlation Coefficients. We identified 59 analyses, contained within 16 studies, that measured the correlation between serum albumin and SF-36 scale scores (13,21,22,26,27,30,32,36,39,40,43,45,59,69–71). The weighted mean r value between albumin and SF-36 scale scores was 0.15 (95% CI 0.05 to 0.25). The largest weighted mean correlations were recorded in the physical function, mental health, and PCS (Table 2). Kurella et al. (10) provided further data correlating albumin with disease-targeted HRQOL, using the KDQOL cognitive function subscale as their outcome. The authors documented a highly significant adjusted correlation between albumin and the KDQOL subscale (r = 0.30; P = 0.007).

(b) Serum Creatinine.  Allen et al. (26) measured ES data stratified by low (<83 mg/L) versus high (≥122 mg/L) serum creatinine levels in a cross-sectional analysis of 1545 dialysis patients enrolled in the Hemodialysis (HEMO) study. The authors found that patients with low creatinine had a significantly lower adjusted SF-36 PCS score versus patients with higher levels (ES 2.0). In contrast, the relationship between creatinine and MCS was NS. We identified six studies that measured correlation coefficients for the relationship between serum creatinine and SF-36 scores in ESRD (13,31,40,61,72,73). The weighted mean r value between creatinine and SF-36 scale scores was 0.29 (95% CI 0.21 to 0.37). The largest correlations were recorded in the general health, mental health, and bodily pain scales (Table 2).

(c) Body Mass Index.  We identified four studies that measured the relationship between BMI and HRQOL in ESRD (36,45,73,74). In a cross-sectional study of 65 patients with ESRD, Kalantar-Zadeh et al. (73) documented highly significant independent correlations between BMI and both PCS (r = –0.35) and MCS (r = –0.31) scores of the SF-36 after adjusting for potential confounders. Similarly, in a study of 75 dialysis patients, Goller et al. (36) found a significant relationship between BMI and SF-36 physical function scores. Kusek et al. (74) documented a significant relationship with PCS but not MCS. In contrast, Mingardi et al. (45) were unable to document significant relationships between BMI and any SF-36 domains.

Mineral Metabolism Biomarkers
Calcium-Phosphorous Product and Serum Calcium.  We identified one study that linked calcium-phosphorous product (Ca x P) to HRQOL data (75). In a cross-sectional survey of more than 4000 patients with ESRD in Japan, Tanaka et al. (75) found no significant difference in mental health scores between patients with low (<42) versus high (>62) Ca x P. When treated as a linear variable, there was no significant correlation between Ca x P and SF-36 mental health scores. The same authors measured the relationship between serum corrected calcium levels and HRQOL and found that patients with high (>11) versus low (<8.4) calcium had a statistically significant difference in SF-36 mental scale scores (ES 0.22).

Parathyroid Hormone Levels.  We found four studies that measured the relationship between parathyroid hormone (PTH) and SF-36 scores in ESRD (13,45,75,76). Tanaka et al. (75) found a NS difference in mental health scores in patients with high (>600) versus low (<150) PTH levels (ES 0.1). When treated as a linear variable, PTH remained unable to predict independently SF-36 MCS or PCS scores in both a cross-sectional survey of 65 patients with ESRD in the United Kingdom and a survey of 497 patients in Taiwan (13). Similarly, Mingardi et al. (45) found that PTH did not significantly correlate with any of the SF-36 scale scores. Using the KDQOL, Klersy et al. (76) found no significant relationship between PTH and any disease-targeted HRQOL domains.

Inflammation Biomarkers
C-Reactive Protein.  We identified five analyses, contained within three studies (13,73,77), that measured the correlation between C-reactive protein (CRP) levels and SF-36 scale scores. None of the five identified analyses revealed significant correlations between CRP and any SF-36 scale.

TNF and IL-1.  We found one study that measured the relationship between either TNF or IL-1 and the SF-36 scores (77). In a cross-sectional analysis of 54 dialysis patients, Hung et al. (77) found a NS correlation between TNF and the SF-36 physical function scale. The same study measured the relationship between IL-1 and physical function scale scores and also found no significant correlation.


   Discussion
Top
 Abstract
 Introduction
 Materials and Methods
 Results
 Discussion
Conclusions
 Technical Appendix
 Disclosures
 References
 
Although monitoring biomarkers is central to the successful treatment of patients with ESRD, it is necessary but insufficient to understand fully patients’ overall burden of illness. Data indicate that HRQOL is a consistent and powerful predictor of overall mortality in ESRD, on par with the predictive ability of traditional biomarkers such as Kt/V (14). Thus, it is important to complement biologic data with information about patients’ overall HRQOL, yet, with few exceptions (78), adoption of routine HRQOL monitoring in everyday clinical practice remains suboptimal. In the absence of widespread efforts to track HRQOL, an important first step is to measure formally the relationship between common biomarkers and HRQOL. We conducted this systematic review first to establish which domains of HRQOL are most affected by ESRD in general and then to pool data to measure the magnitude of relationships between traditional biomarkers, including those supported by Kidney Disease Outcomes Quality Initiative (KDOQI) guidelines and tracked by Medicare, and patient-reported HRQOL.

Our study has several key findings. First, patients with ESRD have consistently diminished HRQOL compared with matched control subjects without ESRD. Specifically, the HRQOL decrement in ESRD is most pronounced in physical function and vitality. In contrast, the impact of ESRD is least pronounced in mental health. This suggests that compared with interventions directed at improving physical HRQOL in ESRD, efforts aimed at improving mental HRQOL may be limited by a "ceiling effect," because there is relatively less room for improvement in the mental versus physical domains of HRQOL in ESRD.

Second, we found that dialysis adequacy, although a strong predictor of mortality in ESRD, is a poor predictor of HRQOL. Specifically, the pooled ES of high versus low Kt/V is <0.2 SD, a small clinical effect (18). The pooled r value of 0.1 also suggests that Kt/V has a minimal relationship with HRQOL. These findings indicate that Kt/V fails to capture key aspects of illness related to ESRD and is a poor correlate for HRQOL.

Third, mineral metabolism markers, including PTH, Ca x P, and serum calcium, have no significant relationship to HRQOL. As with Kt/V, these data indicate that mineral metabolism markers may not reliably track with the patient-reported experience of the illness. Given the consistency of evidence, it is reasonable to conclude that intervening on mineral metabolism biomarkers through therapeutic or dietary maneuvers, although important for other purposes, may not have a meaningful impact on patient-reported HRQOL; however, it is possible that a more specific HRQOL questionnaire might better capture the impact of these physiologic perturbations. Investigators studying the relationship between mineral metabolism and HRQOL might benefit from developing such a disease-targeted instrument.

Fourth, although data indicate that inflammatory markers, such as TNF, CRP, and IL-1, are consistent predictors of mortality in ESRD, their relationship to HRQOL seems minimal. This emphasizes that the mechanisms that lead to poor HRQOL may be parallel to but are different from the mechanisms that contribute to mortality in ESRD; however, our review found relatively few data linking inflammatory markers to HRQOL, suggesting that further research is necessary before making any definitive conclusions about the contributing role of inflammation to HRQOL decrements in ESRD.

Fifth, the data indicate that Hct has a small to moderate but consistent relationship with HRQOL in ESRD. When treated as a linear variable, Hct is most highly correlated with the physical function and vitality domains of the SF-36, the same domains most affected by ESRD in general (Table 1). This alignment of Hct with key HRQOL domains in ESRD is supported by data that correcting anemia can improve HRQOL, through both pharmacologic (19) and nonpharmacologic interventions. As an example of the latter, Hamilton and Hawley (79) demonstrated that HRQOL was improved for patients who had ESRD and were exposed to a nurse-run outpatient anemia management program compared with patients who received usual care; however, it must be emphasized that the relationship between Hct and HRQOL remains modest (pooled r = 0.15; ES range 0.1 to 0.34), suggesting that knowing patients’ Hct is necessary—but by no means sufficient—to capture fully overall HRQOL.

Last, our review reveals that nutritional biomarkers in ESRD, including albumin, creatinine, and BMI, are moderate to large predictors of both generic and disease-targeted HRQOL in ESRD. The impact of nutritional indices spans all physical and mental HRQOL domains, with particularly large ES in the physical component score of the SF-36. These results are important because nutritional markers are potentially modifiable and, in some instances, can be intervened on with both pharmacologic and nonpharmacologic therapies. This is supported by data indicating that directed nutritional and exercise programs can improve HRQOL for dialysis patients (66). Coupled with data that nutritional indices are among the strongest predictors of overall mortality in ESRD (80), these results emphasize that nutritional biomarkers are central to dictating both morbidity and mortality. A practical implication of these findings is that poor nutritional status should prompt a formal HRQOL assessment in affected patients, and this information should be recorded and tracked as part of everyday care to optimize treatment in these high-risk patients.

Our review has several limitations. First, we combined studies of different design, follow-up, sample size, population characteristics, and case-mix adjustment. This heterogeneity precludes any ability to perform a meta-analysis and tends to undermine any "cut-and-dry" conclusions about strict rank ordering of biomarkers. We did attempt to acknowledge heterogeneity between studies by calculating sample size–weighted means for all of our pooled estimates, a maneuver that accounts for the expectation that larger studies are theoretically more precise than smaller studies. Second, our analysis did not target other factors that may predict HRQOL in ESRD, including psychosocial variables (e.g., marital status, employment status [30,35,81]), dialysis practice variables (e.g., type and timing of dialysis [82,83]), individual comorbidities (47,84) and symptoms (e.g., poor appetite [47], insomnia), and structure/process characteristics (e.g., availability of exercise programs [66,85], use of predialysis clinics [86]), among others. These factors undoubtedly have a major impact on outcomes in dialysis; however, expanding our analysis to this range of factors is beyond the scope of our more modest exercise, which is to focus explicitly on the predictive role of biomarkers in ESRD. Third, our review did not find data regarding nonlinear relationships between biomarkers and HRQOL. Data indicate that several biomarkers in dialysis have curvilinear relationships with survival (e.g., PTH, BMI, Ca x P), but it remains unknown whether that relationship extends to HRQOL. Future research should aim to describe better the potential curvilinear relationships between dialysis biomarkers and HRQOL.


   Conclusions
Top
 Abstract
 Introduction
 Materials and Methods
 Results
 Discussion
 Conclusions
Technical Appendix
 Disclosures
 References
 
Because there is a close connection between HRQOL, morbidity, and mortality in patients with ESRD, it is important to explore which dimensions of HRQOL are most affected by ESRD and to understand how best to monitor HRQOL in individual patients. Our analysis indicates that HRQOL in ESRD is most affected in the physical domains and that nutritional biomarkers and Hct are most closely associated with these domains. In contrast, Kt/V, mineral metabolism indices, and inflammatory markers are poor correlates for HRQOL. These data suggest that, of the commonly measured biomarkers, intervening on nutritional indices, in particular, may have the best impact on overall HRQOL; however, because this conclusion is based on cross-sectional data, future research should prospectively track changes in HRQOL against longitudinal changes in key biochemical indices.


   Technical Appendix
Top
 Abstract
 Introduction
 Materials and Methods
 Results
 Discussion
 Conclusions
 Technical Appendix
Disclosures
 References
 
Systematic Review Method
We performed a systematic review of MEDLINE and EMBASE to identify English-language publications from January 1990 through September 2007. In addition, we reviewed the bibliographies of key review articles for references not captured by our search strategy. Three reviewers (B.M.R.S., G.M., and E.E.) assessed the generated titles for relevance and rejected only titles that fulfilled the following explicit exclusion criteria: (1) Not written in English, (2) not concerning a clinical question regarding humans, (3) not related to ESRD, and (4) solely related to renal posttransplantation patients and acute renal failure. The reviewers then individually assessed the relevance of all abstracts corresponding with the remaining titles and excluded abstracts for the following reasons: (1) Fulfilled one or more of the title exclusion criteria, and (2) did not make reference to one or more of the following patient-oriented outcomes: "health related quality of life," "health status," "overall health," or "preferences/utilities." The reviewers then independently assessed the relevance of all articles that corresponded with the remaining abstracts and included articles when they had data regarding HRQOL and met one of the following two criteria: (1) Presented HRQOL data stratified by presence or absence of ESRD or (2) presented variations in HRQOL across biologic measures. We measured interrater agreement for each step with a {kappa} statistic and adopted a threshold of ≥0.7 as the definition for acceptable agreement (87). Disagreements were settled by discussion and consensus. Three investigators (B.M.R.S., G.M., and E.E.) performed independent abstractions of the resulting articles and entered the data onto a standardized abstraction form. Because most all of the HRQOL data in chronic kidney disease are measured using either the SF-36 or the disease-targeted KDQOL instruments, we focused our review on studies that used these instruments. Other instruments are scattered throughout the chronic kidney disease literature but are not validated in the same way as the SF-36 or KDQOL. Moreover, studies with these alternative instruments are few and far between, making it very difficult to pool data across biomarkers using atypical instruments. Thus, we focused on the bulk of data that arose from the two most common instruments.

Overview of SF-36 Health Survey
The SF-36 instrument is organized into eight discrete scales (physical functioning, physical role limitations, emotional role limitations, bodily pain, general health, emotional well-being, energy/fatigue, and social functioning), which are compiled into two summary scores: (1) Physical component score and (2) mental component score (88). Each raw scale score is linearly transformed to a 0 to 100 scale using the following formula, with higher scores indicating better HRQOL (88):

Formula

All eight scales are scored using this same generic equation, and all are scored on a 100-point scale; therefore, all scales are proportionate with regard to interpreting changes on a scale. In other words, a change of 4.2 points on the role-physical scale is proportionately the same as a change in 4.2 points on the vitality scale, because both scales have a 100-point range; however, interpreting score differences across scales is complicated by the fact that each scale has a unique SD. In other words, a difference of 4.2 points on a scale with a wide SD may be less significant than the same difference on a scale with a narrow SD. This is precisely why the ES is used, because the ES anchors the point change on any particular scale by the SD of the scale.


   Disclosures
Top
 Abstract
 Introduction
 Materials and Methods
 Results
 Discussion
 Conclusions
 Technical Appendix
 Disclosures
References
 
S.R. is an employee of Amgen, Inc.


   Acknowledgments
 
B.M.R.S. is supported by VA HSR&D Research Career Development Award RCD 03 to 179-2 and by the CURE Research Center (NIH 2P30 DK 041301-17). Additional support for this study was provided by an investigator-initiated research grant from Amgen, Inc.

The principal investigator, B.M.R.S., maintained full control over all aspects of the study design, implementation, data collection, data analysis, data interpretation, and manuscript preparation and had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis. The contributions of individual authors is as follows:

B.M.R.S. was responsible for study design, study implementation, data collection, data analysis, data interpretation, manuscript preparation, and manuscript approval and is guarantor of article; G.M. was responsible for data collection and manuscript approval; S.R. was responsible for study design, data analysis, data interpretation, and manuscript approval; and E.E. was responsible for study design, study implementation, data collection, and manuscript approval.


   Footnotes
 
Published online ahead of print. Publication date available at www.cjasn.org.

The opinions and assertions contained herein are the sole views of the authors and are not to be construed as official or as reflecting the views of the Department of Veteran Affairs.

Received February 18, 2008. Accepted July 15, 2008.


   References
Top
 Abstract
 Introduction
 Materials and Methods
 Results
 Discussion
 Conclusions
 Technical Appendix
 Disclosures
 References
 

  1. Alebiosu CO, Ayodele OE: The global burden of chronic kidney disease and the way forward. Ethn Dis15 :418 –423,2005[Medline]
  2. White SL, Cass A, Atkins RC, Chadban SJ: Chronic kidney disease in the general population. Adv Chronic Kidney Dis12 :5 –13,2005[CrossRef][Medline]
  3. Smith S, Freelan M, Heffler D, McKusick D: The next ten years of health spending: What does the future hold? Health Aff (Millwood)17 :128 –140,1998[Abstract]
  4. US Renal Data System: USRDS 2004 Annual Data Report: Atlas of End-Stage Renal Disease in the United States, Bethesda, National Institutes of Health, National Institute of Diabetes and Digestive and Kidney Diseases,2004
  5. K/DOQI clinical practice guidelines for chronic kidney disease: Evaluation, classification, and stratification. Am J Kidney Dis39 [Suppl 1]:S1 –S266,2002[CrossRef][Medline]
  6. Hays RD, Kallich JD, Mapes DL, Coons SJ, Carter WB: Development of the kidney disease quality of life (KDQOL) instrument. Qual Life Res3 :329 –338,1994[CrossRef][Medline]
  7. Kouidi E: Health-related quality of life in end-stage renal disease patients: The effects of renal rehabilitation. Clin Nephrol61 :S60 –S71,2004[Medline]
  8. Mapes DL, Bragg-Gresham JL, Bommer J, fukuhara S, McKevitt P, Wikstrom B, Lopes AA: Health-related quality of life in the Dialysis Outcomes and Practice Patterns Study (DOPPS). Am J Kidney Dis44 [Suppl 3]:54 –60,2004[Medline]
  9. Vázquez I, Valderrábano F, Fort J, Jofré R, López-Gómez JM, Moreno F, Sanz-Guajardo D, Spanish Cooperative Renal Patients Quality of Life Study Group: Psychosocial factors and health-related quality of life in hemodialysis patients. Qual Life Res14 :179 –190,2005[CrossRef][Medline]
  10. Kurella M, Luan J, Yaffe K, Chertow GM: Validation of the Kidney Disease Quality of Life (KDQOL) cognitive function subscale. Kidney Int66 :2361 –2367,2004[CrossRef][Medline]
  11. Lowrie EG, Curtin RB, LePain N, Schatell D: Medical outcomes study short form-36: A consistent and powerful predictor of morbidity and mortality in dialysis patients. Am J Kidney Dis41 :1286 –1292,2003[CrossRef][Medline]
  12. Lopes AA, Bragg-Gresham JL, Satayathum S, McCullough K, Pifer T, Goodkin DA, Mapes DL, Young EW, Wolfe RA, Held PJ, Port FK, Worldwide Dialysis Outcomes and Practice Patterns Study Committee: Health-related quality of life and associated outcomes among hemodialysis patients of different ethnicities in the United States: The Dialysis Outcomes and Practice Patterns Study (DOPPS). Am J Kidney Dis41 :605 –615,2003[CrossRef][Medline]
  13. Kalantar-Zadeh K, Kopple JD, Block G, Humphreys MH: Association among SF36 quality of life measures and nutrition, hospitalization, and mortality in hemodialysis. J Am Soc Nephrol12 :2797 –2806,2001[Abstract/Free Full Text]
  14. DeOreo PB: Hemodialysis patient-assessed functional health status predicts continued survival, hospitalization, and dialysis-attendance compliance. Am J Kidney Dis30 :204 –212,1997[Medline]
  15. Desai AA, Garber AM, Chertow GM: Rise of pay for performance: Implications for care of people with chronic kidney disease. Clin J Am Soc Nephrol2 :1087 –1095,2007[Abstract/Free Full Text]
  16. Guyatt GH, Osoba D, Wu AW, Wyrwich KW, Norman GR: Clinical Significance Consensus Meeting Group: Methods to explain the clinical significance of health status measures. Mayo Clin Proc77 :371 –383,2002[Abstract/Free Full Text]
  17. Spiegel BM, Younossi ZM, Hays RD, Revicki D, Robbins S, Kanwal F: The impact of hepatitis C on health related quality of life: A systematic review of the literature. Hepatology41 :790 –800,2002
  18. Cohen J: Statistical Power Analysis for the Behavioral Sciences, London, Academic Press,1969
  19. Ross SD, Fahrbach K, Frame D, Scheye R, Connelly JE, Glaspy J: The effect of anemia treatment on selected health-related quality-of-life domains: A systematic review. Clin Ther25 :1786 –1805,2003[CrossRef][Medline]
  20. Perlman RL, Finkelstein FO, Liu L, Roys E, Kiser M, Eisele G, Burrows-Hudson S, Messana JM, Levin N, Rajagopalan S, Port FK, Wolfe RA, Saran R: Quality of life in chronic kidney disease (CKD): A cross-sectional analysis in the Renal Research Institute-CKD study. Am J Kidney Dis45 :658 –666,2005[CrossRef][Medline]
  21. Covic A, Seica A, Gusbeth-Tatomir P, Gavrilovici O, Goldsmith DJ: Illness representations and quality of life scores in haemodialysis patients. Nephrol Dial Transplant19 :2078 –2083,2004[Abstract/Free Full Text]
  22. Molsted S, Aadahl M, Schou L, Eidemak I: Self-rated health and employment status in chronic haemodialysis patients. Scand J Urol Nephrol38 :174 –178,2004[CrossRef][Medline]
  23. Pucheu S, Consoli SM, D’Auzac C, Francais P, Issad B: Do health causal attributions and coping strategies act as moderators of quality of life in peritoneal dialysis patients? J Psychosom Res56 :317 –322,2004[CrossRef][Medline]
  24. Fukuhara S, Lopes AA, Bragg-Gresham JL, Kurokawa K, Mapes DL, Akizawa T, Bommer J, Canaud BJ, Port FK, Held PJ, Worldwide Dialysis Outcomes and Practice Patterns Study: Worldwide Dialysis Outcomes and Practice Patterns Study: Health-related quality of life among dialysis patients on three continents—The Dialysis Outcomes and Practice Patterns Study. Kidney Int64 :1903 –1910,2003[CrossRef][Medline]
  25. Perneger TV, Leski M, Chopard-Stoermann C, Martin PY: Assessment of health status in chronic hemodialysis patients. J Nephrol16 :252 –259,2003[CrossRef][Medline]
  26. Allen KL, Miskulin D, Yan G, Dwyer JT, Frydrych A, Leung J, Poole D, Hemodialysis (HEMO) Study Group: Association of nutritional markers with physical and mental health status in prevalent hemodialysis patients from the HEMO study. J Ren Nutr12 :160 –169,2002[CrossRef][Medline]
  27. Mittal SK, Ahern L, Flaster E, Maesaka JK, Fishbane S: Self-assessed physical and mental function of haemodialysis patients. Nephrol Dial Transplant16 :1387 –1394,2001[Abstract/Free Full Text]
  28. Johansen KL, Painter P, Kent-Braun JA, Ng AV, Carey S, Da Silva M, Chertow GM: Validation of questionnaires to estimate physical activity and functioning in end-stage renal disease. Kidney Int59 :1121 –1127,2001[CrossRef][Medline]
  29. Carmichael P, Popoola J, John I, Stevens PE, Carmichael AR: Assessment of quality of life in a single centre dialysis population using the KDQOL-SF questionnaire. Qual Life Res9 :195 –205,2000[CrossRef][Medline]
  30. Blake C, Codd MB, Cassidy A, O’Meara YM: Physical function, employment and quality of life in end-stage renal disease. J Nephrol13 :142 –149,2000[Medline]
  31. Rebollo P, Ortega F, Baltar JM, Badía X, Alvarez-Ude F, Díaz-Corte C, Naves M, Navascúes RA, Ureña A, Alvarez-Grande J: Health related quality of life (HRQOL) of kidney transplanted patients: Variables that influence it. Clin Transplant14 :199 –207,2000[CrossRef][Medline]
  32. Merkus MP, Jager KJ, Dekker FW, de Haan RJ, Boeschoten EW, Krediet RT: Physical symptoms and quality of life in patients on chronic dialysis: Results of the Netherlands Cooperative Study on Adequacy of Dialysis (NECOSAD) Nephrol Dial Transplant14 :1163 –1170,1999[Abstract/Free Full Text]
  33. Rebollo P, Ortega F, Baltar JM, Díaz-Corte C, Navascués RA, Naves M, Ureña A, Badía X, Alvarez-Ude F, Alvarez-Grande J: Health-related quality of life (HRQOL) in end stage renal disease (ESRD) patients over 65 years. Geriatr Nephrol Urol8 :85 –94,1998[CrossRef][Medline]
  34. Sloan RS, Kastan B, Rice SI, Sallee CW, Yuenger NJ, Smith B, Ward RA, Brier ME, Golper TA: Quality of life during and between hemodialysis treatments: Role of L-carnitine supplementation. Am J Kidney Dis Aug32 :265 –272,1998[CrossRef]
  35. Merkus MP, Jager KJ, Dekker FW, Boeschoten EW, Stevens P, Krediet RT: Quality of life in patients on chronic dialysis: Self-assessment 3 months after the start of treatment. The Necosad Study Group. Am J Kidney Dis29 :584 –592,1997[Medline]
  36. Goller JL, McMahon JM, Rutledge C, Walker RG, Wood SE: Dialysis adequacy and self-reported health status in a group of CAPD patients. Adv Perit Dial13 :128 –133,1997[Medline]
  37. Loos C, Briancon S, Frimat L, Hanesse B, Kessler M: Effect of end-stage renal disease on the quality of life of older patients. J Am Geriatr Soc51 :229 –233,2003[CrossRef][Medline]
  38. Wight JP, Edwards L, Brazier J, Walters S, Payne JN, Brown CB: The SF36 as an outcome measure of services for end stage renal failure. Qual Health Care7 :209 –221,1998[Abstract]
  39. Morsch CM, Goncalves LF, Barros E: Health-related quality of life among haemodialysis patients: Relationship with clinical indicators, morbidity and mortality. J Clin Nurs15 :498 –504,2006[CrossRef][Medline]
  40. Chiang CK, Peng YS, Chiang SS, Yang CS, He YH, Hung KY, Wu KD, Wu MS, Fang CC, Tsai TJ, Chen WY: Health-related quality of life of hemodialysis patients in Taiwan: a multicenter study. Blood Purif22 :490 –498,2004[CrossRef][Medline]
  41. Chang ST, Chen CL, Chen CC, Lin FC, Wu D: Enhancement of quality of life with adjustment of dry weight by echocardiographic measurement of inferior vena cava diameter in patients undergoing chronic hemodialysis. Nephron Clin Pract97 :c90 –c97,2004[CrossRef][Medline]
  42. Hamilton G, Locking-Cusolito H: Hemodialysis adequacy and quality of life: How do they relate? CANNT J13 :24 –29,2003[Medline]
  43. Iliescu EA, Coo H, McMurray MH, Meers CL, Quinn MM, Singer MA, Hopman WM: Quality of sleep and health related quality of life in haemodialysis patients. Nephrol Dial Transplant18 :126 –132,2003[Abstract/Free Full Text]
  44. Bakewell AB, Higgins RM, Edmunds ME: Quality of life in peritoneal dialysis patients: Decline over time and association with clinical outcomes. Kidney Int61 :239 –248,2002[CrossRef][Medline]
  45. Mingardi G, Cornalba L, Cortinovis E, Ruggiate R, Mosconi P, Apolone G: Health-related quality of life in dialysis patients: A report from an Italian study using the SF-36 Health Survey. DIA-QOL Group. Nephrol Dial Transplant14 :1503 –1510,1999[Abstract/Free Full Text]
  46. Unruh M, Benz R, Greene T, Yan G, Beddhu S, DeVita M, Dwyer JT, Kimmel PL, Kusek JW, Martin A, Rehm-McGillicuddy J, Teehan BP, Meyer KB,HEMO Study Group: Effects of hemodialysis dose and membrane flux on health-related quality of life in the HEMO Study. Kidney Int66 :355 –366,2004[CrossRef][Medline]
  47. Dwyer JT, Larive B, Leung J, Rocco M, Burrowes JD, Chumlea WC: Nutritional status affects quality of life in Hemodialysis (HEMO) Study patients at baseline. J Ren Nutr12 :213 –223,2002[CrossRef][Medline]
  48. Manns BJ, Johnson JA, Taub K, Mortis G, Ghali WA, Donaldson C: Dialysis adequacy and health related quality of life in hemodialysis patients. ASAIO J48 :565 –569,2002[CrossRef][Medline]
  49. Chen YC, Hung KY, Kao TW, Tsai TJ, Chen WY: Relationship between dialysis adequacy and quality of life in long-term peritoneal dialysis patients. Perit Dial Int20 :534 –540,2000[Abstract/Free Full Text]
  50. Korevaar JC, Merkus MP, Jansen MA, Dekker FW, Boeschoten EW, Krediet RT, NECOSAD study group: Validation of the KDQOL-SF: A dialysis-targeted health measure. Qual Life Res11 :437 –447,2002[CrossRef][Medline]
  51. Avram MM, Blaustein D, Fein PA, Goel N, Chattopadhyay J, Mittman N: Hemoglobin predicts long-term survival in dialysis patients: a 15-year single-center longitudinal study and a correlation trend between prealbumin and hemoglobin. Kidney Int SupplS6 –S11,2003
  52. Collins AJ, Li S, St Peter W, Ebben J, Roberts T, Ma JZ, Manning W: Death, hospitalization, and economic associations among incident hemodialysis patients with hematocrit values of 36 to 39%. J Am Soc Nephrol12 :2465 –2473,2001[Abstract/Free Full Text]
  53. Li S, Collins AJ: Association of hematocrit value with cardiovascular morbidity and mortality in incident hemodialysis patients. Kidney Int65 :626 –633,2004[CrossRef][Medline]
  54. Locatelli F, Pisoni RL, Combe C, Bommer J, Andreucci VE, Piera L, Greenwood R, Feldman HI, Port FK, Held PJ: Anaemia in haemodialysis patients of five European countries: Association with morbidity and mortality in the Dialysis Outcomes and Practice Patterns Study (DOPPS). Nephrol Dial Transplant19 :121 –132,2004[Abstract/Free Full Text]
  55. Ma JZ, Ebben J, Xia H, Collins AJ: Hematocrit level and associated mortality in hemodialysis patients. J Am Soc Nephrol10 :610 –619,1999[Abstract/Free Full Text]
  56. Ofsthun N, Labrecque J, Lacson E, Keen M, Lazarus JM: The effects of higher hemoglobin levels on mortality and hospitalization in hemodialysis patients. Kidney Int63 :1908 –1914,2003[CrossRef][Medline]
  57. Pisoni RL, Bragg-Gresham JL, Young EW, Akizawa T, Asano Y, Locatelli F, Bommer J, Cruz JM, Kerr PG, Mendelssohn DC, Held PJ, Port FK: Anemia management and outcomes from 12 countries in the Dialysis Outcomes and Practice Patterns Study (DOPPS). Am J Kidney Dis44 :94 –111,2004[CrossRef][Medline]
  58. Roberts TL, Foley RN, Weinhandl ED, Gilbertson DT, Collins AJ: Anaemia and mortality in haemodialysis patients: Interaction of propensity score for predicted anaemia and actual haemoglobin levels. Nephrol Dial Transplant200621 :652 –662
  59. Robinson BM, Joffe MM, Berns JS, Pisoni RL, Port FK, Feldman HI: Anemia and mortality in hemodialysis patients: Accounting for morbidity and treatment variables updated over time. Kidney Int68 :2323 –2330,2005[CrossRef][Medline]
  60. Gorodetskaya I, Zenios S, McCulloch CE, Bostrom A, Hsu CY, Bindman AB, Go AS, Chertow GM: Health-related quality of life and estimates of utility in chronic kidney disease. Kidney Int68 :2801 –2808,2005[CrossRef][Medline]
  61. Arogundade FA, Zayed B, Daba M, Barsoum RS: Correlation between Karnofsky Performance Status Scale and Short-Form Health Survey in patients on maintenance hemodialysis. J Natl Med Assoc96 :1661 –1667,2004[Medline]
  62. Frank A, Auslander GD, Weissgarten J: Quality of life of patients with end-stage renal disease at various stages of the illness. Soc Work Health Care38 :1 –27,2003[Medline]
  63. Neto JF, Farraz MB, Cendoroglo M, Draibe S, Yu L, Sesso R: Quality of life at the initiation of maintenance dialysis treatment: A comparison between the SF-36 and the KDQ questionnaires. Qual Life Res9 :101 –107,2000[CrossRef][Medline]
  64. Revicki DA, Brown RE, Feeny DH, Henry D, Teehan BP, Rudnick MR, Benz RL: Health-related quality of life associated with recombinant human erythropoietin therapy for predialysis chronic renal disease patients. Am J Kidney Dis25 :548 –554,1995[Medline]
  65. Lee SY, Lee JH, Kim YK, Kim SH, Kim L, Lee MS, Joe SH, Jung IK, Suh KY, Kim HK: Neurocognitive function and quality of life in relation to hematocrit levels in chronic hemodialysis patients. J Psychosom Res57 :5 –10,2004[CrossRef][Medline]
  66. Painter P, Moore G, Carlson L, Paul S, Myll J, Phillips W, Haskell W: Effects of exercise training plus normalization of hematocrit on exercise capacity and health-related quality of life. Am J Kidney Dis39 :257 –265,2002[Medline]
  67. Parfrey PS, Foley RN, Wittreich BH, Sullivan DJ, Zagari MJ, Frei D: Double-blind comparison of full and partial anemia correction in incident hemodialysis patients without symptomatic heart disease. J Am Soc Nephrol16 :2180 –2189,2005[Abstract/Free Full Text]
  68. Plantinga LC, Fink NE, Jaar BG, Huang IC, Wu AW, Meyer KB, Powe NR: Relation between level or change of hemoglobin and generic and disease-specific quality of life measures in hemodialysis. Qual Life Res16 :755 –765,2007[CrossRef][Medline]
  69. Caskey FJ, Wordsworth S, Ben T, de Charro FT, Delcroix C, Dobronravov V, van Hamersvelt H, Henderson I, Kokolina E, Khan IH, Ludbrook A, Luman M, Prescott GJ, Tsakiris D, Barbullushi M, MacLeod AM, EURODICE group: Early referral and planned initiation of dialysis: What impact on quality of life? Nephrol Dial Transplant18 :1330 –1338,2003[Abstract/Free Full Text]
  70. Harris LE, Luft FC, Rudy DW, Tierney WM: Clinical correlates of functional status in patients with chronic renal insufficiency. Am J Kidney Dis21 :161 –166,1993[Medline]
  71. Vasilieva IA: Quality of life in chronic hemodialysis patients in Russia. Hemodial Int10 :274 –278,2006[CrossRef][Medline]
  72. Fujisawa M, Ichikawa Y, Yoshiya K, Isotani S, Higuchi A, Nagano S, Arakawa S, Hamami G, Matsumoto O, Kamidono S: Assessment of health-related quality of life in renal transplant and hemodialysis patients using the SF-36 health survey. Urology56 :201 –206,2000[CrossRef][Medline]
  73. Kalantar-Zadeh K, Kuwae N, Wu DY, Shantouf RS, Fouque D, Anker SD, Block G, Kopple JD: Associations of body fat and its changes over time with quality of life and prospective mortality in hemodialysis patients. Am J Clin Nutr83 :202 –210,2006[Abstract/Free Full Text]
  74. Kusek JW, Greene P, Wang SR, Beck G, West D, Jamerson K, Agodoa LY, Faulkner M, Level B: Cross-sectional study of health-related quality of life in African Americans with chronic renal insufficiency: The African American Study of Kidney Disease and Hypertension Trial. Am J Kidney Dis39 :513 –524,2002[Medline]
  75. Tanaka M, Yamazaki S, Yahashino Y, Fukuhara S, Akiba T, Saito A, Asano Y, Port FK, Kurokawa K, Akizawa T: Hypercalcaemia is associated with poor mental health in haemodialysis patients: Results from Japan DOPPS. Nephrol Dial Transplant22 :1658 –1664,2007[Abstract/Free Full Text]
  76. Klersy C, Callegari A, Giorgi I, Sepe V, Efficace E, Politi P, Pavia Working Group on QoL in Organ Transplant: Italian translation, cultural adaptation and validation of KDQOL-SF, version 1.3, in patients with severe renal failure. J Nephrol20 :43 –51,2007[CrossRef][Medline]
  77. Hung AM, Chertow GM, Young BS, Carey S, Johansen KL: Inflammatory markers are unrelated to physical activity, performance, and functioning in hemodialysis. J Ren Nutr12 :170 –176,2002[CrossRef][Medline]
  78. Meyer KB, Espindle DM, DeGiacomo JM, Jenuleson CS, Kurtin PS, Davies AR: Monitoring dialysis patients’ health status. Am J Kidney Dis24 :267 –279,1994[Medline]
  79. Hamilton R, Hawley S: Quality of life outcomes related to anemia management of patients with chronic renal failure. Clin Nurse Spec20 :139 –143,2006[CrossRef][Medline]
  80. Tentori F, Hunt WC, Rohrscheib M, Zhu M, Stidley CA, Servilla K, Miskulin D, Meyer KB, Bedrick EJ, Johnson HK, Zager PG: Which targets in clinical practice guidelines are associated with improved survival in a large dialysis organization? J Am Soc Nephrol18 :2377 –2384,2007[Abstract/Free Full Text]
  81. van Manen JG, Korevaar JC, Dekker FW, Reuselaars MC, Boeschoten EW, Krediet RT, NECOSAD Study Group: Netherlands Cooperative Study on Adequacy of Dialysis: Changes in employment status in end-stage renal disease patients during their first year of dialysis. Perit Dial Int21 :595 –601,2001[Abstract/Free Full Text]
  82. Manns B, Johnson JA, Taub K, Mortis G, Ghali WA, Donaldson C: Quality of life in patients treated with hemodialysis or peritoneal dialysis: What are the important determinants? Clin Nephrol60 :341 –351,2003[Medline]
  83. Korevaar JC, Jansen MA, Dekker FW, Boeschoten EW, Bossuyt PM, Krediet RT, NECOSAD Study Group: Netherlands Cooperative Study on the Adequacy of Dialysis-2: Evaluation of DOQI guidelines: Early start of dialysis treatment is not associated with better health-related quality of life. National Kidney Foundation-Dialysis Outcomes Quality Initiative. Am J Kidney Dis39 :108 –115,2002[Medline]
  84. Martinez-Castelao A, Gorriz JL, Garcia-Lopez F, López-Revuelta K, De Alvaro F, Cruzado JM, Spanish CALVIDIA Study Group: Perceived health-related quality of life and comorbidity in diabetic patients starting dialysis (CALVIDIA study). J Nephrol17 :544 –551,2004[Medline]
  85. Levendoglu F, Altintepe L, Okudan N, Ugurlu H, Gökbel H, Tonbul Z, Güney I, Türk S: A twelve week exercise program improves the psychological status, quality of life and work capacity in hemodialysis patients. J Nephrol17 :826 –832,2004[Medline]
  86. White CA, Pilkey RM, Lam M, Holland DC: Pre-dialysis clinic attendance improves quality of life among hemodialysis patients. BMC Nephrol3 :3 ,2002[CrossRef][Medline]
  87. Landis JR, Koch GG: The measurement of observer agreement for categorical data. Biometrics,33 :159 –174,1977[CrossRef][Medline]
  88. Ware JE, Sherbourne CD: A 36-item short-form health survey (SF-36): Conceptual framework and item selection. Med Care30 :473 –483,1992[Medline]




This Article
Right arrow Abstract Freely available
Right arrow Full Text (PDF)
Right arrow All Versions of this Article:
CJN.00820208v1
3/6/1759    most recent
Right arrow Alert me when this article is cited
Right arrow Alert me if a correction is posted
Right arrow Citation Map
Services
Right arrow Email this article to a friend
Right arrow Similar articles in this journal
Right arrow Similar articles in PubMed
Right arrow Alert me to new issues of the journal
Right arrow Download to citation manager
Right arrow reprints & permissions
Citing Articles
Right arrow Citing Articles via Google Scholar
Google Scholar
Right arrow Articles by Spiegel, B. M.R.
Right arrow Articles by Esrailian, E.
Right arrow Search for Related Content
PubMed
Right arrow PubMed Citation
Right arrow Articles by Spiegel, B. M.R.
Right arrow Articles by Esrailian, E.

HOME HELP FEEDBACK SUBSCRIPTIONS ARCHIVE SEARCH TABLE OF CONTENTS