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Published ahead of print on June 8, 2006
Clin J Am Soc Nephrol 1: 802-810, 2006
© 2006 American Society of Nephrology
doi: 10.2215/CJN.00710206
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Epidemiology and Outcomes

Sleep Quality and Its Correlates in the First Year of Dialysis

Mark L. Unruh*, Daniel J. Buysse{dagger}, Mary Amanda Dew{dagger}, Idris V. Evans{ddagger}, Albert W. Wu§, Nancy E. Fink§, Neil R. Powe§, Klemens B. Meyer||; for the Choices for Healthy Outcomes in Caring for End-Stage Renal Disease (CHOICE) Study

* Renal-Electrolyte Division, University of Pittsburgh Medical Center, and {dagger} Department of Psychiatry, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania; {ddagger} Epidemiology Data Center, University of Pittsburgh, Pittsburgh, Pennsylvania; § Welch Center for Prevention, Epidemilogy, and Clinical Research, Johns Hopkins University, Baltimore, Maryland; and || Division of Nephrology, Tufts-New England Medical Center, Boston, Massachusetts

Address correspondence to: Dr. Mark L. Unruh, University of Pittsburgh Medical Center, Renal-Electrolyte Division, 3550 Terrace Street, A909 Scaife Hall, Pittsburgh, PA 15261. Phone: 412-647-2561; Fax: 412-647-6891; E-mail: unruh{at}pitt.edu


   Abstract
Top
 Abstract
Introduction
 Materials and Methods
 Results
 Discussion
 Conclusion
 References
 
Although sleep problems are thought to be prevalent among patients who undergo dialysis, there is only limited information on the determinants of sleep quality and the change in sleep quality during the first year of dialysis treatment. This report uses data from a national cohort study of incident hemodialysis and peritoneal dialysis patients to identify the correlates of sleep quality and to determine the extent to which sleep quality is related to patients’ health-related quality of life and survival. This report includes 909 incident dialysis patients who responded to questions about sleep quality. Three quarters of incident dialysis patients reported impaired sleep, and 14% had a decline in sleep quality in the first year of treatment. Poor sleep quality was significantly related to black race, higher serum phosphate, current smoking, benzodiazepine prescription, and complaints of severe restless legs. Poor baseline sleep quality was associated with lower SF-36 physical and mental component summary scores, vitality scores, and bodily pain scores (all P < 0.001). Younger age, current smoking, and benzodiazepine prescription were associated with decreases in sleep quality at 1 yr. There was no association between baseline sleep quality and survival; however, a decline in sleep quality during the first year on dialysis was associated with shorter survival (hazard ratio 1.44; 95% confidence interval 1.13 to 1.83; P = 0.003). Future work should examine the link between sleep quality and daytime functioning in the kidney failure population and the extent to which improving sleep quality will improve dialysis patient outcomes.


   Introduction
Top
 Abstract
 Introduction
Materials and Methods
 Results
 Discussion
 Conclusion
 References
 
Self-reported sleep problems are strongly associated with dialysis patients’ disability days, health care utilization, and health-related quality of life (HRQOL) (1). In the general population, those who report poor sleep quality use more health services (2), more hypnotic drugs (3), report reduced functional capabilities and lower HRQOL (4). In addition, studies in the general population have found that self-reported long and short sleepers have a higher risk for death and disability when compared with individuals who report average sleep duration (5,6). Those with ESRD have been shown to have high rates of sleep apnea, periodic limb movements, insomnia, and restless legs syndrome (7–10). Because sleep problems are associated with lower HRQOL (11) and may be related to progression of cardiovascular disease (12), identifying and treating dialysis patients who sleep poorly may significantly improve their lives (13,14).

Although many chronic dialysis patients complain of poor sleep, we know little about the natural history of sleep problems as patients adjust to maintenance dialysis, and the causes of these problems are uncertain: Dialysis patients have many physiologic disorders that might explain disordered sleep (15). A number of studies on ESRD have demonstrated the importance of sleep quality using individual items rather than using a scale score to assess sleep complaints (16–18), and most reports have been limited to cross-sectional analysis that represents the experience of a single clinical practice (11,19,20). Because both race and comorbidity influence self-reported sleep quality, it is particularly important to study sleep quality in a diverse, nationally representative population in which the burden of comorbid illness is well described (21).

This study uses data from the Choices for Healthy Outcomes in Caring for End-Stage Renal Disease (CHOICE) Study to assess sleep quality among incident peritoneal dialysis (PD) and hemodialysis (HD) patients. In this report, we first examine the predictors of sleep quality among the dialysis participants in CHOICE at baseline and then determine the factors that are related to a longitudinal change in sleep quality. Last, we examine the relationship of sleep quality to HRQOL and survival.


   Materials and Methods
Top
 Abstract
 Introduction
 Materials and Methods
Results
 Discussion
 Conclusion
 References
 
Study Design and Population
The study patients were a subpopulation of patients who participated in CHOICE (22). CHOICE is a national, prospective cohort study of incident HD and PD patients. From October 1995 to June 1998, 1041 (764 HD and 277 PD) patients were enrolled from 81 dialysis clinics associated with Dialysis Clinic, Incorporated (Nashville, TN), New Haven CAPD (New Haven, CT), and the Hospital of St. Raphael (New Haven, CT). All study participants were incident patients who had kidney failure and were starting outpatient dialysis, were 18 yr or older, and spoke English or Spanish. Patients were enrolled a median of 45 d from initiation of chronic dialysis (98% within 4 mo). Home HD patients were excluded from the study. Approximately two thirds of eligible patients were enrolled; these patients were similar to nonenrolled patients with regard to gender and age. Our study included participants who responded to the CHOICE Health Experience Questionnaire (CHEQ) and completed at least the items regarding sleep quality (n = 909). Participants who did not respond to the CHEQ (n = 113) or who had incomplete information on sleep quality items (n = 19) were excluded. The study protocol was approved by the Johns Hopkins University School of Medicine Institutional Review Board.

Baseline Data Collection
Demographic characteristics, primary cause of kidney failure, and date of first chronic dialysis were ascertained from the Health Care Financing Administration Medical Evidence Form (Form 2728), which was completed at initiation of chronic dialysis. Data regarding health behaviors were collected from the baseline questionnaire. Laboratory values for serum albumin, creatinine, phosphate, and hematocrit were collected monthly, and the average value for the first 3 mo on dialysis was calculated. Comorbidity was measured using the Index of Coexistent Disease (ICED), a composite scoring system based on 19 medical and 11 physical impairment categories (23–25). Study participants’ medication profiles were obtained by review of computerized medical records and by chart review.

For this report, race was categorized as black or nonblack (including white, Hispanic, and Asian). This categorization was used to account for the small numbers of Hispanic and Asian CHOICE participants. Dialysis modality at baseline was defined as the modality used the majority of time within 90 d of study enrollment (22). The ICED medical subscales were used to classify those with both type 1 and type 2 diabetes; we identified insulin-using patients with diabetes to create a population of participants with chronic exposure to hyperglycemia and a high risk for complications. We also examined diabetes by categorizing all those who were using oral hypoglycemic agents or insulin as having diabetes compared with both those with no diabetes and those whose diabetes was managed with diet.

Sleep Quality Assessment
The development of the CHEQ instrument that contains the sleep quality domain was based on a structured literature review; content analysis of five focus groups with HD and PD patients, nephrologists, and other providers; a survey of 110 dialysis providers; and a semistructured survey of 25 patients with ESRD on the effects of dialysis on functioning and HRQOL (26). The CHEQ instrument was refined further by a representative sample of 136 dialysis patients rating each item for frequency and bother. The final three items assess sleep initiation ("Have trouble falling asleep?"), sleep maintenance ("Awaken during sleep and have trouble falling asleep again?"), and sleep adequacy ("Get enough sleep to feel rested upon waking in the morning?"). The response set for these items were all the time, most of the time, good bit of the time, some of the time, a little of the time, and none of the time. In this population, there was no comparison with polysomnography for external validation; however, the sleep quality scale demonstrated adequate internal consistency reliability as measured by the Cronbach {alpha} of 0.75, and its construct validity was described previously (26). In the CHEQ sleep quality scale, a higher score reflects better sleep quality.

HRQOL Domains
The SF-36 Version 1 physical component summary score (PCS), the mental component summary (MCS) score, the vitality scale, and the bodily pain scale were scored and analyzed. The PCS and MCS were chosen as global measurements of physical and mental well-being and are scored in such a way that 50 is the average score in the general population, with an SD of 10 points. We selected the SF-36 subscales of vitality and bodily pain because we thought that these domains were related directly to sleep quality and because we had previously demonstrated their association with symptoms of restless legs (27). For these subscales of pain and vitality, the minimum score is 0 and the maximum score is 100, with higher scores reflecting better health (28).

Selection of Covariates
The selection of covariates was guided by factors that were found to be associated with sleep quality in the general population and among dialysis patients (29). (1) Demographics: Sleep quality has been associated with age, gender, and race (21,30–32). It also is likely that socioeconomic factors affect sleep, an effect that possibly is mediated by stress (33). Therefore, our study tested marital status, employment, and level of education. (2) Lifestyle factors: It was demonstrated previously that current smoking, alcohol use, and exercise can affect sleep quality (34,35). (3) Dialysis treatment–related factors: A number of studies have examined clinical (mode of dialysis, dose of dialysis, cause of ESRD) (36,37) and laboratory (hematocrit, serum albumin, serum creatinine, serum phosphate) factors (32). (4) Psychologic factors: The SF-36 MCS score was used to assess the role of psychologic well-being in sleep quality. (5) Comorbid disease: Patients who undergo dialysis often have substantial comorbidity and exhibit elevated levels of inflammatory markers (38,39), both of which have been associated with reduced sleep quality (40). Therefore, our models examined diabetes; the cause of kidney failure; comorbidity as measured by ICED (21); and fibrinogen, IL-6, and C-reactive protein levels. (6) Sleep disorders: Sleep quality has been associated with symptoms of restless legs (27,30,41).

Statistical Analyses
Baseline demographic, socioeconomic, and laboratory factors are described as means (SD) for continuous variables and as frequency distributions for categorical variables. Statistical significance of the differences between sleep category groups was tested using ANOVA for continuous variables and {chi}2 tests for categorical variables.

We created an ordinal logistic regression model to identify factors that were associated independently with the sleep quality at the onset of chronic outpatient dialysis therapy. Model selection was based on variables that were found to have a relationship (P < 0.10) to sleep quality in the bivariate analysis and included major demographic factors. The entire set of potential covariates was examined for potential multicollinearity using principal components, followed by model selection using backward stepwise covariate selection. The proportional odds assumption was examined using the score test.

To assess the relationship between sleep quality and HRQOL, we performed separate mixed regression analyses using robust variance to account for the effects of clinic. These analyses were performed to estimate the association between sleep category and MCS, PCS, vitality, and pain scores. In all these models, the sleep category was used as an independent variable, along with possible confounding factors.

In longitudinal analysis, we defined a statistically significant individual change in sleep quality as a change in score that exceeded 2 SEM (42). We calculated the SEM as SD x (1 – R)1/2, where SD is the SD of the baseline domain score and R is the sleep quality internal consistency reliability. In those who survived to 1 yr and had a 6-mo CHEQ but missed a 1-yr CHEQ, the sleep quality score was carried forward to 1 yr. For the patients with both a baseline and either 6-mo or 1-yr CHEQ, we assigned a score of "worsened" when their domain score decreased significantly in 1 yr, "no change" when their domain score did not change significantly, and "improved" when their domain score increased significantly. We tested differences in statistically significant individual change in sleep quality in multiple logistic regression, comparing patients whose sleep quality score improved with those whose score was unchanged or worsened, adjusting for baseline sleep quality score, gender, race, ICED scores, and the presence or absence of diabetes. There was 86% power to find a difference in decline of 1 SEM of sleep quality in 12% of the HD versus 22% of PD using a two-sample test of proportions.

We hypothesized that poor sleep quality would be associated with shorter survival. Patients who received transplants, who were lost to follow-up, and who remained enrolled at the close of the study were censored at the time of those events. To adjust for baseline factors that may confound the relationship of sleep quality to survival, we entered into the Cox proportional hazards regression model baseline covariates that were significantly associated with sleep quality and other major demographic covariates that were thought to be associated with survival using robust variance to account for the cluster effect of clinic. The robust variance models that were used to provide our confidence intervals (CI) have been shown to be suited especially to study designs with at least 30 centers (43). The association of change in sleep quality over 1 yr with survival was examined using the groups that were derived from a statistically significant individual change in sleep quality. For the change in sleep quality survival model, survival time was initiated at 1 yr. All analyses were performed in SAS v8.1 (SAS, Institute, Cary, NC).


   Results
Top
 Abstract
 Introduction
 Materials and Methods
 Results
Discussion
 Conclusion
 References
 
The 909 patients who were included in this study were more likely than the excluded 132 CHOICE patients to be white (72.8 versus 64.4; P = 0.04) and to begin kidney failure treatment with HD rather than PD (74.9 versus 65.2%; P = 0.02). However, these groups were similar in age, gender, comorbidity score, proportion currently smoking, hematocrit, and serum albumin.

Among those who responded to the sleep items, 74.2% had at least a little trouble falling asleep, 79.4% would awaken from sleep and have trouble falling back to sleep, and 87.2% did not get enough sleep. Table 1 shows patient characteristics and compares patients by sleep quality score. Across categories, those with poorer sleep quality (lower sleep scores) were younger, were more likely to be black, had a significantly higher serum phosphate, had lower dialysis dose, and were more likely to be currently smoking and to report severe restless legs. There were no significant differences in sleep quality by gender, cause of ESRD, mode of dialysis, severity of comorbid disease, or diabetes. There also were no differences in sleep quality by level of education; body mass index (BMI); serum albumin; serum creatinine; hematocrit; or markers of inflammation, such as fibrinogen, C-reactive protein, and IL-6. Those with poor sleep quality also reported a significantly lower HRQOL in the domains of mental well-being, physical well-being, vitality, and bodily pain. Also, those with a lower sleep score were more likely to use benzodiazepines and opiates, but there was no significant association with the use of antidepressants. The use of both antipsychotic and antiepileptic drugs had a bimodal distribution, with a higher proportion of users at both the lowest and the highest sleep quality categories. The proportion of patients who currently were drinking alcohol (11.5%) and those who were exercising at least weekly (21.6%) did not differ by sleep quality. There also was no unadjusted relationship of BMI2 (F = 0.42; P = 0.74) or obesity (BMI > 30; {chi}2 = 0.035; P = 0.99) with sleep quality.


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  		Table 1. Characteristics of patients in different sleep score categoriesa

 
Independent Characteristics of Patients with Poor Sleep Quality
In multivariate analysis, black race, current smoking, higher serum phosphate, and a prescription for benzodiazepines were associated with a lower likelihood of reporting better sleep quality at baseline (Table 2). The association of current smoking with sleep quality remained consistent after exclusion of medication use from the model. In addition, when sleep quality was examined as a continuous variable in an adjusted mixed-effects model as a sensitivity analysis, there were significant associations between sleep quality and current smoking (P < 0.001), a higher serum phosphate (P = 0.02), restless legs (P < 0.001), and benzodiazepine use (P < 0.01). There also was no relationship of BMI (Wald {chi}2 0.79; P = 0.37), BMI2 (Wald {chi}2 0.71; P = 0.39), or BMI >30 (Wald {chi}2 0.03; P = 0.86) with sleep quality when these were tested in the parsimonious adjusted model.


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  		Table 2. Characteristics associated with better sleep qualitya

 
Associations of Sleep Quality with HRQOL
Figure 1 shows that worse sleep quality was associated with a significantly lower HRQOL as measured by both composite scores and on both specific scales examined (P < 0.0001). Patients who reported the poorest sleep quality had adjusted PCS means that were more than five points lower than the scores of patients who reported the best sleep quality (30.3 versus 35.5; P < 0.0001 by least squares). MCS scores were more than eight points lower (42.6 versus 50.9; P < 0.0001). Vitality scores were more than 15 points lower (34.7 versus 48.0; P < 0.0001), and pain scores were more than 17 points lower (48.7 versus 66.3; P < 0.0001).


Figure 1
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  		Figure 1. Adjusted mean health-related quality of life (HRQOL) scores by sleep quality. MCS, mental component summary score; PCS, physical component summary score; BP, bodily pain; VT, vitality. The MCS and PCS are normed to average a score of 50. The bodily pain and vitality scales have a maximum score of 100. *P < 0.0001 for between-group differences.

 
Longitudinal Changes in Sleep Quality
Statistically significant individual change in sleep quality was examined in 717 patients. Sleep quality worsened in 14.4%, did not change in 70.4%, and improved in 15.2%. Younger patients, those who currently were smoking, and those who were using benzodiazepines were less likely to experience improvement in sleep quality (Table 3). No differences were found between groups in baseline mode of dialysis, gender, race, phosphate levels, diabetes, or symptoms of restless legs.


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  		Table 3. Adjusted OR for better sleep quality at one yeara

 
Association of Sleep Quality with Survival
There were 431 deaths during an average follow-up period of 1132 d. In the Kaplan-Meier estimate of hazard ratio (HR), self-reported sleep quality was not associated with an increased hazard of death (Wilcoxon = 6.1, P = 0.1). Patients with the best sleep quality had a similar hazard of death compared with those with the worst sleep quality (Table 4). However, a decline in sleep quality in the first year of dialysis was associated with an increased crude risk for death (Figure 2). A decline in sleep quality over 1 yr compared with those with stable or improved sleep quality was associated independently with a higher risk for death (HR 1.44; 95% CI 1.13 to 1.83; P = 0.003). To address the issue of reverse causality (i.e., imminent death leading to poor sleep quality versus sleep quality contributing to death), we introduced a 3-mo lag between the last measurement (1 yr CHEQ) and measurement of death. These results were substantially the same as presented above with a decline in sleep quality associated with a higher risk for death (HR 1.39; 95% CI 1.04 to 1.86; P = 0.03).


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  		Table 4. Association of sleep quality with patient survivala

 

Figure 2
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  		Figure 2. Crude survival among those with a worsening in sleep quality in the first year of dialysis compared with those who were stable or had improved sleep. Wilcoxon statistic 3.4; P = 0.07.

 
Sensitivity Analyses
The reclassification of patients with diabetes as using either oral hypoglycemics or insulin was examined in these analyses, and this reclassification did not change our interpretation of the data. In addition, when we stratified the population on exposure to benzodiazepines, there were no substantial differences in the parameter estimates of predictors of poor sleep quality or the relationship of sleep quality with HRQOL and survival.


   Discussion
Top
 Abstract
 Introduction
 Materials and Methods
 Results
 Discussion
Conclusion
 References
 
In this population of incident HD and PD patients, higher serum phosphate, current smoking, prescription of benzodiazepines, and severe restless legs were associated independently with lower sleep quality at the initiation of dialysis. Poor self-reported sleep was strongly associated with lower physical and mental well-being, diminished vitality, and increased bodily pain. Whereas sleep quality score at baseline was not associated with survival, patients with a decline in sleep quality over 1 yr were at 44% higher risk for premature death. Despite the initiation of dialysis and frequent contact with health care providers, 14% of patients had a decline in sleep quality at follow-up. These longitudinal findings demonstrate that younger age, current smoking, and prescription of benzodiazepines were associated with a decrease in sleep quality. These findings are generalizable to incident dialysis patients: CHOICE was a large study with age and racial distributions similar to those in previous national samples (44,45). Many previous studies of sleep quality represented a single center’s experience (17,32), whereas patients in this report were enrolled in 81 different dialysis units.

Our findings confirm the complex correlates of sleep problems in patients with kidney disease (9,46). These results also are consistent with previous findings of a high rate of sleep complaints (16–19,32,47–50). These findings confirm previous work in a smaller sample of maintenance dialysis patients that higher serum phosphate was associated with a lower sleep quality (32). As we have demonstrated previously (27), symptoms of severe restless legs were associated with sleep quality; this finding also was confirmed subsequently in a large population of chronic dialysis patients in Hungary (19). We confirm earlier findings of no substantial differences in sleep between PD and HD patients (32,42,47,51), but a higher Kt/V was associated with better sleep quality. Our findings suggest that a combination of demographic, lifestyle, and treatment factors contribute to sleep quality.

The finding that cigarette smoking was associated with lower sleep quality is consistent with results in the general population and in limited studies of the HD population. Holley et al. (17) noted in a study of 48 HD patients that those with sleep disorders were more likely to smoke cigarettes, and a larger cross-sectional study of 833 HD patients found that a history of smoking was associated with a 70% higher risk for sleep disorders. However, these findings were not reproduced in a cross-sectional study of 172 HD and PD patients (48). These studies did not examine change in sleep quality and only partially accounted for potential confounding factors. In a large study of adults in the general population, cigarette smoking was related to difficulty getting to sleep and to nonrestorative sleep (52). In 484 rural Kentucky residents who were aged 14 to 84, cigarette smokers were more likely to report problems with sleep initiation and maintenance and daytime sleepiness (34). Both exposure to nicotine and withdrawal from nicotine have been shown to have adverse effects on sleep quality (53,54). Nonsmoking individuals who are exposed to nicotine have increases in earlier awakening and decreased rapid eye movement sleep (55). Our findings suggest that the role of tobacco cessation in sleep among those who undergo dialysis merits study, although our results cannot establish causality or reversibility.

This report is unable to distinguish cause and effect in the relationship of poor sleep quality at 1 yr with medications such as benzodiazepines. Modulation of GABA(A) receptor activity by benzodiazepines produces sedative, hypnotic, anxiolytic, muscle relaxant, and anticonvulsant activities (56). Benzodiazepines produce reliable improvements in commonly measured parameters of sleep in patients with chronic insomnia (57). It is important to note that our classification of benzodiazepines included both short-half-life benzodiazepines and long-half-life benzodiazepines. Furthermore, we have no data on the intent of the benzodiazepine prescription or on the patients’ actual use of benzodiazepines. Despite these limitations, the association between a documented benzodiazepine prescription and sleep quality was robust after adjustment for potential confounders. Perhaps dialysis patients who use benzodiazepines for insomnia should be targeted for cognitive behavioral therapy (58) or for a trial of nocturnal HD (59) in an attempt to improve their poor sleep quality.

Sleep quality was shown previously to be associated with HRQOL among those who undergo maintenance dialysis (19,50,60), and our results extend these previous findings to a large, racially diverse incident dialysis population. These findings that the HRQOL remains related to sleep quality after adjustment suggests that it may be that poor sleep quality contributes to poor daytime functioning among dialysis patients. In addition to the strong association of sleep quality with HRQOL, we found that a decline in sleep quality was associated with a significantly higher risk for death. Our finding that baseline sleep quality was not associated with survival mirrors the recent finding from the Atherosclerosis Risk in Communities (ARIC) Study that in the general population, insomnia symptoms were not associated with survival (61). We are not aware of previous findings relating change in sleep quality to survival, although studies have related polysomnographic sleep quality (62) in the general population and both restless legs (27,63) and periodic limb movements in the HD population (64) to long-term survival. Although it may be that the relationship between declining sleep quality and survival is due to residual confounding, this report adjusted for a number of potential confounders. Although declining sleep quality could be used as a tool to identify those who are at risk for clinical events, it remains to be shown that reversing this decline in sleep quality would extend survival.

Several limitations of this report should be noted when interpreting our findings. First, subjective reports of sleep quality do not have a single criterion standard. Although some investigators believe that polysomnography is a reference standard for sleep quality, there is increasing evidence that polysomnography may not reflect fully the physiologic correlates of sleep complaints such as insomnia and daytime sleepiness. Positron emission tomography imaging of patients who report these symptoms can yield striking findings in the absence of significant polysomnographic abnormalities (65). These self-reports of sleep quality are clinically important: Complaints about sleep often are echoed in dialysis units. Second, our study cannot address the mechanism by which cigarette smoking may diminish sleep quality. Third, there was no use of polysomnography in the CHOICE Study; therefore, this report cannot examine the contribution of sleep quality to HRQOL and survival independent of sleep disorders such as sleep apnea and periodic limb movements. However, these findings suggest that clinicians should assess those with poor sleep quality, and the assessment may include the use of polysomnography.


   Conclusion
Top
 Abstract
 Introduction
 Materials and Methods
 Results
 Discussion
 Conclusion
References
 
This report demonstrates that improved sleep quality is an important problem for patients who begin maintenance dialysis. Our findings suggest that smoking cessation could be considered as a part of sleep hygiene among dialysis patients, because smoking was a potentially modifiable factor that was associated with worsening sleep quality. Although these findings represent retrospective cross-sectional assessments of the individual and provide only a static assessment of sleep quality, it is likely that sleep and fatigue have both diurnal and day-to-day variation. It is of considerable importance to understand the variability in sleep quality within the individual and to characterize the contribution of dialysis treatments to the diurnal and day-to-day variability of sleep and fatigue in patients who are supported by renal replacement therapy. Although the diurnal description of sleep quality in this population should be defined better, the effect of treating poor sleep quality on longitudinal outcomes needs to be examined further. Moreover, it is crucial to examine the relationship of nighttime complaints to daytime functioning in this high-risk population, because we still do not know whether improving sleep quality will improve dialysis outcomes.


   Acknowledgments
 
CHOICE was supported by R01-HS-08365 (Agency for Healthcare Research and Quality) from June 1995 to May 2000 and is currently supported by R01-HL-62985 (National Heart, Lung, and Blood Institute) and R01-DK-07024 (National Institute of Diabetes and Digestive and Kidney Diseases). Other grants that support this research are K24-DK-02643 (National Institute of Diabetes and Digestive and Kidney Diseases; N.R.P.), R01-MH24652 (D.J.B.), ASN-Hartford-ASP Junior Development Grant in Geriatric Nephrology, and DK-66006 (M.L.U.).

These findings were presented at the 62nd Annual Scientific Conference of the American Psychosomatic Society, Orlando, FL, March 3 to 6, 2004.

We thank the patients, staff, and physicians who participated in the CHOICE Study at Dialysis Clinic, Inc., St. Raphael’s Hospital, and New Haven CAPD.


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

Received February 28, 2006. Accepted April 24, 2006.


   References
Top
 Abstract
 Introduction
 Materials and Methods
 Results
 Discussion
 Conclusion
 References
 

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