Changes in Quality of Life during Hemodialysis and Peritoneal Dialysis Treatment: Generic and Disease Specific Measures : Journal of the American Society of Nephrology

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Epidemiology and Outcomes

Changes in Quality of Life during Hemodialysis and Peritoneal Dialysis Treatment

Generic and Disease Specific Measures

Wu, Albert W.*,†,‡; Fink, Nancy E.†,‡; Marsh-Manzi, Jane V.R.; Meyer, Klemens B.§; Finkelstein, Frederic O.; Chapman, Michelle M.§; Powe, Neil R.*,†,‡

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Journal of the American Society of Nephrology 15(3):p 743-753, March 2004. | DOI: 10.1097/01.ASN.0000113315.81448.CA
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Every year in the United States, nearly 90,000 patients with chronic kidney disease progress to ESRD and begin renal replacement therapy (1). Because the availability of kidney transplants is limited, most patients must choose between hemodialysis (HD), usually performed at a dialysis center, and chronic ambulatory peritoneal dialysis (PD), usually performed at home. Careful investigations in recent years suggest that PD patient survival equals or is slightly shorter than that of patients who undergo HD (2–6). After consulting with their physicians, fewer than 10% of patients choose PD as their initial therapy (1). Nonetheless, conclusions based on anecdote allow neither systematic nor longitudinal comparison of representative patients and fail to take account of case-mix factors.

It is now widely accepted that health-related quality of life (HRQOL) is an important outcome of health care and one on which patients base treatment decisions. HRQOL is a multidimensional concept that includes physical functioning, social and role functioning, mental health, and general health perceptions (7). In studies of specific conditions, it has become common to measure aspects of quality of life typically affected by the condition and its treatments in addition to general HRQOL (8). Combining both generic and disease-specific measures allows comparisons to other populations and should increase sensitivity to changes over time, whether in the natural history of the disease or in response to treatment.

Cross-sectional studies have yielded most of the data on the HRQOL of people with ESRD. Comparisons of HRQOL on HD and PD have produced conflicting results. A review of this literature that focused on the relationship of dialysis modality to mental health domains suggested that patients experience less distress and better psychologic well-being on PD (9). However, interpretation of this literature was limited because of small and convenient samples of patients, use of new instruments or instruments not tested in ESRD, inadequate control of case-mix, and a lack of repeated measures.

There have been just a few longitudinal studies of HRQOL in ESRD (10–13). The only longitudinal comparison of modalities was a study of consecutive patients who began dialysis in 13 Dutch dialysis centers. The results suggested that HD was associated with a relative benefit in physical aspects of patient-reported health (14). However, this study was small and did not assess dialysis-specific aspects of HRQOL. We comprehensively examined HRQOL in an incident cohort of ESRD patients who were treated by HD and PD in the United States.

Materials and Methods

Study Design and Participants

We conducted a national, prospective cohort study, the Choices for Healthy Outcomes in Caring for ESRD (CHOICE) Study, of incident dialysis patients who were enrolled at 81 clinics in the United States between 1995 and 1998 (15). Details of the study design have been published (15–17). Briefly, to be eligible, patients had to be initiating HD or PD for ESRD, to be 18 yr or older, and to speak either English or Spanish. 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. The enrolled population was oversampled for PD patients to allow statistical comparisons by modality. PD accounted for 26% of the patients in CHOICE compared with 17% in nonenrolled patients. The CHOICE cohort had a larger percentage of white patients (72% versus 68%) and a larger percentage of patients with diabetes as the primary cause of ESRD (47% versus 37%) than the nonenrolled patients. The final CHOICE sample is representative of the United States Renal Data System (USRDS) population with respect to age, gender, and race but differs in the percentage PD. Diabetes and hypertension together accounted for ESRD in two thirds of the cohort, a figure similar to the USRDS; however, the percentage attributed to hypertension in the study population was lower than in the USRDS (17.5% versus 28.7%). Patients were enrolled a median of 45 d from the start of chronic dialysis (98% within 4 mo). The protocol was approved by Institutional Review Boards at all institutions, and participants gave written informed consent to participate in the study.

Data Collection

At baseline, we obtained sociodemographic information from a patient questionnaire, from the treatment clinic, and from the Health Care Financing Administration Medical Evidence Form 2728. Baseline clinical assessments were reported from each treatment clinic. Participants completed the Choice Health Experience Questionnaire (CHEQ) that included information on generic and dialysis specific HRQOL (described below). Follow-up visits were scheduled at 3, 6, and 12 mo after enrollment. This article focuses on data collected at the baseline and 12-mo visits.

We categorized sociodemographic variables as follows: age <65 yr (yes/no), race (white, black, other), currently married (yes/no), employed part or full time (yes/no), obtained high school degree (yes/no), and residing <30 mi from treatment clinic (yes/no). Baseline laboratory values included serum albumin (g/dl), serum creatinine (mg/dl), hematocrit (%), and blood urea nitrogen. We created baseline and 12-mo Index of Co-existent Disease (ICED) scores as described previously (18–20) using data from a comorbidity assessment form. From 19 disease categories, each graded according to four levels of severity, and 11 physical impairment categories, each graded according to three levels of severity, we calculated the four-level aggregate ICED score, reflecting peak disease and physical impairment scores. A higher ICED score represents greater extent and/or severity of comorbid conditions.

HRQOL Assessment

The CHEQ includes the SF-36 (21) and 14 dialysis-specific domains (22) (Table 1). For the SF-36, eight domain scores (physical functioning [PF], role limitations as a result of physical problems [RP], bodily pain [BP], general health perceptions [GH], social functioning [SF], role limitations as a result of emotional problems [RE], vitality [VT], and mental health [MH]) and summary Physical Component (PCS) and Mental Component (MCS) scores were calculated at baseline and 1 yr. We calculated SF-36 domain scores ranging from 0 to 100 according to published guidelines (23, 24). In addition, we computed 14 baseline and 1-yr dialysis-specific raw domain scores by summing responses from CHEQ questionnaire items. Dialysis-specific domains include time, freedom, travel, cognitive function, financial concerns, diet restrictions, recreation, work, body image, symptoms, sleep, sexual functioning, dialysis access, and global quality of life. For example, one of the items on time asks, “Did you feel like you spent too much time dealing with your dialysis treatment?” One of the items on financial concerns asks, “Have you been bothered by being unable to work at a paying job?” We linearly converted dialysis-specific domain scores to a 0- to 100-point scale in the same manner as used for the SF-36 domain scores.

Table 1:
Generic and dialysis-specific domains of health-related quality of lifea

For multi-item scales (PF, RP, BP, GH, SF, RE, VT, MH, Time, Freedom, Cognitive Function, Symptoms, Sex, Sleep, Access, and Quality of Life), we obtained internal consistency reliability estimates (Cronbach’s α) using Multitrait/Multi-item Analysis Program-Revised version 1 (25). For the SF-36 PCS and MCS, we used reliability estimates from the U.S. population. For single-item domains (Travel, Finance, Diet, Recreation, Work, and Body Image), we estimated test-retest reliability using Pearson correlation between CHEQ domain scores from baseline and 3 mo later among 830 CHOICE patients who completed a CHEQ at 3 mo after baseline. Although this may not be ideal given changes that might occur early in the course of dialysis, the coefficients that we observed represent estimates for reproducibility.

Statistical Analyses

We first performed an intention-to-treat analysis on patients who completed the CHEQ at both baseline and 1 yr later. We compared differences in unadjusted mean CHEQ and SF-36 domain scores between HD and PD patients at baseline and 1 yr later using t tests (nonparametric Wilcoxon rank sum tests yielded similar results). We calculated unadjusted mean 1-yr changes in every domain for each dialysis modality by subtracting baseline from 1-yr scores and tested differences between HD and PD patients using t tests. To estimate and test differences between HD and PD patients in baseline and 1-yr mean domain scores and in mean 1-yr changes adjusting for confounders, we used generalized estimating equation models for repeated measures with a restructured data set containing two observations per patient.

Each observation contained baseline risk adjustment variables selected from the literature, either the baseline or 1-yr domain scores, and a time variable equal to zero for baseline domain scores or the difference between the date of the 1-yr CHEQ and the enrollment date for the 1-yr scores (9, 26–34). Domain scores were modeled as dependent variables and baseline modality, the time variable, the interaction of the time variable and baseline modality, and confounders (age, gender, race, education, baseline albumin, creatinine, hematocrit, and ICED) as independent variables, specifying an equal within-patient correlation structure and the robust Huber/White/sandwich estimator of variance. The coefficients for the time variable and the interaction term represented the adjusted mean 1-yr change in domain score for HD patients and the adjusted mean difference in 1-yr change in domain score for PD versus HD patients, respectively. Because the baseline domain score is part of the slope calculation in the repeated measures model, it was not included as an adjustment variable.

To address the loss of information as a result of dropouts, we conducted a separate analysis that examined deteriorations, improvements, and constancy (no change) in domain HRQOL from baseline to 1 yr later using all patients with a baseline CHEQ. For the patients with both a baseline and 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 defined significant 1-yr increases and decreases in dialysis-specific and SF-36 domains as changes in domain scores exceeding two standard errors of measurement (SEM) (24). We calculated the SEM as SD*(1 − R)1/2 where SD is the SD of the baseline domain score and R is the domain reliability. For patients who were missing the 1-yr CHEQ, we assigned a domain score of “worsened” to those who died within the first 15 mo after enrollment and a score of “improved” to those who received a kidney transplant during that period. For the remaining patients, we assigned a score of “worsened” when their ICED score increased in 1 yr, “no change” when their ICED score remained identical, and “improved” when their ICED score decreased. We tested differences in changes in domain HRQOL according to dialysis modality in bivariate (χ2 tests) and multivariable (logistic regression comparing patients whose domain score improved with those whose score was unchanged or worsened, adjusting for baseline domain score, age, gender, race, education and baseline albumin, creatinine and hematocrit, but not ICED, which was included in the outcome definition) analyses. Because not all patients continued on their baseline dialysis modality throughout the subsequent year as the above intention-to-treat analyses assume, sensitivity analyses were conducted to assess the impact of modality switches during the first year after baseline. All analyses were repeated excluding patients who switched modality before completing the 1-yr CHEQ or, for patients without a 1-yr CHEQ, before the 1-yr anniversary of the enrollment date. All analyses were conducted using SAS (version 6.12) and Stata (version 7.0) statistical software.


Patients and Baseline Characteristics

Of the 1041 patients, 89% (698 HD patients and 230 PD patients) completed a baseline CHEQ. Compared with the 113 patients who did not complete the CHEQ, patients who completed the CHEQ tended to be female (47% versus 35%; P = 0.02), receiving HD (75% versus 60%; P = 0.001), at least high school educated (71% versus 56%; P = 0.04), not currently married (45% versus 30%; P = 0.01), and living farther from the treatment clinic (13% versus 4%; P = 0.48). There were no other significant differences between the two groups of patients. Of the 928 patients who completed a baseline CHEQ, 585 also completed a CHEQ 1 yr later. Of the 343 patients with a baseline but no 1-yr CHEQ, 244 were no longer under follow-up and were closed out before receipt of their 1-yr CHEQ (within 15 mo of their enrollment date); the remaining 99 remained in the study but failed to return a 1-yr CHEQ. Reasons for closing out included death (n = 101), kidney transplantation (n = 55), moving to a dialysis clinic not in CHOICE (n = 48), refusal (n = 17), and other (n = 23). There were no significant differences in demographics, comorbidity, or clinical measures at baseline between participants who did and did not complete the 12-mo follow-up. Quality of life was better on a few domains for completers (C) versus noncompleters (NC) of the 12-mo follow-up (mean scores and t test P values follow): social function 60.5 (C), 56.8 (NC), P = 0.05; mental health 69.5 (C), 66.2 (NC), P = 0.02; symptoms 77.8 (C), 75.2 (NC), P = 0.02; travel 59.7 (C), 53.5 (NC), P = 0.01; recreation 60.0 (C), 54.0 (NC), P = 0.01; and access 67.9 (C), 63.8 (NC), P = 0.02.

Baseline characteristics of cohort members who completed the CHEQ at baseline (n = 928) and 1 yr later (n = 585) are displayed in Table 2, stratified by baseline dialysis modality. PD patients tended to be younger, white, college educated, employed, married, and living farther from the treatment clinic and to have less baseline comorbidity and higher hematocrit. There were no HD-PD differences according to gender or other measures such as baseline residual renal function. Mean Kt/V among HD patients was 1.3 at baseline (n = 534) and 1.3 1 yr later (n = 344).

Table 2:
Baseline characteristics of CHOICE patients by baseline dialysis modality and CHEQ form completion status at baseline and 1 year latera

Internal Consistency and Test-Retest Reliability

Internal consistency estimates were as follows: PF, 0.91; RP, 0.82; BP, 0.86; GH, 0.72; VT, 0.85; SF, 0.76; RE, 0.86; MH, 0.84; PCS, 0.91; MCS, 0.87; symptoms, 0.81; cognitive function, 0.86; sleep, 0.77; sex, 0.93; quality of life, 0.67; freedom, 0.76; time, 0.58; and access, 0.72. Test-retest reliability correlations were as follows: travel, 0.62; finance, 0.79; diet, 0.70; recreation, 0.64; work, 0.72; and body image, 0.55.

Comparisons of Crude Quality of Life Domain Scores Between HD and PD

At baseline, SF-36 domain and summary scores were substantially lower than in the general population (23). The only exception was for mental health, for which scores were only a few points less. When unadjusted mean dialysis-specific and SF-36 domain scores were compared (Table 3), PD patients had significantly (P < 0.05) higher scores for SF-36 bodily pain (9 to 11% higher) and for the dialysis domains of travel (12 to 15% higher), diet restrictions (19 to 23% higher), and dialysis access (8 to 9% higher), both at baseline and 1 yr later compared with HD patients. PD patients also had significantly higher baseline scores for the SF-36 domains PF (17% higher) and RE (20% higher), as well as for the dialysis domain of financial concerns (20% higher) at 1 yr. The only domains for which significant differences favored HD patients were VT (12% higher) and sexual functioning (15% higher) at 1 yr.

Table 3:
Unadjusted and adjusted mean quality-of-life domain scores at baseline and 1 year later by dialysis modalitya

When unadjusted mean changes in SF-36 domain scores from baseline to 1 yr later were compared between HD and PD patients, HD patients tended to show greater improvement in SF-36 domain scores than did PD patients (Figure 1). HD patients had significantly greater improvements in PF (5.1 points greater) and GH perceptions (4.1 points greater) than PD patients. Results for dialysis domains were mixed, with HD patients exhibiting significantly greater improvement in sleep (8.1 points greater) and global quality of life (4.3 points greater) over 1 yr, and PD patients showing greater improvement in the finance domain (8.1 points greater).

Figure 1.:
Crude mean differences in general (SF-36) and ESRD domain scores from baseline to 1 yr by baseline dialysis modality. □, hemodialysis (HD); ▪, peritoneal dialysis (PD); Symp, symptoms; Cog, cognitive function; Body, body image; Free, freedom; Trav, travel; Recr, recreation; Fin, finances; Acc, access. *P < 0.05.

Comparisons of Adjusted Quality-of-Life Domain Scores Between HD and PD

When mean change over 1 yr was adjusted for potential confounders (Figure 2), a similar pattern of results was obtained. In terms of generic HRQOL, HD patients showed greater improvement in all SF-36 domains except mental health, although only differences in PF and GH perceptions were statistically significant. With regard to dialysis-specific domains, after adjustment for confounders, HD patients had a significantly greater improvement in sleep (7.4 points greater), whereas PD patients had significantly greater improvement in the finance domain (6.6 points greater).

Figure 2.:
Adjusted (for age; gender; race; education; Index of Co-existent Disease; and baseline albumin, creatinine, and hematocrit) mean differences in general (SF-36) and ESRD domain scores from baseline to 1 yr by baseline dialysis modality. □, HD; ▪, PD; Symp, symptoms; Cog, cognitive function; Body, body image; Free, freedom; Trav, travel; Recr, recreation; Fin, finances; Acc, access. *P < 0.05.

Comparisons of Deteriorations and Improvements in Domains by Modality in All Patients

Considering changes (worsened, no change, improved) in overall domain health status, defined by changes in domain score, death, kidney transplantation, or changes in extent of comorbidity, among all patients 20 to 31% had a worsening, 42 to 60% had no change, and 19 to 28% had an improvement in generic domains of HRQOL. Among all patients, 19 to 30% had a worsening, 50 to 65% had no change, and 16 to 24% had an improvement in dialysis-specific domains of HRQOL. There were no statistically significant differences between HD and PD patients in these changes for any of the generic or dialysis-specific domains (Table 4). Adjusted analyses tended to confirm these results (Table 5). Sensitivity analyses excluding the 55 patients (25 HD, 30 PD) who switched from their baseline modality during the first year produced virtually identical results (data not shown).

Table 4:
Change in overall health statusa from baseline to 1 year after baseline in 928 CHOICE patients overall and stratified by modality
Table 5:
Adjusted odds ratiosa for improvement in overall health statusb on PD versus HD


This national study provides a comprehensive and detailed description of the quality of life of patients who started HD and PD and their progress 1 yr later. The findings have important implications for physicians who evaluate and treat patients with chronic kidney disease. They suggest that there is no simple answer to the question of which dialysis modality can be expected to provide better quality of life.

One year after starting dialysis, patients on both HD and PD reported improvements in nearly all aspects of general functioning and psychologic well-being. The surprising finding was that patients on HD improved more on aspects of general HRQOL than patients on PD, with greater improvements in PF and GH perceptions. Despite lower scores at baseline, at 1 yr, patients on HD actually reported better scores in some domains, such as better physical role functioning. These findings remained after adjustment for baseline patient characteristics, although the differences were no longer statistically significant.

Changes in dialysis-specific aspects of life were more mixed, and there were more differences between the two modalities. HD patients improved more in some aspects, such as sleep (which for PD patients actually became worse over time) and body image. At the end of 1 yr, patients on HD reported significantly better sexual functioning than those on PD. PD patients improved more on other dialysis-specific aspects of life, such as financial well-being, and continued to have higher scores for ability to travel, diet, and dialysis access.

Are the differences and changes in scores clinically important? Previous studies using the SF-36 suggest that scores in the range of 2 to 3 points on the physical and mental health summary scores (equivalent to 0.2 to 0.3 SD units) and 5 or more points for the individual subscale scores are likely to be clinically important. We observed changes that were 2 to 3 points greater, suggesting that they are likely to be noticeable and meaningful to patients on dialysis (24). For example, 49% of patients on PD and 54% of HD patients reported moderate or severe financial problems at baseline, whereas 41% of PD patients and 54% of HD patients reported such problems at 1 yr. It should be noted, however, that there were substantial differences in only a few of the domains.

To strengthen the comparisons in the absence of an experimental design, we assembled a large, national cohort of incident dialysis patients and examined a comprehensive set of domains of quality of life allowing examination of the impact of dialysis modality on specific aspects of patients’ lives. We attempted to account for selection bias by adjusting for a wide range of baseline clinical and sociodemographic characteristics that might affect patients’ ability to improve. We presented both actual scores reported by patients and changes in scores, because the former are more useful to describe what life is like for patients, whereas changes are more likely to be attributable to the choice of therapy and may be more helpful for choosing modality. In this study as well as many others, patients who started PD were healthier, more privileged, and more likely to be working than those who started HD (27). Patients who started PD had significantly better general HRQOL, including better PF, less BP, and better RE scores than patients who started HD. PD patients also reported better quality of life in areas specific to dialysis, with significantly greater ability to travel, fewer dietary restrictions, and fewer concerns about dialysis access. Bivariate results provide the best description of expected quality-of-life scores for typical patients, whereas the multivariable results provide greater confidence in attributing changes in scores to dialysis modality.

We also followed patients longitudinally and, with a thorough accounting of the disposition of patients, guarded against survival biases that might be introduced by patients who left the cohort during the course of the study. We performed an overall analysis that combined the major important outcomes, including HRQOL, transplantation, and survival. This analysis showed that observed differences in quality of life were not due to a higher mortality or transplant rate that might in turn be associated with dialysis modality. Information on many of the dialysis-specific domains has not been collected systematically in previous evaluations of dialysis treatment modalities. The results highlight the importance of measuring dialysis-specific domains. The results are also supported by hypotheses that reflect clinical intuition and experience. Patients on HD would be expected to have more problems with pain (e.g., needle sticks) and dialysis access (35). Patients on PD would be expected to have more problems with sleep and body image but greater ability to travel and work and better financial status. Measuring each of these domains separately allowed us to identify specific aspects of life that differed by modality, information that might be useful to individual patients with specific preferences as they attempt to decide between modalities.

Our findings expand the results of previous studies, most of which were cross-sectional and collectively were not conclusive as to impact of dialysis modality on quality of life. Several studies suggest advantages for PD in some domains (14, 35, 36–38), advantages for HD in others (28, 35), or little difference (27, 30, 32, 35–37, 39, 40). Cameron et al. (9) performed a meta-analysis to determine whether dialysis treatment modality and case-mix could explain differences in emotional distress and well-being. PD was associated with greater well-being than HD, but several case-mix variables seemed important. The authors concluded by recommending a repeated measures cohort study with careful identification and documentation of case-mix variables and statistical control, with a representative sampling of quality-of-life outcomes.

Merkus et al. (14) published a longitudinal comparison of modalities in their study of consecutive patients with incident ESRD in 13 Dutch dialysis centers. HRQOL was measured using the generic SF-36, which was administered 3, 6, 12, and 18 mo after the start of dialysis. As only 139 of the 230 patients stayed on their initial dialysis modality, an intention-to-treat analysis was also used. They found an overall decrease in SF-36 PCS over time of 1.9 points, with a significantly smaller decrease for patients on HD compared with those on PD. It is interesting that the mean difference in change score from 3 to 18 mo was 2.3 points. We observed a small increase in PCS for patients on HD and similar decrease for patients on PD, with a difference of 1.3 points over the 12 mo of study that favored HD. Thus, our results are fairly comparable. However, in their study, mental health scores did not change significantly over time and were similar between modalities. Our subjects reported small increases in mental health, again with no difference by modality. In their intention-to-treat analysis, there were no significant treatment effects between HD and PD. Harris et al. (40), in their 12-mo prospective cohort study of 174 dialysis patients 70 yr or older from four hospital-based renal units in London, found quality of life to be similar at 1 year in PD and HD patients.

Our study had some limitations. First, we did not measure quality of life before patients began dialysis. Therefore, it is likely that we did not capture all of the improvements in HRQOL experienced by patients at the very start of dialysis. These are likely to be concentrated in domains of general quality of life as assessed by the SF-36. If this initial improvement differed between dialysis modalities, then it is possible that we might fail to capture some of the subsequent changes. In the future, attempts should be made to follow quality of life in chronic kidney disease patients as kidney function declines, with particular attention to changes as they make the transition from the predialysis state to dialysis treatment.

Second, it is possible that baseline patient characteristics might have been associated with a greater or lesser proclivity to improve or report improvement. It is possible that PD patients, who were on average more advantaged than patients who started HD, had more to lose and less opportunity to benefit from dialysis. We also did not measure patient expectations about the outcomes of dialysis or their personality characteristics, both of which might also influence treatment selection and adherence. For example, optimistic people with good social support and high self-efficacy might choose PD. HD patients might choose their therapy because they are less independent and receive more social support as part of their treatment. In a small Swedish sample, Lindqvist et al. (41) observed more avoidant coping among patients on HD and suggested that there may be trait differences in patients who select different modalities. However, there are no data in this patient population to suggest how such traits might affect changes in HRQOL. Problems with measurement can sometimes lead to similar results, but this does not seem to be the case because ceiling effects were not evident for the majority of scales for which there were differences between groups. If the functional form of recovery were nonlinear and sicker patients were on a steeper portion of the functional status curve than healthier patients, then it might also be easier to detect an increase in score. This possibility would need to be addressed in a future study.

Third, despite our relatively large cohort, we had limited power to detect differences in subdomains of HRQOL. Even larger studies would be needed to determine whether some of the smaller observed differences truly exist, such as those that favor HD in most domains on the SF-36, the small difference in mental health favoring PD, and other small differences in dialysis-specific domains. In addition, given the multiple statistical tests performed, we cannot discount the possibility that some of the statistically significant findings are due to chance.

Finally, as this was an observational study rather than a randomized controlled trial, we cannot be absolutely certain that differences in quality-of-life changes can be attributed to dialysis modality. However, it is doubtful whether a randomized controlled trial would be possible, because it would not be possible to blind assignment to treatment groups. In addition, assignment to a specific dialysis modality might in itself decrease the patient’s quality of life, threatening the internal validity of such a design (42).

Our study has important implications for practice. The good news for patients on both modalities is that health and general well-being should improve during the first year of dialysis. This information should be reassuring to patients who are reluctant to start dialysis. Conversely, there may be losses in some specific aspects of life. Although some of these losses are related to the dialysis modality, we cannot conclude that PD produces a better quality of life than HD for patients who initiate renal replacement therapy. In fact, HD may result in more positive changes in general and physical aspects of HRQOL. There do seem to be distinct advantages and disadvantages to each of the two modalities that should be explored with patients who are choosing between them. Physicians should be as explicit as possible in describing specific tradeoffs and attempt to elicit individual preferences for these aspects of quality of life. For some patients, these preferences could be decisive. For example, for individuals for whom work is a top priority, PD may allow the best future functioning. Patients on PD can also anticipate less pain and problems with diet. However, for those who are more concerned about other aspects of life, such as sleep or sexual functioning, HD may offer relative advantages.

In conclusion, contrary to anecdotal reports, it does not seem that PD produces a better quality of life than HD for patients who initiate renal replacement therapy. In general, patients on HD may have maintained their general health status to a greater extent than PD patients. However, there are distinct advantages and disadvantages to each of the two modalities. These should be explored with all patients who confront this choice.

Supported by grant R01-HS-08365 from the Agency for Health Care Research and Quality (Rockville, MD) and grant RO1 DK 59616 from the National Institute of Diabetes and Digestive and Kidney Diseases (Bethesda, MD). Dr. Powe is supported in part by grant K24DK02643 from the National Institute of Diabetes and Digestive and Kidney Diseases (Bethesda, MD).

We thank the patients, staff, and medical directors of the participating clinics at Dialysis Clinic, Inc. (DCI), St. Raphael’s Hospital, and New Haven CAPD who contributed to the study. We thank Patricia Bayton for help in preparing this manuscript.

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