1 United States Renal Data System (USRDS), Rehabilitation/Quality of Life Special Studies Center, Emory University, Atlanta, GA and 2 United States Renal Data System (USRDS), Coordinating Center, Minneapolis, MN, USA
Correspondence and offprint requests to: Nancy G. Kutner, PhD, Director, USRDS Rehabilitation/QoL Special Studies Center, Emory University, CRM-1441 Clifton Rd. NE, Atlanta, GA 30322, USA. Email: nkutner{at}emory.edu
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Abstract |
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Methods. Association of modality with incident patients' health status and quality of life scores was investigated with propensity score (PS) analysis and also with traditional multivariable regression analyses. We compared patient reported health status and quality of life scores after 1 year of therapy in 455 HD and 413 PD patients who participated in a national study, stayed on the same modality and had complete socio-demographic and clinical information needed to create a PS indicating their expected probability of starting on PD.
Results. One year scores on the majority of health status and quality of life measures were not significantly different for HD and PD patients within propensity-matched quintiles. PD patients' scores were higher than HD patients' scores on effects of kidney disease, burden of kidney disease, staff encouragement and satisfaction with care in some quintiles, and traditional regression analyses confirmed that dialysis modality was associated with patients' scores on these variables.
Conclusions. This study provides support for making the choice of PD more widely available as an option to patients initiating chronic dialysis therapy. Patient lifestyle opportunities associated with use of PD, a home-based and self-care therapy, may also apply to home-based HD or in-centre self-care HD. Patients' expectations regarding treatment and their attitudes toward management of their health may interact with treatment modality to shape patient-reported experience on dialysis; this is an important focus for future studies.
Keywords: haemodialysis; health status; peritoneal dialysis; propensity score analysis; quality of life
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Introduction |
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A prominent topic in the recent literature is the rationale for wider availability of PD as a treatment option for patients beginning chronic dialysis therapy [27]. It is argued that the majority of incident patients have no contraindication to either HD or PD [5], that the risk of death is generally lower for PD during the first year or two of dialysis [7] and that costs are lower with PD compared with HD [3,6]. It is also suggested that no large differences in quality of life have been found between patients starting with HD or PD therapy [5], but few studies of incident patients have addressed this issue.
In a randomized trial of 38 patients starting HD and PD in 19972000, Korevaar et al. in The Netherlands found a small difference in patients' quality-adjusted life year (QALY) scores in the first 2 years of dialysis, and this difference favoured HD over PD [8]. The Choices for Healthy Outcomes in Caring for End-stage Renal Disease (CHOICE) study of incident patients in the USA during 19951998 concluded that at 1 year patients on HD and PD generally reported similar health status but that patients on the two modalities had different assessments of several dimensions of disease-specific quality of life. Patients on HD scored higher on sexual functioning than did patients on PD, but patients on PD reported better quality of life than patients on HD as measured by perceived ability to travel, financial concerns, restrictions in eating and drinking, and dialysis access problems [9].
It is imperative to consider carefully differences among patients when treatment outcomes are analysed. It is difficult for case mix adjustments to account adequately for these differences in analyses that compare patient outcomes in relation to use of HD and use of PD because patients are differentially selected to RRT [10]. The ideal study design is a randomized clinical trial. Korevaar et al. attempted the only clinical trial in which patients were randomly assigned to HD and PD, but they were not able to recruit enough patients for an adequately powered study, and noted that after extensive patient education, many patients are likely to develop a preference for a particular modality, making random assignment difficult [8]. Observational studies therefore remain the primary source of information about patient outcomes associated with treatment by HD vs treatment by PD.
Here we report an analysis of health status and quality of life reported by patients after 1 year of HD or PD treatment. Our data source was a large cohort of incident patients who participated in the Dialysis Morbidity and Mortality Study (DMMS) Wave 2 conducted in 19961998 in the USA [11]. We used both propensity score (PS) analysis and traditional regression analyses to examine the data. PS analysis identifies patients who are similar on measured confounders who then can be compared on the outcomes of interest. Patients are given a score that represents their expected probability of receiving one treatment over another. This score can be estimated from a logistic regression model of the actual treatment received that is fit to the data. Outcomes can then be compared among patients who have been classified into strata (quintiles) based on their similar propensity to receive a particular treatment [12].
We also used multivariable regression analyses to investigate the association of HD and PD with patients' 1 year health status and quality of life scores, adjusting for patients' socio-demographic and clinical characteristics (the measured confounders) as well as for the health status/quality of life scores that patients reported at treatment start. Findings with the two analysis approaches were expected to be consistent, as discussed in a recent commentary on the value of PS analysis [12]. An advantage of PS analysis is that it allows a visual comparison of HD and PD patient scores displayed within quintiles, where quintiles contain patients determined to be similar on a series of measured covariates.
Like the CHOICE study, PD patients were over-sampled in the DMMS Wave 2, and data were collected in the same time period (19961998). While the CHOICE study enrolled patients from 81 non-randomly selected dialysis centres, however, the DMMS Wave 2 enrolled patients from 799 clinics that were a 25% random sample of US centres at the time of the study [11]. We chose to focus on patients who were on HD or PD from treatment start to the end of the first year of treatment, when patients had had a chance to experience some of the complications of their chosen modality [2]. Overviews of DMMS Wave 2 patient scores at treatment start have been reported [11,13], but there has been no previous analysis of the 1 year health status/quality of life data from the DMMS Wave 2. Patient scores should not be generalized to incident patients currently, given practice changes in delivery of both HD and PD, but the DMMS Wave 2 is a valuable source of data because it is the largest study of a national sample of patients initiating HD and PD in the USA.
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Methods |
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There were 3606 DMMS Wave 2 patients for whom modality information was available at baseline: 1820 patients who started on HD and 1786 patients who started on PD. Among patients who started on HD, at 1 year 64.8% were on HD, 2.0% were on PD, 2.5% had received a transplant, 13.6% were deceased and 17.1% were lost to follow-up. Among patients who started on PD, at 1 year 59.2% were on PD, 8.8% were on HD, 5.9% had received a transplant, 11.5% were deceased and 14.6% were lost to follow-up.
We used DMMS Wave 2 data with updated patient characteristics available on the 2001 USRDS Core Standard Analysis File. Our study includes 455 HD patients and 413 PD patients who provided information about their perceived health status and quality of life as requested in the patient questionnaire at baseline (60 days) and again after 1 year on dialysis and who had complete socio-demographic and clinical information for variables used to create a PS. These patients were similar with respect to modality, age, gender, diabetic ESRD and baseline cardiovascular co-morbidity to patients who did not answer the patient questionnaire or had missing socio-demographic and clinical information, but they were less likely to be black.
Measures and data collection
DMMS Wave 2 data collection instruments are available in the Researcher's Guide to the USRDS Database at www.usrds.org/research.htm. Dialysis centre personnel supplied demographic and medical history information for each patient, abstracting data from the patient's medical record. A questionnaire distributed to enrolled patients at baseline (day 60 after treatment start) and at 1 year asked about employment status and included scales from the Kidney Disease Quality of Life-Short Form (KDQOL-SF) instrument (http://www.gim.med.ucla.edu/kdqol/); the baseline questionnaire also asked about medical care received prior to chronic dialysis. The protocol specified that patients should self-complete the questionnaire at the dialysis centre whenever possible, but patients unable to complete the questionnaire because of their level of education or because of a physical disability such as impaired vision could receive assistance from a dialysis centre staff member or a family member.
Socio-demographic variables (age, gender, race, education, marital status and household status), dialysis start date, diabetes as primary cause of ESRD, cardiovascular co-morbidity and laboratory data were identified from information in the DMMS Wave 2 medical questionnaire completed by dialysis centre personnel. Cardiovascular co-morbidity included one or more of the following conditions documented from chart review: coronary heart disease/coronary artery disease, acute myocardial infarction, cardiac arrest, cerebrovascular accident/stroke, peripheral vascular disease or congestive heart failure. Serum creatinine values were reported for the day of the patient's first regular dialysis or the closest day prior to that date. Serum bicarbonate values were obtained from information closest to study start date, i.e. 60 days past the start of regular dialysis, from a period of up to 3 months before study start date (www.usrds.org/research.htm). Early referral for pre-ESRD care by a nephrologist was defined in our study as 4 months or more before dialysis treatment start, consistent with prior research [14].
Reliability and validity have been demonstrated for the KDQOL-SF [15]. The KDQOL-SF includes generic measures of health status (the RAND 36-item health survey) and multiple disease-specific quality of life scales. The instrument also includes two scales that focus on the patient's assessment of dialysis care. Each scale is scored 0100, with a higher score indicating a better rating. The program used to calculate the scores is available at http://www.gim.med.ucla.edu/kdqol/.
All eight generic health status measures (Physical functioning, Role limitation physical, Pain, General health perceptions, Emotional well-being, Role limitation emotional, Social functioning and Vitality) had adequate internal consistency reliability estimates (0.7) in the DMMS Wave 2 data, as did seven disease-specific quality of life scales (Symptoms/problems, Effects of kidney disease on daily life, Burden of kidney disease, Social support satisfaction, Cognitive function, Sleep and Sexual function) and the two dialysis care scales (Staff encouragement and Patient satisfaction). The effects of kidney disease, social support satisfaction, sleep, sexual function, staff encouragement and patient satisfaction scales used in the DMMS Wave 2 contained minor modifications in wording (see http://www.usrds.org/research.htm).
Data analysis
Socio-demographic and clinical characteristics of patients starting on HD and patients starting on PD were compared by t-test for continuous variables and by 2 test for categorical variables.
A PS representing the probability of receiving PD over HD at treatment start was estimated for each patient. Patient characteristics previously shown [14] to predict dialysis modality selection in the DMMS Wave 2 (listed in Table 1) were used to build the PS model. Logistic regression with backwards elimination was used to estimate the PS with baseline treatment modality (HD or PD) as the outcome. The c statistic of the PS model indicated good prediction of dialysis modality (c = 0.74) [16]. Patients were classified into quintiles defined by PS (quintile I = most likely to receive PD to quintile V = least likely to receive PD). Health status, quality of life and dialysis care scores reported at baseline and at 1 year by HD and PD patients were compared within quintiles, using t-tests (baseline analyses) and regression analyses with adjustment for patients' baseline scores (1 year analyses).
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Analyses were performed using SAS version 8e (SAS Institute, Cary, NC).
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Results |
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After patients were classified into propensity-matched quintiles, there were no significant differences in characteristics of PD and HD patients within quintiles, with the exception of marital status in quintile III (PD patients less likely to be widowed compared with HD patients), vintage in quintile IV (PD patients had a longer time since initiation of dialysis compared with HD patients) and average serum bicarbonate in quintile II (PD patients had a higher average value compared with HD patients) (Table 2).
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Discussion |
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Approximately one-third of patients who started on HD and of patients who started on PD were not included in our analyses because of transplantation, death, modality change or loss to follow-up at 1 year. It is important to consider whether there were systematic differences by modality with regard to baseline health status/quality of life values among patients who were included and not included. Among patients who started on HD, patients included in our analyses scored higher at baseline on 10 of the 17 dependent health status/quality of life measures than did patients who started on HD but who were not included in our analyses. Among patients who started on PD, patients included in our analyses scored higher at baseline on 11 of the 17 dependent health status/quality of life measures than did patients who started on PD but who were not included in our analyses. Moreover, the measures on which patients included in our analyses scored higher than those not included in our analyses were almost identical. Included patients on both modalities scored higher on physical functioning, bodily pain, emotional well-being, role limitation emotional, social functioning, vitality, symptoms/problems, effects of kidney disease on daily life and burden of kidney disease. In addition, HD patients included in our analyses scored higher on sleep than did those HD patients not included, and PD patients included in our analyses scored higher on general health and cognitive function than did those PD patients not included.
Inferences about patients' quality of life are influenced by the specific measurement tools used in a study as well as by the ability of researchers to control for potential confounding variables. Patient responses to questions in a standardized instrument may or may not effectively capture aspects of quality of life that are of most importance to individual patients. The KDQOL-SF is an instrument with demonstrated reliability and validity and includes measures of perceived health status, quality of life and satisfaction with care, all of which comprise important dimensions of patient experience on dialysis. Other instruments, however, may capture additional elements of the effects of illness and effects of treatment that are salient for patients.
Selection bias is a major concern in studies comparing patient outcomes in relation to RRT [1,7,9,10,15,18,19]. The PS helps to balance observed baseline covariates between exposure groups, but unmeasured characteristics remain unbalanced. The nature of the education and modality orientation that patients receive pre-dialysis (not simply whether or not patients receive pre-ESRD care) and patient attitudes toward managing their disease are two examples of important unmeasured variables that may influence both selection of a dialysis modality and subjective experience on dialysis [7]. The randomized clinical trial balances unmeasured and measured covariates. This ideal design for investigating the association of dialysis modality with patient experience and outcomes is an elusive goal, however, when modalities differ in their requirements for the patient's capability and willingness to participate [10]. In the absence of an ideal design, it is crucial to make efforts to adjust adequately for confounding variables. Winkelmayer and Kurth [12] note that the success of PS analysis can be gleaned from a table comparing baseline covariates between exposure groups within PS strata. Although DMMS Wave 2 patients starting treatment on HD and PD differed on a large number of characteristics at baseline, Table 2 demonstrates that almost no significant socio-demographic or clinical differences were evident between HD and PD patients within propensity-matched quintiles.
Most of the research examining the association of treatment modality with dialysis patients' health status and quality of life consists of cross-sectional studies of prevalent patients. Studies investigating health status and quality of life reported by incident patients have the advantage of being able to compare HD and PD patients who are at a similar point in their treatment experience, thereby controlling for an important source of potential variation in patient response. Relative risk of death with HD and PD varies by the length of time that patients have been on dialysis [7]. Patients' reported quality of life is also likely to differ by the length of time that patients have been on dialysis as patients adapt to their changing life circumstances and/or experience change in co-morbidity.
Information about health outlook and quality of life among incident patients has come primarily from investigators in The Netherlands [8,17] and from the CHOICE study in the USA [2,9]. Merkus et al. [17] examined SF-36 responses from 84 HD and 55 PD patients in The Netherlands who remained on their initial modality. At 12 months after start of dialysis, stay on HD patients had a higher physical summary score than did stay on PD patients, while mental summary scores were very similar for the two groups. Among 18 patients randomized to HD and 20 patients randomized to PD by these investigators, the mean QALY score after 2 years was 59.1±12.0 for HD patients and 54.0±19.0 for PD patients when the investigators used the EuroQol to derive a single valuation of patients' overall health [8].
In the CHOICE study, 452 HD patients and 133 PD patients supplied health status and quality of life data near treatment start and again 1 year later [9]. Age, gender, race, education, albumin, creatinine and haematocrit were adjusted in analyses investigating the association of treatment modality with health status/quality of life; an additional covariate was a co-morbidity score based on multiple disease and physical impairment categories graded by level of severity. Patients' employment status, marital status, living situation, timing of pre-ESRD care and serum bicarbonate were not adjusted. Because the number of PD patients who provided data at 1 year was relatively small, the study had limited power to detect differences by patients' dialysis modality in the various subdomains of health status and quality of life, but the data indicated that patients on HD had better sexual functioning while patients on PD had better quality of life as measured by perceived ability to travel, financial concerns, restrictions in eating and drinking, and dialysis access problems [9]. Similarly, we found that DMMS Wave 2 patients on PD evaluated the effects of kidney disease on daily life more positively than did HD patients, and the effects of kidney disease on daily life scale includes items asking about dietary restriction and ability to travel. Positive assessment of these aspects of dialysis experience may influence prevalent PD patients to want to remain on their current modality [18].
Especially important, at baseline, DMMS Wave 2 PD patients in all five quintiles rated their encouragement from staff and their satisfaction with care higher than did HD patients (see Table 3), consistent with the results of the CHOICE study reported by Rubin et al. in which PD patients rated their dialysis care higher at treatment start than did patients initiating HD [2]. After 1 year of dialysis, PD patients in the DMMS Wave 2 continued to be more likely than HD patients to evaluate staff encouragement and satisfaction with care positively, as Table 5 highlights.
Individuals' work status (employed/not employed) can be viewed as one dimension of their quality of life; work status can also be viewed as a socio-demographic characteristic likely to influence individuals' self-assessed quality of life. In this study, we included patients' work status in the model that was developed to define patients' propensity for being selected to PD. Almost half of the patients in quintile I were working, regardless of modality. In quintile V, no patients on either modality were employed. As Hirth et al. also demonstrated [19], it is reasonable to conclude that patient characteristics drive labour force participation more than dialysis modality selection drives labour force participation. At the same time, using a therapy that does not require going regularly to a dialysis clinic for treatments makes employment more feasible, and patients may select or be recommended for PD to facilitate their ability to work [19].
We believe that our study provides support for making the choice of PD more widely available as an option to patients initiating chronic dialysis therapy. Patients who initiate PD may be able to enjoy a valued period of time when they are largely independent of the dialysis facility, and they are more likely to be able to continue jobs held prior to dialysis. Patient lifestyle opportunities and the overall cost advantages associated with use of PD, a home-based and self-care therapy, may also apply to home-based HD or in-centre self-care HD [2,10].
Vonesh et al. argue that valid comparisons of survival outcomes associated with HD and PD therapy require patient stratification according to major risk factors known to interact with treatment modality to influence patient survival [7]. We did not consider interactions of specific patient characteristics with treatment modality in this study, but we did consider a large number of potential confounders in our analyses. Dialysis adequacy, for which we did not have measures, would be an additional potential clinical confounder to consider [17]. Moreover, it would be informative to investigate reported health status and quality of life after stratifying patients on variables such as expectations regarding treatment [20] and attitudes toward self-management of health. Measuring variables such as these in addition to socio-demographic and clinical covariates, and determining how they may interact with treatment modality to shape patient experience, are important objectives for continued study.
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Acknowledgments |
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Conflict of interest statement. None declared.
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References |
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