Health profiles and health preferences of dialysis patients

G. Ardine de Wit1,, Maruschka P. Merkus2, Raymond T. Krediet3 and Frank Th. de Charro4

1 Sanders Institute, Erasmus University Rotterdam, and National Institute of Public Health and the Environment, Bilthoven, 2 Department of Clinical Epidemiology and Biostatistics, Academic Medical Centre, Amsterdam, 3 Renal Unit, Department of Medicine, Academic Medical Centre, Amsterdam and 4 Renal Replacement Registry of the Netherlands (Renine), Rotterdam, The Netherlands



   Abstract
 Top
 Abstract
 Introduction
 Subjects and methods
 Results
 Discussion
 References
 
Background. Health-related quality of life (HRQOL) of haemodialysis (HD) and peritoneal dialysis (PD) patients has been assessed with health profiles and health preferences methods. Few studies have used both types of HRQOL instruments. The main objective of this study was to assess the relationship between information from the two types of HRQOL instruments in dialysis patients.

Methods. We interviewed 135 patients, using two health profiles (Short Form 36 and EuroQol/EQ-5D) and two health preferences methods (Standard Gamble and Time Trade Off). Socio-demographic, clinical, and treatment-related background data were collected from patient charts and during the interview. Relationships between the outcome measures were assessed with Pearson correlation coefficients. Multiple regression models were used to study the relationship of HRQOL outcomes to background variables.

Results. The HRQOL of dialysis patients as measured with health profiles was severely impaired. The health preferences scores were higher (0.82–0.89) than scores previously reported in the literature. Correlations between health profiles and health preferences were poor to modest. HRQOL outcomes were poorly explained by background characteristics. Differences between HD and PD groups could not be demonstrated.

Conclusions. Health profiles and health preferences represent different aspects of HRQOL. An impaired health status may not be reflected in the preference scores. Coping strategies and other attitudes towards health may affect the preference scores more than they influence health profile outcomes. The added value of health preferences methods in clinical research is limited.

Keywords: haemodialysis; health-related quality of life; health status; peritoneal dialysis; preference measurements



   Introduction
 Top
 Abstract
 Introduction
 Subjects and methods
 Results
 Discussion
 References
 
Many different questionnaires and interview techniques, either generic or disease-specific, have been used for the assessment of health-related quality of life (HRQOL) in end-stage renal disease (ESRD) patients [1,2]. Generic HRQOL measures cover all important aspects of health and are intended to be applicable in a wide variety of conditions, patients, and demographic groups. Therefore, they can be used to compare a patient group suffering from a certain disease with other patient groups and with general population samples. Within the group of generic measures, a distinction can be made between health profiles and preference or utility-based measures [3]. Health profiles describe the health status of a person on a number of domains, such as physical, psychological, and social function. Preference-based measures aim to express HRQOL in a single indicator; often a number between 0 and 1, where 0 represents death and 1 represents full health.

The experience with preference measurements in dialysis patients is relatively limited. A MedLine literature search identified 16 studies using preference measurements in dialysis and renal transplant patients [419]. Most studies that applied preference measurements have assessed small patient groups and focused on renal transplantation and haemodialysis (HD). Peritoneal dialysis (PD) was only covered in two Canadian studies from the 1980s [4,7], and in one more recent publication that included 30 PD patients [14]. Only two studies reported on the relationship between health profiles and health preferences in ESRD patients [10,18]. Both studies found low to moderate correlations between the two types of instruments (correlation coefficients between 0.15 and 0.31). The purpose of the present cross-sectional study was to compare health preference methods with health profiles in HD and PD patients. Two health preferences methods (Standard Gamble (SG) and Time Trade Off (TTO)) and two health profiles (Short-Form 36 and EuroQol/EQ-5D) were used to study HRQOL.



   Subjects and methods
 Top
 Abstract
 Introduction
 Subjects and methods
 Results
 Discussion
 References
 
Study design and patients
A total number of 135 dialysis patients participated in this study. These patients participated in a prospective cohort study on the adequacy of dialysis, the NECOSAD-I study [20]. The 135 patients interviewed were treated in 13 of the 49 dialysis centres in The Netherlands. The study was approved by the ethical committees of all participating centres. In the period October 1993 to March 1995, all new patients in these 13 centres were asked to participate in NECOSAD. All patients who had not been withdrawn from NECOSAD by the time we started the present HRQOL study and who had received the same dialysis treatment for at least 3 months were considered for inclusion. Further inclusion criteria were written informed consent, age above 18 years, adequate eyesight to enable the administration of questionnaires and an adequate understanding of the Dutch language. Interviews were conducted at patients’ homes by one of three trial nurses, who received training to administer the HRQOL questionnaires. For HD patients, interviews were carried out on non-dialysis days.

Background variables
At the interview, data were collected on sex, age, marital and employment status, and educational level. Data on primary diagnosis, dialysis adequacy, current treatment, treatment history, length of time on dialysis, and presence of comorbid diseases at the start of dialysis were obtained from the NECOSAD study and the patient's nephrologist. Primary diagnosis of renal failure was classified according to the ERA–EDTA classification. Adequacy of dialysis was expressed as weekly total Kt/Vurea in HD and PD patients. HD Kt/Vurea was estimated using a second generation Daugirdas formula [21]. The weekly Kt/Vurea in PD patients was calculated as the peritoneal Kt/Vurea per 24 h multiplied by 7.

Questionnaires used to assess HRQOL
HRQOL was assessed with the Short-Form 36 (SF-36) Health Survey, EuroQol/EQ-5D, SG, and TTO. The four questionnaires were always administered in this sequence. The first two questionnaires were self-completed. The interviewer then continued with the administration of the SG and TTO.

The SF-36 Health Survey generates a profile of scores on eight dimensions of quality of life [22]. These dimensions are (i) physical functioning, (ii) role functioning—physical, (iii) bodily pain, (iv) general health perception, (v) vitality, (vi) social functioning, (vii) role functioning—emotional, (viii) mental health. Raw scores on the eight scales are transformed to calculate a score between 0 and 100, where a higher score indicates better health. The physical and mental components of the eight scales are combined into a Physical Component Summary (PCS) and a Mental Component Summary (MCS). The two summary measures are standardized to have a mean of 50 and a standard deviation of 10 in the general population and, therefore, allow for easy comparison of patient scores with general population scores. SF-36 scores of persons of similar age (55–64 years) were derived from a validation study in the Dutch population [23].

The EQ-5D or EuroQol is a generic questionnaire, consisting of a classification system (EQ-5Dprofile) and a Visual Analog Scale (EQVAS) [24]. The EQ-5Dprofile covers five domains of health (mobility, self-care, usual activities, pain/discomfort, anxiety/depression), each with three levels of functioning: (level 1, no problems; level 2, some problems; level 3, severe problems). The EQVAS is a graduated, vertical line, anchored at 0 (worst imaginable health state) and 100 (best imaginable health state). The patient is asked to mark a point on the EQVAS that best reflects his/her actual health state.

The SG is a method to measure preferences for health states [25]. The respondent is presented with two alternatives and asked to choose the one most preferred. The first alternative offers the certainty of staying in the current health state for the remainder of the respondent's life. The second alternative is a gamble with specified probabilities for both the positive outcome of the gamble (a normal health state for the remainder of time) and the negative outcome (immediate death). These probabilities are varied until the respondent is indifferent between the gamble and living in his/her current health state. The SG score, a score between zero and one, is calculated as one minus the risk percentage at the point of indifference, divided by one hundred. An SG score of 0.80 implies that a person is prepared to take a gamble with 20% risk of dying immediately and 80% chance to improve his current health to normal health. The SG score reflects the value a person assigns to his own health state. In our study, the concept of the SG was practiced with a visual aid, using imaginary health states. Afterwards, the patient was asked to value his own current health state.

The TTO method is also a preference-based method [4,7]. Patients are asked whether they are prepared to give up some remaining time of their lives, in order to improve their current health state to normal health. The time perspective that is presented to the patient corresponds with statistical life expectancy of people of the same age and sex. The quotient of the chosen number of years in a normal health state over statistical life expectancy yields the TTO score. A TTO score of 0.80 implies that a person is indifferent between living 8 years in excellent health vs 10 years in his current health state. We practiced the TTO concept with imaginary health states, before the patient was asked to value his/her own current health state.

Statistical analysis
Differences between HD and PD treatment groups were tested by means of Student's t-test or Mann–Whitney U-test, as appropriate. Categorical variables were compared using the Pearson {chi}2-test. Mann–Whitney U-test was used for non-response analysis. In order to be able to control for case-mix differences, the association between background variables (see above) and main quality of life outcomes was studied with multiple regression models. A forward stepwise selection strategy was chosen, using the F-statistic with P=0.05 as the criterion level for selection. To search for violations of necessary assumptions in multiple regression, normal plots of the residuals of the regression models were produced. The relationship between health profiles and health preference measures was assessed with Pearson correlation coefficients. Analyses reported here are based on treatment at the time of the interview. A two-sided P-value of 0.05 was chosen as cut off for statistical significance.



   Results
 Top
 Abstract
 Introduction
 Subjects and methods
 Results
 Discussion
 References
 
Patient characteristics
In April 1995, 193 patients still participated in the NECOSAD study. Eight patients (4%) could not be interviewed because they were medically unstable or had language problems. A group of 24 patients (13%) was withdrawn from follow-up in the NECOSAD study before an interview could be scheduled, either because of transplantation, death, or transfer to a non-participating dialysis centre. Finally, 26 patients (14%) refused to participate in the present study. This resulted in 135 patients (70%) who were interviewed.

Table 1Go lists the main demographic, clinical, and treatment characteristics of the 135 patients interviewed, according to treatment modality. Sixty-nine patients were treated with HD and 66 with PD (59 CAPD and seven APD). The HD and PD groups differed significantly with respect to age and educational level. On average, PD patients were 5 years younger and better educated than HD patients. No other demographic and clinical differences were found between the groups.


View this table:
[in this window]
[in a new window]
 
Table 1. Patient characteristics according to treatment modality (mean, SD, range, or per cent)

 

Results of SF-36
The upper part of Table 2Go contains the two SF-36 summary scores for physical (PCS) and mental (MCS) HRQOL. HD and PD patient groups did not differ with regard to PCS and MCS scores. The mean PCS score of this sample of dialysis patients was 1.2 SD (P<0.001) below the mean score for a general population sample of the same age. The mean MCS score of both groups of dialysis patients was not different from the reference group.


View this table:
[in this window]
[in a new window]
 
Table 2. Mean (SD and range) scores on health-status and health-preference questionnaires

 

Results of EQ-5D
As shown in Table 2Go, self-rated health status on the EQVAS was similar for both patient groups, with scores of 60 and 62 on a scale from 0 to 100. Table 3Go shows the proportion of HD and PD patients that indicated having some or severe problems on the five dimensions of the EQ-5Dprofile. None of the differences between HD and PD patients were significant. Patients turned out to have problems on all five dimensions. Most problems were present with ‘daily activities’: 61% of the patients could not perform their daily activities normally. Approximately half of the patients reported some or severe difficulties with ‘mobility’ and ‘pain’. Fourteen per cent of the patients had difficulties with self-care, such as bathing and dressing independently. In this patient sample, 24% of responders felt anxious and/or depressed.


View this table:
[in this window]
[in a new window]
 
Table 3. Proportion of HD and PD patients showing none (level 1), some (level 2), or severe (level 3) problems on EQ-5Dprofile

 

Results of SG and TTO
Answers to SG could not be obtained in five patients (3.7%) and answers to TTO could not be obtained in 14 (10.4%) patients. This non-response was caused by patient refusal to answer and/or cognitive problems in understanding the SG and TTO concepts. The reasons for refusal were diffuse and included religious reasons, familial circumstances, and patient fatigue. Responders and non-responders to SG and/or TTO were compared with respect to age, number of comorbid conditions, time on dialysis, and HRQOL as measured with health profiles. Results of the non-response analysis are shown in Table 4Go. Compared with responders, non-responders were older and had a worse self-rated health as assessed with the EQVAS.


View this table:
[in this window]
[in a new window]
 
Table 4. Analysis of non-response to SG (n=5) and/or TTO (n=14)

 
The lower part of Table 2Go shows the mean SG and TTO scores of both patient groups. HD and PD patients groups valued their health status equally high (P>0.05), with scores between 0.82 and 0.89.

Association between background variables and HRQOL outcomes
The results of the multiple regression analyses to explain the independent associations between demographic, clinical, and treatment variables on the one hand and outcome variables on the other hand are shown in Table 5Go. The number of comorbid conditions was negatively associated with all HRQOL outcomes, except MCS. Age was negatively associated with PCS and with the EQVAS score. Employed patients had better PCS and EQVAS scores. The number of months on dialysis was negatively associated with MCS and EQVAS scores. Treatment modality was not associated with any of the HRQOL outcomes. The models constructed showed that HRQOL was poorly explained by the background variables under study (total adjusted R2 from 1.9 to 18.2%).


View this table:
[in this window]
[in a new window]
 
Table 5. Multiple regression analysisa to study the association between demographic, clinical-, and treatment-related variables and outcome variables, expressed as standardized regression coefficient ß, partial R2 and total R2b

 

Relationships between health profiles and health preferences
The correlations between health profiles outcomes and health preference measurements are shown in Table 6Go. Correlations between the two types of questionnaires were poor to modest (r=0.03-0.31). With regard to the TTO, the highest correlations were found with SF-36 scales social functioning (r=0.29), bodily pain (r=0.23), and vitality (r=0.21). The SF-36 domains that correlated best with the SG score were vitality (r=0.31), mental health (r=0.29), and social functioning (r=0.24).


View this table:
[in this window]
[in a new window]
 
Table 6. Correlation (Pearson's r) between descriptive instruments and preference measurements

 


   Discussion
 Top
 Abstract
 Introduction
 Subjects and methods
 Results
 Discussion
 References
 
The present cross-sectional study using four different HRQOL measures showed a similar impairment of quality of life in HD and PD patients. Compared with a general population sample of similar age, impairments were most obvious in the physical components of health profiles, but much less for the mental components. The preference-based measures yielded relatively high scores for dialysis patients. Multiple regression analysis showed that background variables, such as the presence of comorbid diseases, explained 15% of physical HRQOL and 18% of the VAS. Correlations between the different HRQOL tests were poor to modest. These findings will be discussed in the following sections.

The severely reduced physical HRQOL of dialysis patients in comparison with the general population has been reported in many other studies (reviewed in [26]). The equivalence of HRQOL in HD and PD patients found in the present study is in accordance with the results of other recently published investigations [27,28], but could also be related to the inadequate power to detect differences between groups. Given the number of patients included in our study, the power was adequate (ß>80%) to detect differences of 5.3 (PCS) to 5.7 (MCS) units in the scale scores between PD and HD patient groups. Our study adds to the existing nephrologic HRQOL literature because we have not only applied health status measures but also health preference measurements. Preference-based instruments have been used less often than health status measures and only three studies have reported on health preferences of PD patients [4,7,14]. The present study has shown that health preferences of HD and PD patients were similar. A remarkable finding of the present study was the high scores (0.82–0.89) obtained using the preference measurements. These values indicate that patients on average valued their current health state as 82–89% of normal health. The average TTO value found in the present study (0.88), was similar to the value found after renal transplantation (0.87) in a study from the 1980s [4]. Typical values of prevalent dialysis patients are in the 0.40–0.70 range, with two exceptions of patients reporting values above 0.80 [10,19]. Highest scores were found in patients with a renal transplant [4,16], in patients using erythropoietin [9], and in more recently published studies [6,10,12,19].

Why do our scores differ from scores found previously? We excluded patients with language or vision problems from our study, but, due to the nature of preference measurement, such positive patient selection must also have been present in other series. Besides, the SF-36 outcomes in our patients are similar to other published SF-36 scores of dialysis patients [29,30], making it less likely that our higher preference scores are caused by selection bias. Our patients were recruited from a clinical study on adequacy of dialysis. Consequently, patients were monitored intensively by highly motivated staff members. This may have had a positive influence on perceived HRQOL. Further, widespread use of erythropoietin (Epo) among study patients (85%) may have played a role. Most of the previous work on preferences of dialysis patients stems from the pre-Epo period. Of the three studies that have reported on the contribution of Epo to health preference scores, one small study (n=28) showed higher TTO scores after introduction of Epo [9]. However, a large (n=118) placebo-controlled randomized trial [5] and an observational study in 40 patients [17] showed stable TTO scores after the introduction of Epo, despite improvements in the physical and fatigue domains of HRQOL. Given the fact that the best evidence is provided by the randomized trial, we do not consider Epo use as an important factor to explain our relatively high preference scores.

The influence of non-responders on the high average preference scores has at best been limited. The patients in our sample that did not respond to SG and/or TTO (12%) were older and had a worse self-rated health than responders. In the unlikely event that all non-responders to the TTO had valued their current health state at 0, the average TTO score still would have been 0.79. Finally, it has to be considered that preference-based methods are less reliable than suggested [4,7,31], at least in a cross-cultural context, because cultural differences present between countries or continents might have a strong influence on the valuation of health. In the present study, some patients refused to answer SG and TTO for religious reasons. It is possible that this religious factor resulted in unwillingness of patients to trade-off quality and quantity of life or to accept a gamble with a negative outcome, resulting in higher scores than in non- or less religious populations. Also, cultural differences in the attitude towards risk may exist. Our finding of high preference values in Dutch dialysis patients is in accordance with data from other international comparative research [32], which showed that The Netherlands, among 48 countries, scored highest on several well-being scales. A positive general attitude to life in The Netherlands might, therefore, have influenced preference scores in our study. We suggest that health preference scores of similar patient populations may not easily be compared if elicited in different countries or continents. The influence of cultural differences on health preferences and the transferability of study results to other countries remains a subject for future study.

Correlations between health preferences methods and health profiles were absent to moderate (maximal Pearson's r 0.31). This finding is in accordance with results of previous research in ESRD patients [10,18], pre-dialysis patients [33], and other seriously ill patients [34,35], but could also be contributed to insufficient variance in the data or insufficient statistical power to detect correlations. Correlation as a measure of association depends on the variance of values found. Because patients’ preference scores in the present study concentrated at the upper end of the scale, high correlations are unlikely. The sample size used was large enough to detect correlations as low as 0.25 with adequate statistical power, but most correlation coefficients were below that threshold. Two studies which have used multiple regression analyses to study the independent associations between health profiles and health preferences found R2 values of 19 and 0% [33,34]. We have not reported such multivariate analyses because it is instantly clear from the low Pearson correlation coefficients (Table 6Go) that health preferences scores cannot be explained by health profile outcomes. The implication of this finding is that both types of questionnaires truly reflect different aspects of the HRQOL concept. The health profiles assess patients’ functioning on different domains of quality of life, whereas the health preferences methods elicit individual judgements of the value of the current health status, relative to full health and death. The implication is that the two types of questionnaires may lead to different conclusions on HRQOL of dialysis patients. The descriptive questionnaires SF-36 and EQ-5D indicated that quality of life of dialysis patients in this study was severely impaired. Despite these impairments, patients valued their health status as high as 82–89% of normal health. The discrepancy between the results of the descriptive questionnaires and the preference measurements might be explained by the fact that the coping mechanism, through which patients gradually learn to adapt to their new situation and to accept the fact that it will remain unchanged, is more reflected in the preference-based methods than in the descriptive questionnaires. The reality for many ESRD patients seems to be that, despite the severe physical limitations experienced in everyday life, they subjectively experience a relatively high quality of life.

The multivariate regression analyses (Table 5Go) failed to show obvious relationships between socio-demographic-, clinical-, and treatment-related variables and health preferences scores. Previous research showed that health preferences were also not correlated with clinical variables, such as haematocrit, haemoglobin, and glomerular filtration rate [33]. If health preferences scores are poorly explained by both health status (Table 6Go) and socio-demographic, clinical-, and treatment-related variables (Table 5Go), what else constitutes a person's preference score? Besides, by coping behaviour of patients, the preference scores may be influenced by beliefs about health, previous experiences and knowledge, a person's attitude towards risk and time and non-health related factors, such as financial status, family circumstances, and social support [31]. These confounders hamper the strict interpretation of the preference scores as the valuation of health status only and, consequently, the use of health preference methods in clinical HRQOL studies, especially in populations of chronic patients. In such populations, the coping process may prevent patients from using the whole range of possible scores [36]. Two of the three prospective Epo studies that used the TTO instrument reported no difference in health values after treatment with Epo had been started [5,17]. Also, a study in survivors of myocardial infarction concluded that health values are stable over time, despite changes in health status [37]. This further reduces the usefulness of health preference methods, especially in prospective studies. Other disadvantages of health preferences methods include the necessity of an interviewer situation, the relatively high non-response and the unknown influence of cross-cultural factors on health values of patients. Until these issues are resolved, the use of health preferences methods should be limited to a research context.



   Acknowledgments
 
Parts of this study were published in abstract form in Peritoneal Dialysis International 1997 volume 17, Supplement 1. We are indebted to Roos Wisse, Barbara Nijman, and Rita Morren for the conduction of interviews, to Jan van Busschbach, Frans Rutten, and Sjoerd Terpstra for comments on earlier drafts of this paper and to Rosalind Rabin, who assisted with English language editing. We thank the following members of the NECOSAD Study Group and nephrologists from the participating dialysis centres for their cooperation: J. Barendregt, Maastricht; M. Boekhout, Leiderdorp; E. W. Boeschoten, Amsterdam; W. J. W. Bos, Nieuwegein; H. R. Buller, Amsterdam; F. W. Dekker, Amsterdam; W. Geerlings, ‘s-Gravenhage; P. G. G. Gerlag, Veldhoven; J. P. M. C. Gorgels, Haarlem; R. M. Huisman, Groningen; K. J. Jager, Amsterdam; W. A. H. Koning-Mulder, Enschede; M. I. Koolen, ‘s-Hertogenbosch; K. M. L. Leunissen, Maastricht; K. J. Parlevliet, Arnhem; C. H. Schroder, Utrecht; P. Stevens, Amsterdam; J. G. P. Tijssen, Amsterdam; R. M. Valentijn, ‘s-Gravenhage; A. van Es, Hilversum; J. A. C. A. van Geelen, Alkmaar; L. van Leusen, Arnhem; H. H. Vincent, Nieuwegein; P. Vos, Utrecht.



   Notes
 
Correspondence and offprint requests to: G. A. de Wit, National Institute of Public Health and Environment (RIVM), Department for Health Services Research CZO, PO Box 1, NL-3720 BA Bilthoven, The Netherlands. Email: ardine.de.wit{at}rivm.nl Back



   References
 Top
 Abstract
 Introduction
 Subjects and methods
 Results
 Discussion
 References
 

  1. Rettig RA, Sadler JH, Meyer KB et al. Assessing health and quality of life outcomes in dialysis: a report on an Institute of Medicine workshop. Am J Kidney Dis1997; 30: 140–155[ISI][Medline]
  2. Gokal N. Quality of life. In: Gokal R, Nolph KD, eds. The Textbook of Peritoneal Dialysis. Kluwer Academic Publishers, Dordrecht, 1994; 679–698
  3. Guyatt GH, Feeny DH, Patrick DL. Measuring health-related quality of life. Ann Int Med1993; 118: 622–629[Abstract/Free Full Text]
  4. Churchill DN, Torrance GW, Taylor DW et al. Measurement of quality of life in end stage renal disease: the time trade-off approach. Clin Invest Med1987; 10: 14–20[ISI][Medline]
  5. Canadian Erythropoietin Study Group. Association between recombinant human erythropoietin and quality of life and exercise capacity of patients receiving hemodialysis. Br Med J1990; 300: 573–578[ISI][Medline]
  6. Laupacis A, Keown P, Pus N et al. A study of the quality of life and cost-utility of renal transplantation. Kidney Int1996; 50: 235–242[ISI][Medline]
  7. Churchill DN, Morgan J, Torrance GW. Quality of life in end stage renal disease. Perit Dial Bull1984; 4: 20–23[ISI]
  8. Churchill DN, Wallace JE, Ludwin D, Beecroft ML, Taylor DW. A comparison of evaluative indices of quality of life and cognitive function in hemodialysis patients. Control Clin Trials1991; 12 [Suppl]: S159–S167[ISI]
  9. Harris DCH, Chapman JR, Stewart JH, Lawrence S, Roger SD. Low dose erythropoietin in maintenance hemodialysis: improvement in quality of life and reduction in true cost of hemodialysis. Aust N Z J Med1991; 21: 693–700[ISI][Medline]
  10. Hornberger JC, Redelmeier DA, Petersen J. Variability among methods to assess patients' well-being and consequent effect on a cost-effectiveness study. J Clin Epidemiol1992; 45: 505–512[ISI][Medline]
  11. Russell JD, Beecroft ML, Ludwin D, Churchill DN. The quality of life in renal transplantation—a prospective study. Transplantation1992; 54: 656–660[ISI][Medline]
  12. Sesso R, Yoshihiro MM. Time of diagnosis of chronic renal failure and assessment of quality of life in haemodialysis patients. Nephrol Dial Transplant1997; 12: 2111–2116[Abstract]
  13. Sesso R, Yoshihiro MM, Ajzen H. Late diagnosis of chronic renal failure and the quality of life during dialysis treatment. Braz J Med Biol Res1996; 29: 1283–1289[ISI][Medline]
  14. Molzahn AE, Northcott HC, Dossetor JB. Quality of life of individuals with end stage renal disease: perceptions of patients, nurses and physicians. ANNA J1997; 24: 325–333[Medline]
  15. Churchill DN, Bird DR, Taylor DW, Beecroft ML, Gorman J, Wallace JE. Effect of high-flux hemodialysis on quality of life and neuropsychological function in chronic hemodialysis patients. Am J Nephrol1992; 12: 412–418[ISI][Medline]
  16. Laupacis A, Pus N, Muirhead N, Wong C, Ferguson B, Keown P. Disease-specific questionnaire for patients with a renal transplant. Nephron1993; 64: 226–231[ISI][Medline]
  17. Muirhead N, Cattran DC, Zaltzman J et al. Safety and efficacy of recombinant human erythropoietin in correcting the anemia of patients with chronic renal allograft dysfunction. J Am Soc Nephrol1994; 5: 1216–1222[Abstract]
  18. Laupacis A, Wong C, Churchill D, and the Canadian Erythropoietin Study Group. The use of generic and specific quality-of-life measures in hemodialysis patients treated with erythorpoietin. Control Clin Trials1991; 12 [Suppl]: S168–S179[Medline]
  19. Lenert LA, Hornberger JC. Computer-assisted quality of life assessment for clinical trials. Proc AMIA Annu Fall Symp1996; 992–996
  20. Merkus MP, Jager KJ, Dekker FW, Boeschoten EW, Stevens P, Krediet RT and The Necosad Study Group. Quality of life in patients on chronic dialysis: self-assessment 3 months after the start of treatment. Am J Kidney Dis1997; 29: 584–592[ISI][Medline]
  21. Daugirdas JT. The post:pre dialysis plasma urea nitrogen ratio to estimate Kt/V and nPCR: Mathematic modelling and validation. Int J Artif Organs1989; 12: 411–419[ISI][Medline]
  22. Ware JE, Sherbourne CD. The MOS 36-item Short-Form Health Survey: 1. Conceptual framework and item selection. Med Care1992; 30: 473–483[ISI][Medline]
  23. Van der Zee K, Sanderman R. Measuring health state with the RAND-36. A manual (in Dutch). Groningen, Northern Center for Health Care Research; 1995
  24. Brooks R, with the EuroQol Group. EuroQol: the current state of play. Health Policy1996; 37: 53–72[ISI][Medline]
  25. Torrance GW, Thomas WH, Sackett DL. A utility maximization model for evaluation of health care programs. Health Serv Res1972; 7: 118–133[Medline]
  26. Merkus MP, Krediet RT. Quality of life and functional status in chronic hemo- and peritoneal dialysis. In: Lameire HN, Mehta RL, eds. Complications of Dialysis. Recognition and Management. Marcel Dekker Inc., New York: 2000: 497–515
  27. Gudex CM. Health-related quality of life in end-stage renal failure. Qual Life Res1995; 4: 359–366[ISI][Medline]
  28. Moreno F, Lopez Gomez JM, Sanz-Guajardo D, Jofre R, Valderrabano F, on behalf of the Spanish Cooperative Renal Patients Quality of Life Study Group. Quality of life in dialysis patients. A Spanish multicentre study. Nephrol Dial Transplant1996; 11 [Suppl 2]: 125–129[Abstract]
  29. Beusterien KM, Nissenson AR, Port FK, Kelly M, Steinwald B, Ware JE. The effects of recombinant human erythropoietin on functional health and well-being in chronic dialysis patients. J Am Soc Nephrol1996; 7: 763–773[Abstract]
  30. DeOreo PB. Hemodialysis patient-assessed functional health status predicts continued survival, hospitalization and dialysis-attendance compliance. Am J Kidney Dis1997; 30: 204–212[ISI][Medline]
  31. Torrance GW. Measurement of health state utilities for economic appraisal: a review article. J Health Econ1986; 5: 1–30[ISI][Medline]
  32. Veenhoven R. Happy life-expectancy. A comprehensive measure of quality-of-life in nations. Soc Indicators Res1996; 39: 1–58[ISI]
  33. Revicki DA. Relationship between health utility and psychometric health status measures. Med Care1992; 30 [Suppl]: S274–S282
  34. Revicki DA, Kaplan RM. Relationship between psychometric and utility-based approaches to the measurement of health-related quality of life. Qual Life Res1993; 2: 477–487[ISI][Medline]
  35. Llewellyn-Thomas HA, Sutherland HJ, Tritchler DL et al. Benign and malignant breast disease: the relationship between women's health status and health values. Med Decis Making1991; 11: 180–188[ISI][Medline]
  36. De Wit GA, Busschbach JJV, de Charro FT. Sensitivity and perspective in the valuation of health status: whose values count? Health Econ2000; 9: 109–126[ISI][Medline]
  37. Tsevat J, Goldman L, Soukup JR et al. Stability of time-tradeoff utilities in survivors of myocardial infarction. Med Decis Making1993; 13: 161–165[ISI][Medline]
Received for publication: 7. 8.00
Revision received 11. 9.01.