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      A Systematic Review and Meta-Analysis of Utility-Based Quality of Life in Chronic Kidney Disease Treatments

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          Melanie Wyld and colleagues examined previously published studies to assess pooled utility-based quality of life of the various treatments for chronic kidney disease. They conclude that the highest utility was for kidney transplants, with home-based automated peritoneal dialysis being second.



          Chronic kidney disease (CKD) is a common and costly condition to treat. Economic evaluations of health care often incorporate patient preferences for health outcomes using utilities. The objective of this study was to determine pooled utility-based quality of life (the numerical value attached to the strength of an individual's preference for a specific health outcome) by CKD treatment modality.

          Methods and Findings

          We conducted a systematic review, meta-analysis, and meta-regression of peer-reviewed published articles and of PhD dissertations published through 1 December 2010 that reported utility-based quality of life (utility) for adults with late-stage CKD. Studies reporting utilities by proxy (e.g., reported by a patient's doctor or family member) were excluded.

          In total, 190 studies reporting 326 utilities from over 56,000 patients were analysed. There were 25 utilities from pre-treatment CKD patients, 226 from dialysis patients (haemodialysis, n = 163; peritoneal dialysis, n = 44), 66 from kidney transplant patients, and three from patients treated with non-dialytic conservative care. Using time tradeoff as a referent instrument, kidney transplant recipients had a mean utility of 0.82 (95% CI: 0.74, 0.90). The mean utility was comparable in pre-treatment CKD patients (difference = −0.02; 95% CI: −0.09, 0.04), 0.11 lower in dialysis patients (95% CI: −0.15, −0.08), and 0.2 lower in conservative care patients (95% CI: −0.38, −0.01). Patients treated with automated peritoneal dialysis had a significantly higher mean utility (0.80) than those on continuous ambulatory peritoneal dialysis (0.72; p = 0.02). The mean utility of transplant patients increased over time, from 0.66 in the 1980s to 0.85 in the 2000s, an increase of 0.19 (95% CI: 0.11, 0.26). Utility varied by elicitation instrument, with standard gamble producing the highest estimates, and the SF-6D by Brazier et al., University of Sheffield, producing the lowest estimates. The main limitations of this study were that treatment assignments were not random, that only transplant had longitudinal data available, and that we calculated EuroQol Group EQ-5D scores from SF-36 and SF-12 health survey data, and therefore the algorithms may not reflect EQ-5D scores measured directly.


          For patients with late-stage CKD, treatment with dialysis is associated with a significant decrement in quality of life compared to treatment with kidney transplantation. These findings provide evidence-based utility estimates to inform economic evaluations of kidney therapies, useful for policy makers and in individual treatment discussions with CKD patients.

          Editors' Summary


          Ill health can adversely affect an individual's quality of life, particularly if caused by long-term (chronic) conditions, such as chronic kidney disease—in the United States alone, 23 million people have chronic kidney disease, of whom 570,000 are treated with dialysis or kidney transplantation. In order to measure the cost-effectiveness of interventions to manage medical conditions, health economists use an objective measurement known as quality-adjusted life years. However, although useful, quality-adjusted life years are often criticized for not taking into account the views and preferences of the individuals with the medical conditions. A measurement called a utility solves this problem. Utilities are a numerical value (measured on a 0 to 1 scale, where 0 represents death and 1 represents full health) of the strength of an individual's preference for specified health-related outcomes, as measured by “instruments” (questionnaires) that rate direct comparisons or assess quality of life.

          Why Was This Study Done?

          Previous studies have suggested that, in people with chronic kidney disease, quality of life (as measured by utility) is higher in those with a functioning kidney transplant than in those on dialysis. However, currently, it is unclear whether the type of dialysis affects quality of life: hemodialysis is a highly technical process that directly filters the blood, usually must be done 2–4 times a week, and can only be performed in a health facility; peritoneal dialysis, in which fluids are infused into the abdominal cavity, can be done nightly at home (automated peritoneal dialysis) or throughout the day (continuous ambulatory peritoneal dialysis). In this study, the researchers reviewed and assimilated all of the available evidence to investigate whether quality of life in people with chronic kidney disease (as measured by utility) differed according to treatment type.

          What Did the Researchers Do and Find?

          The researchers did a comprehensive search of 11 databases to identify all relevant studies that included people with severe (stage 3, 4, or 5) chronic kidney disease, their form of treatment, and information on utilities—either reported directly, or included in quality of life instruments (SF-36), so the researchers could calculate utilities by using a validated algorithm. The researchers also recorded the prevalence rates of diabetes in study participants. Then, using statistical models that adjusted for various factors, including treatment type and the method of measuring utilities, the researchers were able to calculate the pooled utilities of each form of treatment for chronic kidney disease.

          The researchers included 190 studies, representing over 56,000 patients and generating 326 utility estimates, in their analysis. The majority of utilities (77%) were derived through the SF-36 questionnaire via calculation. Of the 326 utility estimates, 25 were from patients pre-dialysis, 226 were from dialysis patients (the majority of whom were receiving hemodialysis), 66 were from kidney transplant patients, and three were from conservative care patients. The researchers found that the highest average utility was for those who had renal transplantation, 0.82, followed by the pre-dialysis group (0.80), dialysis patients (0.71), and, finally, patients receiving conservative care (0.62). When comparing the type of dialysis, the researchers found that there was little difference in utility between hemodialysis and peritoneal dialysis, but patients using automated peritoneal dialysis had, on average, a higher utility (0.80) than those treated with continuous ambulatory peritoneal dialysis (0.72). Finally, the researchers found that patient groups with diabetes had significantly lower utilities than those without diabetes.

          What Do These Findings Mean?

          These findings suggest that in people with chronic kidney disease, renal transplantation is the best treatment option to improve quality of life. For those on dialysis, home-based automated peritoneal dialysis may improve quality of life more than the other forms of dialysis: this finding is important, as this type of dialysis is not as widely used as other forms and is also cheaper than hemodialysis. Furthermore, these findings suggest that patients who choose conservative care have significantly lower quality of life than patients treated with dialysis, a finding that warrants further investigation. Overall, in addition to helping to inform economic evaluations of treatment options, the information from this analysis can help guide clinicians caring for patients with chronic kidney disease in their discussions about possible treatment options.

          Additional Information

          Please access these websites via the online version of this summary at

          Related collections

          Most cited references 20

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          The use of fractional polynomials to model continuous risk variables in epidemiology.

          The traditional method of analysing continuous or ordinal risk factors by categorization or linear models may be improved. We propose an approach based on transformation and fractional polynomials which yields simple regression models with interpretable curves. We suggest a way of presenting the results from such models which involves tabulating the risks estimated from the model at convenient values of the risk factor. We discuss how to incorporate several continuous risk and confounding variables within a single model. The approach is exemplified with data from the Whitehall I study of British Civil Servants. We discuss the approach in relation to categorization and non-parametric regression models. We show that non-linear risk models fit the data better than linear models. We discuss the difficulties introduced by categorization and the advantages of the new approach. Our approach based on fractional polynomials should be considered as an important alternative to the traditional approaches for the analysis of continuous variables in epidemiological studies.
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            A study of the quality of life and cost-utility of renal transplantation.

            The objective of this study was to assess the cost-utility of renal transplantation compared with dialysis. To accomplish this, a prospective cohort of pre-transplant patients were followed for up to two years after renal transplantation at three University-based Canadian hospitals. A total of 168 patients were followed for an average of 19.5 months after transplantation. Health-related quality of life was assessed using a hemodialysis questionnaire, a transplant questionnaire, the Sickness Impact Profile, and the Time Trade-Off Technique. Fully allocated costs were determined by prospectively recording resource use in all patients. A societal perspective was taken. By six months after transplantation, the mean health-related quality of life scores of almost all measures had improved compared to pre-transplantation, and they stayed improved throughout the two years of follow up. The mean time trade-off score was 0.57 pre-transplant and 0.70 two years after transplantation. The proportion of individuals employed increased from 30% before transplantation to 45% two years after transplantation. Employment prior to transplantation [relative risk (RR) = 23], graft function (RR 10) and age (RR 1.6 for every decrease in age by one decade), independently predicted employment status after transplantation. The cost of pre-transplant care ($66,782 Can 1994) and the cost of the first year after transplantation ($66,290) were similar. Transplantation was considerably less expensive during the second year after transplantation ($27,875). Over the two years, transplantation was both more effective and less costly than dialysis. This was true for all subgroups of patients examined, including patients older than 60 and diabetics. We conclude that renal transplantation was more effective and less costly than dialysis in all subgroups of patients examined.
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              Introducing economic and quality of life measurements into clinical studies.

              Although the collection of cost and quality of life data alongside clinical studies generates detailed patient level data in a timely fashion, it also raises practical and methodological challenges. These include the fact that the settings and patients enrolled in trials may not be typical of those found in regular clinical practice, that costs and quality of life may be influenced by the trial protocol, that the clinical alternatives compared in trials may not be the most relevant for cost-effectiveness assessments, that the length of follow-up may be too short to observe changes in cost and quality of life, and that adding these data will increase the overall measurement burden in the trial. This paper discusses these challenges and the ways in which they might be overcome, focussing particularly on preference-based measures of quality of life. In particular, recommendations are given for choosing the range of quality of life instruments, sample size calculations for quality of life measurement and the measurement of quality of life in multinational studies.

                Author and article information

                Role: Academic Editor
                PLoS Med
                PLoS Med
                PLoS Medicine
                Public Library of Science (San Francisco, USA )
                September 2012
                September 2012
                11 September 2012
                : 9
                : 9
                [1 ]Sydney School of Public Health, University of Sydney, Sydney, New South Wales, Australia
                [2 ]School of Public Health and Community Medicine, University of New South Wales, Sydney, New South Wales, Australia
                [3 ]Centre for Transplant and Renal Research, Westmead Hospital, Westmead, New South Wales, Australia
                University of Edinburgh, United Kingdom
                Author notes

                The authors have declared that no competing interests exist.

                Conceived and designed the experiments: MW RLM AH KH ACW. Performed the experiments: MW RLM AH KH ACW. Analyzed the data: MW RLM AH KH ACW. Contributed reagents/materials/analysis tools: AH. Wrote the first draft of the manuscript: MW. Contributed to the writing of the manuscript: MW RLM AH KH ACW. ICMJE criteria for authorship read and met: MW RLM AH KH ACW. Agree with manuscript results and conclusions: MW RLM AH KH ACW.


                This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                Pages: 10
                No specific funding was received for this study. MW was supported by a summer scholarship stipend. RM was supported through National Health and Medical Research Council grants #457281 and #571372. AH was supported through a National Health and Medical Research Council grant #633003. KH and AW were personally salaried by their institutions during the period of writing (though no specific salary was set aside or given for the writing of this paper). No funding bodies had any role in the study design, data collection, analysis, decision to publish or preparation of the manuscript.
                Research Article
                Acute Renal Failure
                Chronic Kidney Disease
                Renal Transplantation
                Non-Clinical Medicine
                Health Care Policy
                Quality of Life
                Social and Behavioral Sciences
                Health Economics



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