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      Eliciting patient preferences, priorities and trade-offs for outcomes following kidney transplantation: a pilot best–worst scaling survey

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          Abstract

          Objectives

          Eliciting preferences and trade-offs that patients may make to achieve important outcomes, can assist in developing patient-centred research and care. The pilot study aimed to test the feasibility of a case 2 best–worst scaling survey (BWS) to elicit recipient with kidney transplantation preferences after transplantation.

          Design

          Preferences for graft survival and dying, cancer, cardiovascular disease, diabetes, infection and side effects (gastrointestinal, weight-gain and appearance) were assessed in recipients with transplantation using a BWS (20 scenarios of nine outcomes). Participants chose ‘best’ and ‘worst’ outcomes. Responses were analysed using a multinomial logit model. Selected participants were interviewed.

          Outcomes

          Attribute coefficients and survey completion error rates.

          Results

          81 recipients with transplantation were approached, and 39 (48%), mean age 50.5 years, completed the BWS. 4 (10%) surveys were invalid with major errors and of 35 remaining, 7 of 1400 (0.5%) choices were missing. –23 (59%) took >20 min to complete the survey. 1 was unable to finish, and 1 did not understand the survey. 2 (5%) found it very hard and 14 (35%) moderately hard. Most attribute coefficients were significant (p<0.05) and showed face validity. Graft survival was most important with normalised coefficients from 1 (95% CI 0.89 to 1.11) to 0.06 (95% CI −0.03 to 0.16) for 30 and 1 year duration, respectively. Attribute level coefficients decreased with increasing risk of adverse outcomes. Error rates of 20% and 2% were estimated for dominant attributes ‘100% risk of dying’ and ‘30 years graft survival’, respectively. 7 participants were interviewed regarding counterintuitive selection of ‘100% risk of dying’ as a ‘best’ outcome. Misunderstanding, not linking dying to graft survival and aversion to dialysis were reasons given.

          Conclusions

          Recipients with transplant recipients successfully completed a complex case 2 BWS with attribute coefficients having face validity with respect to duration of graft survival and risk of adverse outcomes. Areas for refinement to reduce complexity in design have been identified.

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          Most cited references23

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          Long-term renal allograft survival in the United States: a critical reappraisal.

          K E Lamb, S. Lodhi, H.‐U. Meier‐Kriesche (2011)
          Renal allograft survival has increased tremendously over past decades; this has been mostly attributed to improvements in first-year survival. This report describes the evolution of renal allograft survival in the United States where a total of 252 910 patients received a single-organ kidney transplant between 1989 and 2009. Half-lives were obtained from the Kaplan-Meier and Cox models. Graft half-life for deceased-donor transplants was 6.6 years in 1989, increased to 8 years in 1995, then after the year 2000 further increased to 8.8 years by 2005. More significant improvements were made in higher risk transplants like ECD recipients where the half-lives increased from 3 years in 1989 to 6.4 years in 2005. In low-risk populations like living-donor-recipients half-life did not change with 11.4 years in 1989 and 11.9 years in 2005. First-year attrition rates show dramatic improvements across all subgroups; however, attrition rates beyond the first year show only small improvements and are somewhat more evident in black recipients. The significant progress that has occurred over the last two decades in renal transplantation is mostly driven by improvements in short-term graft survival but long-term attrition is slowly improving and could lead to bigger advances in the future. ©2010 The Authors Journal compilation©2010 The American Society of Transplantation and the American Society of Transplant Surgeons.
          Article 0 comments Cited 270 times – based on 0 reviews     Review now
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            Patient preferences versus physicians' judgement: does it make a difference in healthcare decision making?

            Christin Juhnke, C Mühlbacher (2013)
            Clinicians and public health experts make evidence-based decisions for individual patients, patient groups and even whole populations. In addition to the principles of internal and external validity (evidence), patient preferences must also influence decision making. Great Britain, Australia and Germany are currently discussing methods and procedures for valuing patient preferences in regulatory (authorization and pricing) and in health policy decision making. However, many questions remain on how to best balance patient and public preferences with physicians' judgement in healthcare and health policy decision making. For example, how to define evaluation criteria regarding the perceived value from a patient's perspective? How do physicians' fact-based opinions also reflect patients' preferences based on personal values? Can empirically grounded theories explain differences between patients and experts-and, if so, how? This article aims to identify and compare studies that used different preference elicitation methods and to highlight differences between patient and physician preferences. Therefore, studies comparing patient preferences and physician judgements were analysed in a review. This review shows a limited amount of literature analysing and comparing patient and physician preferences for healthcare interventions and outcomes. Moreover, it shows that methodology used to compare preferences is diverse. A total of 46 studies used the following methods-discrete-choice experiments, conjoint analyses, standard gamble, time trade-offs and paired comparisons-to compare patient preferences with doctor judgements. All studies were published between 1985 and 2011. Most studies reveal a disparity between the preferences of actual patients and those of physicians. For most conditions, physicians underestimated the impact of intervention characteristics on patients' decision making. Differentiated perceptions may reflect ineffective communication between the provider and the patient. This in turn may keep physicians from fully appreciating the impact of certain medical conditions on patient preferences. Because differences exist between physicians' judgement and patient preferences, it is important to incorporate the needs and wants of the patient into treatment decisions.
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              Valuing citizen and patient preferences in health: recent developments in three types of best-worst scaling.

              Terry N Flynn (2010)
              There is increased interest in the use of best-worst scaling (BWS) as a method of preference elicitation in health. However, the method is undergoing rapid development in several fields, making dissemination of new insights challenging. Furthermore, there are two types of BWS that have hitherto received little interest in health, but that are uniquely placed to address certain issues. This article offers an update of the state of play of BWS, presents original research to illustrate new methods of analysis and introduces to health researchers some issues on the research frontier.
              Article 0 comments Cited 60 times – based on 0 reviews     Review now
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                Author and article information

                Journal
                Journal ID (nlm-ta): BMJ Open
                Journal ID (iso-abbrev): BMJ Open
                Journal ID (hwp): bmjopen
                Journal ID (publisher-id): bmjopen
                Title: BMJ Open
                Publisher: BMJ Publishing Group (BMA House, Tavistock Square, London, WC1H 9JR )
                ISSN (Electronic): 2044-6055
                Publication date Collection: 2016
                Publication date (Electronic): 25 January 2016
                Volume: 6
                Issue: 1
                Electronic Location Identifier: e008163
                Affiliations
                [1 ]Centre for Kidney Research, The Children's Hospital at Westmead , Westmead, New South Wales, Australia
                [2 ]School of Public Health, The University of Sydney , Sydney, New South Wales, Australia
                [3 ]Centre for Transplant and Renal Research, Westmead Hospital , Westmead, New South Wales, Australia
                [4 ]Institute for Choice, University of South Australia Business School , North Sydney, New South Wales, Australia
                Author notes
                [Correspondence to ] Dr Martin Howell; martin.howell@ 123456health.nsw.gov.au
                Article
                Publisher ID: bmjopen-2015-008163
                DOI: 10.1136/bmjopen-2015-008163
                PMC ID: 4735165
                PubMed ID: 26810994
                SO-VID: 4d11b1af-6d72-497a-84fb-ad0023e2b9de
                Copyright © Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://www.bmj.com/company/products-services/rights-and-licensing/
                License:

                This is an Open Access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/

                History
                Date received : 11 March 2015
                Date revision received : 2 November 2015
                Date accepted : 5 January 2016
                Categories
                Subject: Patient-Centred Medicine
                Subject: Research
                Subject: 1506
                Subject: 1722
                Subject: 1728
                Subject: 1730
                Subject: 1701

                ScienceOpen disciplines: Medicine
                Keywords: transplant medicine,statistics & research methods,health economics
                Data availability:
                ScienceOpen disciplines: Medicine
                Keywords: transplant medicine, statistics & research methods, health economics

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