Standardised Outcomes in Nephrology—Polycystic Kidney Disease (SONG-PKD): study protocol for establishing a core outcome set in polycystic kidney disease

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Abstract

Background

Autosomal dominant polycystic kidney disease (ADPKD) is the most common potentially life threatening inherited kidney disease and is responsible for 5–10% of cases of end-stage kidney disease (ESKD). Cystic kidneys may enlarge up to 20 times the weight of a normal kidney due to the growth of renal cysts, and patients with ADPKD have an increased risk of morbidity, premature mortality, and other life-time complications including renal and hepatic cyst and urinary tract infection, intracranial aneurysm, diverticulosis, and kidney pain which impair quality of life. Despite some therapeutic advances and the growing number of clinical trials in ADPKD, the outcomes that are relevant to patients and clinicians, such as symptoms and quality of life, are infrequently and inconsistently reported. This potentially limits the contribution of trials to inform evidence-based decision-making. The Standardised Outcomes in Nephrology—Polycystic Kidney Disease (SONG-PKD) project aims to establish a consensus-based set of core outcomes for trials in PKD (with an initial focus on ADPKD but inclusive of all stages) that patients and health professionals identify as critically important.

Methods

The five phases of SONG-PKD are: a systematic review to identify outcomes that have been reported in existing PKD trials; focus groups with nominal group technique with patients and caregivers to identify, rank, and describe reasons for their choices; qualitative stakeholder interviews with health professionals to elicit individual values and perspectives on outcomes for trials involving patients with PKD; an international three-round Delphi survey with all stakeholder groups (including patients, caregivers, healthcare providers, policy makers, researchers, and industry) to gain consensus on critically important core outcome domains; and a consensus workshop to review and establish a set of core outcome domains and measures for trials in PKD.

Discussion

The SONG-PKD core outcome set is aimed at improving the consistency and completeness of outcome reporting across ADPKD trials, leading to improvements in the reliability and relevance of trial-based evidence to inform decisions about treatment and ultimately improve the care and outcomes for people with ADPKD.

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Standardising outcomes for clinical trials and systematic reviews

Mike Clarke (2007)

Introduction Fifteen years ago, what was to become OMERACT met for the first time in The Netherlands to discuss ways in which the multitude of outcomes in assessments of the effects of treatments for rheumatoid arthritis might be standardised. In Trials, Tugwell et al have described the need for, and success of, this initiative [1] and Cooney and colleagues have set out their plans for a corresponding initiative for ulcerative colitis [2]. Why do we need such initiatives? What's the problem? And are these and other initiatives the solution? What's the problem? Every year, millions of journal articles are added to the tens of millions that already exist in the health literature, and tens of millions of web pages are added to the hundreds of millions currently available. Within these, there are many tens of thousands of research studies which might provide the evidence needed to make well-informed decisions about health care. The task of working through all this material is overwhelming enough, without then finding that the studies of relevance to the decision you wish to make all describe their findings in different ways, making it difficult if not impossible to draw out the relevant information. Of course, you might be able to find a systematic review, but even then there is no guarantee that the authors of that review will not have been faced with an insurmountable task of bringing together and making sense of a variety of studies that used a variety of outcomes and outcome measures. These difficulties are great enough but the problem gets even worse when one considers the potential for bias. If researchers have measured a particular outcome in a variety of ways, (for example using different pain instruments filled in by different people at different times) they might not report all of their findings from all of these measures. Studies have highlighted this problem in clinical trials, showing that this selectivity in reporting is usually driven by a desire to present the most positive or statistically significant results [3]. This will mean that, where the original researcher had a choice, the reader of the clinical trial report might be presented with an overly optimistic estimate of the effect of an intervention and therefore be led towards the wrong decision. In the 1990s, the potential scale of the problem of multiple outcome measures was highlighted in mental health by a comprehensive descriptive account of randomised trials in the treatment of people with schizophrenia. Thornley and Adams identified a total of 2000 such trials, which had assessed more than 600 different interventions. However, these trials had included an even greater number of rating scales for mental health than the number of interventions: 640 [4]. The potential for biased reported and the challenges of comparing the findings of different trials of different interventions using different ways of measuring illness make the identification of effective, ineffective and unproven treatments for this condition especially difficult [5]. This is true whether the readers of the report of a clinical trial are trying to use it to inform their decisions, or whether they are trying to combine similar trials within a systematic review. Thornley and Adams, who had done the descriptive study of the large number of rating scales in mental health trials, were faced with this very problem in a review of chlorpromazine. They concluded that review with the following implications for research, "if rating scales are to be employed, a concerted effort should be made to agree on which measures are the most useful. Studies within this review reported on so many scales that, even if results had not been poorly reported, they would have been difficult to synthesise in a clinically meaningful way." [6]. What's the solution? If we want to choose the shortest of three routes between two towns, how would we cope if told that one is 10 kilometres and another is 8 miles? Doing that conversion between miles and kilometres might not be too much of a problem, but what if the third route was said to be 32 furlongs? Now, imagine that the measurements had all been taken in different ways. One came from walking the route with a measuring wheel, one from an estimate based on the time taken to ride a horse between the two towns and one from using a ruler on a map. To make a well informed choice we would want the distances to be available to us in the same units, measured in the same ways. Making decisions about health care should be no different. We want to compare and contrast research findings on the basis of the same outcomes, measured in the same ways. Achieving this is not straightforward, but it is not impossible. Key steps are to decide on the core outcome measures and, in some cases, the core baseline variables, and for these to then be included in the conduct and reporting of research studies. One of the earliest examples is an initiative by the World Health Organisation in the late 1970s, relating to cancer trials. Meetings on the Standardization of Reporting Results of Cancer Treatment took place in Turin (1977) and in Brussels two years later. More than 30 representatives from cooperative groups doing randomised trials in cancer came together and their discussions led to a WHO Handbook of guidelines on the minimal requirements for data collection in cancer trials [7,8]. OMERACT has also grown by trying to reach a consensus among major stakeholders in the field of rheumatology [1] and the IMMPACT recommendations for chronic pain trials have arisen in a similar way [9]. Other approaches have included the use of literature surveys to identify the variety of outcome measures that have been used and reported, followed by group discussion. This is the case with low back pain [10], colon cancer [11] and an e-Delhi survey in maternity care [12]. Having developed these lists of outcomes measures, researchers need to use them and systematic reviewers need to build their reviews around them. These sets of standardised outcomes measures are not meant to stifle the development and use of other outcomes. Rather, they provide a core set of outcome measures, which researchers should use routinely. Researchers wishing to add other outcome measures in the context of their own trial would continue to do so but, when reporting their trial, selective reporting should be avoided through the presentation of the findings for both the core set and all additional outcome measures they collected. Furthermore, the use of the outcome measures in these core sets should not be restricted to research studies. They are also relevant within routine practice. If they are collected within such practice, they would help the provider and the receiver of health care to assess their progress and facilitate their understanding of the relevance to them of the findings of research. Journals such as Trials can help by highlighting initiatives such as those discussed in rheumatology [1] and ulcerative colitis [2]. They should encourage researchers to report their findings for the outcome measures in the core sets, and provide them with the space to do so. This will allow readers and systematic reviewers to make best use of the reported trials. Conclusion When there are differences among the results of similar clinical trials, the fundamental issues of interest to people making decisions about health care are likely to concern the interventions that were tested, the types of patient in the study, or both; not the different outcome measure used. The latter is important but if one remembers that the studies were probably not done to assess differences between the various ways of measuring outcomes, but, rather, differences between the interventions, the benefits of consistency become obvious. Achieving consistency is not something that can be left to serendipity. It will require consensus, guidelines and adherence. The papers in Trials and others mentioned in this commentary show how this might happen. Competing interests I am the author of one of the papers on a core set of outcomes for healthcare research, which is cited in this paper.

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Sirolimus and kidney growth in autosomal dominant polycystic kidney disease.

Andreas L. Serra, Diane Poster, Andreas D. Kistler … (2010)

In autosomal dominant polycystic kidney disease (ADPKD), aberrant activation of the mammalian target of rapamycin (mTOR) pathway is associated with progressive kidney enlargement. The drug sirolimus suppresses mTOR signaling. In this 18-month, open-label, randomized, controlled trial, we sought to determine whether sirolimus halts the growth in kidney volume among patients with ADPKD. We randomly assigned 100 patients between the ages of 18 and 40 years to receive either sirolimus (target dose, 2 mg daily) or standard care. All patients had an estimated creatinine clearance of at least 70 ml per minute. Serial magnetic resonance imaging was performed to measure the volume of polycystic kidneys. The primary outcome was total kidney volume at 18 months on blinded assessment. Secondary outcomes were the glomerular filtration rate and urinary albumin excretion rate at 18 months. At randomization, the median total kidney volume was 907 cm3 (interquartile range, 577 to 1330) in the sirolimus group and 1003 cm3 (interquartile range, 574 to 1422) in the control group. The median increase over the 18-month period was 99 cm3 (interquartile range, 43 to 173) in the sirolimus group and 97 cm3 (interquartile range, 37 to 181) in the control group. At 18 months, the median total kidney volume in the sirolimus group was 102% of that in the control group (95% confidence interval, 99 to 105; P=0.26). The glomerular filtration rate did not differ significantly between the two groups; however, the urinary albumin excretion rate was higher in the sirolimus group. In adults with ADPKD and early chronic kidney disease, 18 months of treatment with sirolimus did not halt polycystic kidney growth. (Funded by Wyeth and others; ClinicalTrials.gov number, NCT00346918.)

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Autosomal dominant polycystic kidney disease: the last 3 years.

Vicente Torres, Peter Harris (2009)

Autosomal dominant polycystic kidney disease is the most prevalent, potentially lethal monogenic disorder. It has large inter- and intra-familial variability explained to a large extent by its genetic heterogeneity and modifier genes. An increased understanding of its underlying genetic, molecular, and cellular mechanisms and a better appreciation of its progression and systemic manifestations have laid out the foundation for the development of clinical trials and potentially effective therapies. The purpose of this review is to update the core of knowledge in this area with recent publications that have appeared during 2006-2009.

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Author and article information

Contributors

Yeoungjee Cho:

ORCID: http://orcid.org/0000-0002-3502-9837

+61 7 3176 5080 , Yeoungjee.cho@health.qld.gov.au

Benedicte Sautenet: benedicute.sautenet@univ-tours.fr

Gopala Rangan: g.rangan@sydney.edu.au

Jonathan C. Craig: Jonathan.craig@sydney.edu.au

Albert C. M. Ong: a.ong@sheffield.ac.uk

Arlene Chapman: achapman1@medicine.bsd.uchicago.edu

Curie Ahn: curie@snu.ac.uk

Dongping Chen: 13764362569@163.com

Helen Coolican: helen@pkdaustralia.org

Juliana Tze-Wah Kao: jtwkao2@gmail.com

Ron Gansevoort: r.t.gansevoort@umcg.nl

Ronald Perrone: rperrone@tuftsmedicalcenter.org

Tess Harris: tess.harris@pkdinternational.org

Vicente Torres: torres.vicente@mayo.edu

York Pei: York.pei@uhn.ca

Peter G. Kerr: peter.kerr@monash.edu

Jessica Ryan: Jessica.ryan@monash.edu

Talia Gutman: Talia.gutman@sydney.edu.au

Martin Howell: Martin.howell@sydney.edu.au

Angela Ju: Angela.ju@sydney.edu.au

Karine E. Manera: Karine.manera@sydney.edu.au

Armando Teixeira-Pinto: Armando.teixeira-pinto@sydney.edu.au

Lorraine A. Hamiwka: Lorraine.hamiwka@ahs.ca

Allison Tong: Allison.tong@sydney.edu.au

Journal

Journal ID (nlm-ta): Trials

Journal ID (iso-abbrev): Trials

Title: Trials

Publisher: BioMed Central (London )

ISSN (Electronic): 1745-6215

Publication date (Electronic): 23 November 2017

Publication date PMC-release: 23 November 2017

Publication date Collection: 2017

Volume: 18

Electronic Location Identifier: 560

Affiliations

[1 ]ISNI 0000 0004 0380 2017, GRID grid.412744.0, Department of Nephrology, , Princess Alexandra Hospital, ; 199 Ipswich Road, Woolloongabba, Brisbane, QLD 4102 Australia

[2 ]ISNI 0000 0000 9320 7537, GRID grid.1003.2, Australasian Kidney Trials Network, , University of Queensland, ; Brisbane, Australia

[3 ]Translational Research Institute, Brisbane, Australia

[4 ]ISNI 0000 0001 2182 6141, GRID grid.12366.30, Department of Nephrology and Clinical Immunology, , Tours Hospital, University Francois Rabelais, INSERMU1246, ; Tours, France

[5 ]ISNI 0000 0004 1936 834X, GRID grid.1013.3, Centre for Transplant and Renal Research, Westmead Institute for Medical Research, , The University of Sydney, ; Sydney, Australia

[6 ]ISNI 0000 0004 1936 834X, GRID grid.1013.3, Sydney School of Public Health, , The University of Sydney, ; Sydney, Australia

[7 ]ISNI 0000 0000 9690 854X, GRID grid.413973.b, Centre for Kidney Research, , The Children’s Hospital at Westmead, ; Sydney, Australia

[8 ]ISNI 0000 0004 1936 9262, GRID grid.11835.3e, Academic Nephrology Unit, Department of Infection Immunity & Cardiovascular Disease, , University of Sheffield, ; Sheffield, UK

[9 ]ISNI 0000 0004 1936 7822, GRID grid.170205.1, Department of Medicine, , The University of Chicago, ; Chicago, USA

[10 ]ISNI 0000 0001 0302 820X, GRID grid.412484.f, Division of Nephrology, , Seoul National University Hospital, ; Seoul, South Korea

[11 ]Kidney Institute, Department of Nephrology, Shanghai Changzheng Hospital, Second Military Medical University, Shanghai, China

[12 ]Polycystic Kidney Disease Foundation of Australia, Sydney, Australia

[13 ]ISNI 0000 0004 1937 1063, GRID grid.256105.5, School of Medicine, , Fu Jen Catholic University, ; Taipei, Taiwan

[14 ]ISNI 0000 0004 0572 7815, GRID grid.412094.a, Department of Internal Medicine, , National Taiwan University Hospital, ; Taipei, Taiwan

[15 ]Faculty of Medical Sciences, University Medical Center Gronigen, Gronigen, The Netherlands

[16 ]ISNI 0000 0000 8934 4045, GRID grid.67033.31, Division of Nephrology, , Tufts University School of Medicine, ; Boston, USA

[17 ]Polycystic Kidney Disease International, London, UK

[18 ]ISNI 0000 0004 0459 167X, GRID grid.66875.3a, Department of Nephrology and Hypertension, , Mayo Clinic, ; Rochester, USA

[19 ]ISNI 0000 0001 2157 2938, GRID grid.17063.33, Division of Nephrology and Division of Genomic Medicine, , University of Toronto, ; Toronto, Canada

[20 ]ISNI 0000 0004 0390 1496, GRID grid.416060.5, Department of Nephrology, , Monash Medical Centre and Monash University, ; Melbourne, Australia

[21 ]ISNI 0000 0004 1936 7697, GRID grid.22072.35, Division of Nephrology, , Albert Children’s Hospital, University of Calgary, ; Calgary, Canada

Author information

Yeoungjee Cho http://orcid.org/0000-0002-3502-9837

Article

Publisher ID: 2298

DOI: 10.1186/s13063-017-2298-4

PMC ID: 5701447

PubMed ID: 29169385

SO-VID: 4bd92b57-e20f-44fc-9138-b44d25c28654

License:

Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

History

Date received : 27 July 2017

Date accepted : 27 October 2017

Funding

Funded by: FundRef http://dx.doi.org/10.13039/501100000925, National Health and Medical Research Council;

Award ID: 1092597

Award ID: 1092957

Award Recipient : Armando Teixeira-Pinto

Funded by: Polycystic kidney disease foundation of Australia

Custom metadata

ScienceOpen disciplines: Medicine

Keywords: core outcome set,outcomes research,patient-centred outcomes clinical trials,chronic kidney disease,autosomal dominant polycystic kidney disease

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ScienceOpen disciplines: Medicine

Keywords: core outcome set, outcomes research, patient-centred outcomes clinical trials, chronic kidney disease, autosomal dominant polycystic kidney disease

Standardised Outcomes in Nephrology—Polycystic Kidney Disease (SONG-PKD): study protocol for establishing a core outcome set in polycystic kidney disease

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Abstract

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Alternative approaches to Alzheimer's Disease

Most cited references 42

Standardising outcomes for clinical trials and systematic reviews

Sirolimus and kidney growth in autosomal dominant polycystic kidney disease.

Autosomal dominant polycystic kidney disease: the last 3 years.

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