Evidence map›Paper›PMID 42044954›Full record

ArticleBMJ open2026

Delphi approach to prioritising research in cardiovascular and kidney disease using routinely collected data.

Ross Alexander Forsyth, Amy Coombe, Nigel Brunskill, Timothy Chico, Neeraj Dhaun, Gavin Dreyer, James Fotheringham, Amy Hodgkinson, Jacqueline Ann Langdon MacArthur, Aisling Mcmahon and 7 more

Abstract read
In one paragraph

Article in BMJ open, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

17 authors.

Ross Alexander ForsythHDR UK, London, UK.
Amy CoombeBritish Heart Foundation Data Science Centre, Health Data Research UK, London, UK.
Nigel BrunskillNephrology, University Hospital of Leicester, Leicester, UK.
Timothy ChicoDepartment of Infection, Immunity and Cardiovascular Disease, The Medical School, The University of Sheffield, Sheffield, UK.
Neeraj DhaunUniversity of Edinburgh, Edinburgh, UK.
Gavin DreyerDepartment of Nephrology, Bart's Health, London, UK.
James FotheringhamSchool of Health and Related Research, University of Sheffield, Sheffield, UK.
Amy HodgkinsonBritish Heart Foundation Data Science Centre, Health Data Research UK, London, UK.
Jacqueline Ann Langdon MacArthurBritish Heart Foundation Data Science Centre, Edinburgh, UK.ORCID http://orcid.org/0000-0002-3550-9769
Aisling McmahonKidney Research UK, Peterborough, UK.
Eve Miller-HodgesBritish Heart Foundation Centre for Cardiovascular Science, University of Edinburgh, Edinburgh, UK.
Mike MoleteBritish Heart Foundation Data Science Centre, Health Data Research UK, London, UK.
Steffen E PetersenBritish Heart Foundation Data Science Centre, Health Data Research UK, London, UK.
Miranda ScanlonLay Advisory Group, Kidney Research UK, Peterborough, UK.
Anna StevensonBritish Heart Foundation Data Science Centre, Health Data Research UK, London, UK.
David C WheelerCentre for Nephrology, University College London, London, UK.ORCID http://orcid.org/0000-0003-0745-3478
Samira BellBritish Heart Foundation Data Science Centre, Health Data Research UK, London, UK s.t.bell@dundee.ac.uk.ORCID http://orcid.org/0000-0001-9100-1575

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectivesChronic kidney disease (CKD) and cardiovascular disease (CVD) are leading global causes of morbidity and mortality, often coexisting and sharing common risk factors. Despite their interconnection, clinical care and research for affected individuals remain siloed and fragmented. Recognising the need for integrated approaches, this study aimed to identify and prioritise key research questions at the intersection of CKD and CVD that can be addressed using real-world healthcare data to inform more cohesive and data-driven strategies for improving outcomes across both disease areas. DESIGN, SETTING AND

participantsA three-round modified Delphi process was conducted: Round 1 online survey collected open-ended research questions about CKD-CVD priorities via BHF Data Science Centre, Kidney Research UK, UK Renal Health Data Network and HDR UK public involvement channels; Round 2 in-person workshop refined and consolidated items; Round 3 online survey prioritised items across urgency, feasibility and impact using 5-point scales.

main outcome measuresSurvey mean scores for each research question were calculated across the three prioritisation domains, each scored out of 5. The top-ranked questions were identified based on overall scores.

resultsSix thematic domains emerged: risk prediction and early detection, treatment optimisation, health inequities, multimorbidity, disease mechanisms and data infrastructure. The highest-rated research priority was "What are the most effective strategies for prevention, early diagnosis and intervention in CKD?" with a mean score of 12.6 (SD 1.1). Other top priorities included evaluating the cost-effectiveness of early treatment, identifying predictors of kidney failure and assessing the benefits of treating cardiovascular and renal conditions independently.

conclusionsAcross domains, prevention/early detection and early treatment in CKD consistently ranked highest, indicating near-term opportunities for data-enabled cardio-renal research and service improvement; these priorities can inform funder calls, data linkage work and evaluation studies.

Indexed as

Biomedical ResearchCardiovascular DiseasesHealth PrioritiesRenal Insufficiency, ChronicDelphi TechniqueHumansSurveys and QuestionnairesUnited KingdomCardiovascular DiseaseDelphi TechniqueNephrology

Identifiers

PMID42044954
PMCPMC13141130

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.