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  • Feasibility of multiorgan risk prediction with routinely collected diagnostics: a prospective cohort study in the UK Biobank

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Original research
Feasibility of multiorgan risk prediction with routinely collected diagnostics: a prospective cohort study in the UK Biobank
  1. Celeste McCracken1,
  2. Zahra Raisi-Estabragh2,3,
  3. Liliana Szabo2,3,4,
  4. Michele Veldsman5,
  5. Betty Raman1,
  6. Anya Topiwala6,
  7. Adriana Roca-Fernández7,
  8. Masud Husain5,8,9,
  9. Steffen E Petersen2,3,10,11,
  10. Stefan Neubauer1,
  11. Thomas E Nichols6,8
  1. 1 Division of Cardiovascular Medicine, Radcliffe Department of Medicine, University of Oxford, and Oxford University Hospitals NHS Foundation Trust, Oxford, UK
  2. 2 William Harvey Research Institute, Queen Mary University of London, London, UK
  3. 3 Barts Heart Centre, St Bartholomew’s Hospital, Barts Health NHS Trust, London, UK
  4. 4 Heart and Vascular Center, Semmelweis University, Budapest, Hungary
  5. 5 Department of Experimental Psychology, University of Oxford, Oxford, UK
  6. 6 Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, Nuffield Department of Population Health, University of Oxford, Oxford, UK
  7. 7 Perspectum Ltd, Oxford, UK
  8. 8 Wellcome Centre for Integrative Neuroimaging (WIN FMRIB), University of Oxford, Oxford, UK
  9. 9 Nuffield Department of Clinical Neuroscience, University of Oxford, Oxford, UK
  10. 10 Health Data Research UK, London, UK
  11. 11 Alan Turing Institute, London, UK
  1. Correspondence to Celeste McCracken; celeste.mccracken{at}cardiov.ox.ac.uk

Abstract

Objectives Despite rising rates of multimorbidity, existing risk assessment tools are mostly limited to a single outcome of interest. This study tests the feasibility of producing multiple disease risk estimates with at least 70% discrimination (area under the receiver operating curve, AUROC) within the time and information constraints of the existing primary care health check framework.

Design Observational prospective cohort study

Setting UK Biobank.

Participants 228 240 adults from the UK population.

Interventions None.

Main outcome measures Myocardial infarction, atrial fibrillation, heart failure, stroke, all-cause dementia, chronic kidney disease, fatty liver disease, alcoholic liver disease, liver cirrhosis and liver failure.

Results Using a set of predictors easily gathered at the standard primary care health check (such as the National Health Service Health Check), we demonstrate that it is feasible to simultaneously produce risk estimates for multiple disease outcomes with AUROC of 70% or greater. These predictors can be entered once into a single form and produce risk scores for stroke (AUROC 0.727, 95% CI 0.713 to 0.740), all-cause dementia (0.823, 95% CI 0.810 to 0.836), myocardial infarction (0.785, 95% CI 0.775 to 0.795), atrial fibrillation (0.777, 95% CI 0.768 to 0.785), heart failure (0.828, 95% CI 0.818 to 0.838), chronic kidney disease (0.774, 95% CI 0.765 to 0.783), fatty liver disease (0.766, 95% CI 0.753 to 0.779), alcoholic liver disease (0.864, 95% CI 0.835 to 0.894), liver cirrhosis (0.763, 95% CI 0.734 to 0.793) and liver failure (0.746, 95% CI 0.695 to 0.796).

Conclusions Easily collected diagnostics can be used to assess 10-year risk across multiple disease outcomes, without the need for specialist computing or invasive biomarkers. Such an approach could increase the utility of existing data and place multiorgan risk information at the fingertips of primary care providers, thus creating opportunities for longer-term multimorbidity prevention. Additional work is needed to validate whether these findings would hold in a larger, more representative cohort outside the UK Biobank.

  • PRIMARY CARE
  • General Practice
  • Primary Healthcare

Data availability statement

Data may be obtained from a third party and are not publicly available. This analysis was produced under UK Biobank Access Application 59867. The data in this study are owned by the UK Biobank (www. ukbiobank.ac.uk) and legal constraints do not permit public sharing of the data. The UK Biobank, however, is open to all bona fide researchers anywhere in the world. Thus, the data used in this communication can be easily and directly accessed by applying through the UK Biobank Access Management System (www.ukbiobank.ac.uk/ register-apply). Results from this study will be returned to UK Biobank according to their published returns policy.

https://creativecommons.org/licenses/by/4.0/

This is an open access article distributed in accordance with the Creative Commons Attribution 4.0 Unported (CC BY 4.0) license, which permits others to copy, redistribute, remix, transform and build upon this work for any purpose, provided the original work is properly cited, a link to the licence is given, and indication of whether changes were made. See: https://creativecommons.org/licenses/by/4.0/.

https://doi.org/10.1136/bmjebm-2023-112518

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    Data availability statement

    Data may be obtained from a third party and are not publicly available. This analysis was produced under UK Biobank Access Application 59867. The data in this study are owned by the UK Biobank (www. ukbiobank.ac.uk) and legal constraints do not permit public sharing of the data. The UK Biobank, however, is open to all bona fide researchers anywhere in the world. Thus, the data used in this communication can be easily and directly accessed by applying through the UK Biobank Access Management System (www.ukbiobank.ac.uk/ register-apply). Results from this study will be returned to UK Biobank according to their published returns policy.

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    Footnotes

    • Contributors The study was conceived by TEN, SN, SEP and CM. CM and TEN formulated the statistical analysis plan. CM led and performed the analysis and led in writing of the manuscript. ZR-E and LS contributed to drafting of the manuscript. All coauthors including MV, BR, AT, AR-F and MH provided critical review of the work. All authors read and approved the final manuscript. TEN and SN provided overall supervision for the work. TEN and SN are the guarantors of the work. The corresponding author attests that all listed authors meet authorship criteria and that no others meeting the criteria have been omitted.

    • Funding CM and SN are supported by the Oxford National Institute for Health and Care Research (NIHR) Biomedical Research Centre (IS-BRC-1215-20008) and the Oxford BHF Centre of Research Excellence. BR acknowledges support from the BHF Oxford CRE (RE/18/3/34214). LS has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 825903 (euCanSHare project). ZR-E recognises the NIHR Integrated Academic Training programme which supports her Academic Clinical Lectureship post and BHF Clinical Research Training Fellowship (FS/17/81/33318). This work acknowledges the support of the National Institute for Health and Care Research Barts Biomedical Research Centre (NIHR203330); a delivery partnership of Barts Health NHS Trust, Queen Mary University of London, St George’s University Hospitals NHS Foundation Trust and St George’s University of London. This work was supported by Health Data Research UK, an initiative funded by UK Research and Innovation, Department of Health and Social Care (England) and the devolved administrations Wellcome Trust, NIHR Oxford Biomedical Centre and leading medical research charities. AT is supported by a Wellcome Trust (https://wellcome.org/) fellowship (216462/Z/19/Z). MH is supported by the Wellcome Trust (206330/Z/17/Z) and NIHR Oxford Biomedical Research Centre (IS-BRC-1215-20008). TEN is supported by the Li Ka Shing Centre for Health Information and Discovery, an NIH grant (https://www.nih.gov/, TN: R01EB026859), the NIHR Oxford Biomedical Research Centre (BRC-1215-20014), and a Wellcome Trust award (TN: 100309/Z/12/Z). This work includes data provided by patients and collected by the NHS and NHS Digital as part of their care and support. The research was supported by the Wellcome Trust Core Award Grant Number 203141/Z/16/Z with funding from the NIHR Oxford BRC. This research used data assets made available by National Safe Haven as part of the Data and Connectivity National Core Study, led by Health Data Research UK in partnership with the Office for National Statistics and funded by UK Research and Innovation (MC_PC_20058).

    • Disclaimer The views expressed are those of the author(s) and not necessarily those of the NHS, the NIHR or the Department of Health.

    • Competing interests SEP provides consultancy to Cardiovascular Imaging, Calgary, Alberta, Canada. BR consulted for Axcella Therapeutics. AR-F is an employee and shareholder in Perspectum, Oxford, UK. SN is a founder, shareholder and former board member of Perspectum. TEN provides consultancy to Perspectum, Oxford, UK. All other authors declare no conflicts of interest. The corresponding author attests that all listed authors meet authorship criteria and that no others meeting the criteria have been omitted.

    • Patient and public involvement Patients and/or the public were not involved in the design, or conduct, or reporting, or dissemination plans of this research.

    • Provenance and peer review Not commissioned; externally peer reviewed.

    • Supplemental material This content has been supplied by the author(s). It has not been vetted by BMJ Publishing Group Limited (BMJ) and may not have been peer-reviewed. Any opinions or recommendations discussed are solely those of the author(s) and are not endorsed by BMJ. BMJ disclaims all liability and responsibility arising from any reliance placed on the content. Where the content includes any translated material, BMJ does not warrant the accuracy and reliability of the translations (including but not limited to local regulations, clinical guidelines, terminology, drug names and drug dosages), and is not responsible for any error and/or omissions arising from translation and adaptation or otherwise.