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

Celeste McCracken; Zahra Raisi-Estabragh; Liliana Szabo; Michele Veldsman; Betty Raman; Anya Topiwala; Adriana Roca-Fernández; Masud Husain; Steffen E Petersen; Stefan Neubauer; Thomas E Nichols

doi:10.1136/bmjebm-2023-112518

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

BMJ Evid Based Med. 2024 May 6:bmjebm-2023-112518. doi: 10.1136/bmjebm-2023-112518. Online ahead of print.

Authors

Celeste McCracken¹, Zahra Raisi-Estabragh^{2

3}, Liliana Szabo^{2

3

4}, Michele Veldsman⁵, Betty Raman⁶, Anya Topiwala⁷, Adriana Roca-Fernández⁸, Masud Husain^{5

9

10}, Steffen E Petersen^{2

3

11

12}, Stefan Neubauer⁶, Thomas E Nichols^{7

9}

Affiliations

¹ Division of Cardiovascular Medicine, Radcliffe Department of Medicine, University of Oxford, and Oxford University Hospitals NHS Foundation Trust, Oxford, UK celeste.mccracken@cardiov.ox.ac.uk.
² William Harvey Research Institute, Queen Mary University of London, London, UK.
³ Barts Heart Centre, St Bartholomew's Hospital, Barts Health NHS Trust, London, UK.
⁴ Heart and Vascular Center, Semmelweis University, Budapest, Hungary.
⁵ Department of Experimental Psychology, University of Oxford, Oxford, UK.
⁶ Division of Cardiovascular Medicine, Radcliffe Department of Medicine, University of Oxford, and Oxford University Hospitals NHS Foundation Trust, Oxford, UK.
⁷ Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, Nuffield Department of Population Health, University of Oxford, Oxford, UK.
⁸ Perspectum Ltd, Oxford, UK.
⁹ Wellcome Centre for Integrative Neuroimaging (WIN FMRIB), University of Oxford, Oxford, UK.
¹⁰ Nuffield Department of Clinical Neuroscience, University of Oxford, Oxford, UK.
¹¹ Health Data Research UK, London, UK.
¹² Alan Turing Institute, London, UK.

PMID: 38719437
DOI: 10.1136/bmjebm-2023-112518

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.

Keywords: General Practice; PRIMARY CARE; Primary Healthcare.