Hayward, C. orcid.org/0000-0001-5563-8296 (Cover date: October 2023) Disease trajectories following myocardial infarction: insights from process mining of 145 million hospitalisation episodes. EBioMedicine, 96. 104792. ISSN 2352-3964
Abstract
Background
Knowledge of post-myocardial infarction (MI) disease risk to date is limited—yet the number of survivors of MI has increased dramatically in recent decades. We investigated temporally ordered sequences of all conditions following MI in nationwide electronic health record data through the application of process mining.
Methods
We conducted a national retrospective cohort study of all hospitalisations (145,670,448 episodes; 34,083,204 individuals) admitted to NHS hospitals in England (1st January 2008–31st January 2017, final follow-up 27th March 2017). Through process mining, we identified trajectories of all major disease diagnoses following MI and compared their relative risk (RR) and all-cause mortality hazard ratios (HR) to a risk-set matched non-MI control cohort using Cox proportional hazards and flexible parametric survival models.
Findings
Among a total of 375,669 MI patients (130,758 females; 34.8%) and 1,878,345 matched non-MI patients (653,790 females; 34.8%), we identified 28,799 unique disease trajectories. The accrual of multiple circulatory diagnoses was more common amongst MI patients (RR 4.32, 95% CI 3.96–4.72) and conferred an increased risk of death (HR 1.32, 1.13–1.53) compared with matched controls. Trajectories featuring neuro-psychiatric diagnoses (including anxiety and depression) following circulatory disorders were markedly more common and had increased mortality post MI (HR ranging from 1.11 to 1.73) compared with non-MI individuals.
Interpretation
These results provide an opportunity for early intervention targets for survivors of MI—such as increased focus on the psychological and behavioural pathways—to mitigate ongoing adverse disease trajectories, multimorbidity, and premature mortality.
Funding
British Heart Foundation; Alan Turing Institute.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2023 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/) |
Keywords: | Disease trajectories; Multimorbidity; Myocardial infarction; Electronic health records; Process mining; Machine learning |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Medicine and Health (Leeds) > School of Medicine (Leeds) > Leeds Institute of Cardiovascular and Metabolic Medicine (LICAMM) > Clinical & Population Science Dept (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 09 Oct 2023 16:20 |
Last Modified: | 09 Oct 2023 16:35 |
Published Version: | https://www.thelancet.com/journals/ebiom/article/P... |
Status: | Published |
Publisher: | Elsevier |
Identification Number: | 10.1016/j.ebiom.2023.104792 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:202995 |