Disease trajectories following myocardial infarction: insights from process mining of 145 million hospitalisation episodesResearch in context
Christopher J. Hayward,
Jonathan A. Batty,
David R. Westhead,
Owen Johnson,
Chris P. Gale,
Jianhua Wu,
Marlous Hall
Affiliations
Christopher J. Hayward
Clinical and Population Sciences Department, Leeds Institute of Cardiovascular and Metabolic Medicine, School of Medicine, Faculty of Medicine and Health, University of Leeds, Leeds, LS2 9JT, UK; Leeds Institute for Data Analytics, University of Leeds, Leeds, LS2 9JT, UK
Jonathan A. Batty
Clinical and Population Sciences Department, Leeds Institute of Cardiovascular and Metabolic Medicine, School of Medicine, Faculty of Medicine and Health, University of Leeds, Leeds, LS2 9JT, UK; Leeds Institute for Data Analytics, University of Leeds, Leeds, LS2 9JT, UK
David R. Westhead
Leeds Institute for Data Analytics, University of Leeds, Leeds, LS2 9JT, UK; School of Molecular and Cellular Biology, Faculty of Biological Sciences, University of Leeds, Leeds, LS2 9JT, UK
Owen Johnson
School of Computing, Faculty of Engineering and Physical Sciences, University of Leeds, LS2 9JT, UK
Chris P. Gale
Clinical and Population Sciences Department, Leeds Institute of Cardiovascular and Metabolic Medicine, School of Medicine, Faculty of Medicine and Health, University of Leeds, Leeds, LS2 9JT, UK; Leeds Institute for Data Analytics, University of Leeds, Leeds, LS2 9JT, UK; Department of Cardiology, Leeds Teaching Hospitals NHS Trust, Great George Street, Leeds, LS1 3EX, UK
Jianhua Wu
Leeds Institute for Data Analytics, University of Leeds, Leeds, LS2 9JT, UK; Wolfson Institute of Population Health, Queen Mary University of London, London, E1 4NS, UK
Marlous Hall
Clinical and Population Sciences Department, Leeds Institute of Cardiovascular and Metabolic Medicine, School of Medicine, Faculty of Medicine and Health, University of Leeds, Leeds, LS2 9JT, UK; Leeds Institute for Data Analytics, University of Leeds, Leeds, LS2 9JT, UK; Corresponding author. Leeds Institute of Cardiovascular and Metabolic Medicine, and Leeds Institute for Data Analytics, University of Leeds, Leeds, LS2 9JT, UK.
Summary: 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.
