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Coronary artery disease
Original research
Comorbidity phenotypes and risk of mortality in patients with ischaemic heart disease in the UK
  1. Francesca Crowe,
  2. Dawit T Zemedikun,
  3. Kelvin Okoth,
  4. Nicola Jaime Adderley,
  5. Gavin Rudge,
  6. Mark Sheldon,
  7. Krishnarajah Nirantharakumar,
  8. Tom Marshall
  1. Institute of Applied Health Research, University of Birmingham, Birmingham, UK
  1. Correspondence to Dr Francesca Crowe, Institute of Applied Health Research, University of Birmingham, Birmingham B15 2TT, UK; F.Crowe{at}bham.ac.uk

Abstract

Objectives The objective of this study is to use latent class analysis of up to 20 comorbidities in patients with a diagnosis of ischaemic heart disease (IHD) to identify clusters of comorbidities and to examine the associations between these clusters and mortality.

Methods Longitudinal analysis of electronic health records in the health improvement network (THIN), a UK primary care database including 92 186 men and women aged ≥18 years with IHD and a median of 2 (IQR 1–3) comorbidities.

Results Latent class analysis revealed five clusters with half categorised as a low-burden comorbidity group. After a median follow-up of 3.2 (IQR 1.4–5.8) years, 17 645 patients died. Compared with the low-burden comorbidity group, two groups of patients with a high-burden of comorbidities had the highest adjusted HR for mortality: those with vascular and musculoskeletal conditions, HR 2.38 (95% CI 2.28 to 2.49) and those with respiratory and musculoskeletal conditions, HR 2.62 (95% CI 2.45 to 2.79). Hazards of mortality in two other groups of patients characterised by cardiometabolic and mental health comorbidities were also higher than the low-burden comorbidity group; HR 1.46 (95% CI 1.39 to 1.52) and 1.55 (95% CI 1.46 to 1.64), respectively.

Conclusions This analysis has identified five distinct comorbidity clusters in patients with IHD that were differentially associated with risk of mortality. These analyses should be replicated in other large datasets, and this may help shape the development of future interventions or health services that take into account the impact of these comorbidity clusters.

  • comorbidity
  • ischaemic heart disease
  • latent class analysis
  • mortality
  • the health improvement network
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/heartjnl-2019-316091

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    Footnotes

    • KN and TM are joint senior authors.

    • Twitter @Dawit_TZ, @TomPMarshall

    • Contributors FC, TM and KN had the original idea for the study. FC, TM and KN designed the study. FC and DTZ designed and performed the analysis. FC wrote the first draft of the paper, which was revised in collaboration with NJA, TM, KN, KO, GR, MS and DTZ.

    • Funding Funding support was provided by a contract from the British Heart Foundation https://www.bhf.org.uk.

    • Disclaimer This paper presents independent research and the views expressed are those of the author(s) and not necessarily those of the British Heart Foundation (BHF).

    • Competing interests None declared.

    • 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.

    • Patient consent for publication Not required.

    • Ethics approval Collection of data for THIN was approved by the South-East Multicentre Research Ethics Committee in 2003; under the terms of this approval, studies must undergo independent scientific review. Scientific Review Committee approval for this analysis was obtained in June 2018 (SRC reference18THIN066).

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

    • Data availability statement All data relevant to the study are included in the article or uploaded as supplementary information. All relevant data are within the paper and its supporting information files.

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