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Advances in computational modelling for personalised medicine after myocardial infarction
  1. Kenneth Mangion1,2,
  2. Hao Gao3,
  3. Dirk Husmeier3,
  4. Xiaoyu Luo3,
  5. Colin Berry1,2
  1. 1BHF Glasgow Cardiovascular Research Centre, University of Glasgow, Glasgow, UK
  2. 2West of Scotland Heart and Lung Centre, Golden Jubilee National Hospital, Clydebank, UK
  3. 3Department of Mathematics and Statistics, University of Glasgow, Glasgow, UK
  1. Correspondence to Professor Colin Berry, Cardiovascular Research Centre, Institute of Cardiovascular and Medical Sciences, University of Glasgow, 126 University Place, Glasgow G12 8TA, Scotland, UK; colin.berry{at}glasgow.ac.uk

Abstract

Myocardial infarction (MI) is a leading cause of premature morbidity and mortality worldwide. Determining which patients will experience heart failure and sudden cardiac death after an acute MI is notoriously difficult for clinicians. The extent of heart damage after an acute MI is informed by cardiac imaging, typically using echocardiography or sometimes, cardiac magnetic resonance (CMR). These scans provide complex data sets that are only partially exploited by clinicians in daily practice, implying potential for improved risk assessment. Computational modelling of left ventricular (LV) function can bridge the gap towards personalised medicine using cardiac imaging in patients with post-MI. Several novel biomechanical parameters have theoretical prognostic value and may be useful to reflect the biomechanical effects of novel preventive therapy for adverse remodelling post-MI. These parameters include myocardial contractility (regional and global), stiffness and stress. Further, the parameters can be delineated spatially to correspond with infarct pathology and the remote zone. While these parameters hold promise, there are challenges for translating MI modelling into clinical practice, including model uncertainty, validation and verification, as well as time-efficient processing. More research is needed to (1) simplify imaging with CMR in patients with post-MI, while preserving diagnostic accuracy and patient tolerance (2) to assess and validate novel biomechanical parameters against established prognostic biomarkers, such as LV ejection fraction and infarct size. Accessible software packages with minimal user interaction are also needed. Translating benefits to patients will be achieved through a multidisciplinary approach including clinicians, mathematicians, statisticians and industry partners.

  • cardiac imaging and diagnostics
  • advanced cardiac imaging
  • cardiac magnetic resonance (CMR) imaging
  • acute myocardial infarction

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Footnotes

  • Contributors CB conceived the idea for the review. KM and HG drafted the manuscript. DH, XYL and CB were involved in revising this manuscript critically for important intellectual content. KM and HG were responsible for designing the figures. All authors (KM, HG, DH, XYL and CB) gave final approval of the version to be submitted and any revised version. CB is responsible for the overall content as guarantor.

  • Funding This work is supported by funding from the British Heart Foundation including two project grants (PG/11/2/28474 to CB; PG/14/64/31043 to CB, HG, XYL, DH), Clinical Research Training Fellowship (FS/15/54/31639 to KM), Engineering and Physical Sciences Research Council (EPSRC: EP/N014642/1 to HG, XYL, DH), and a Leverhulme Research Fellowship (RF-2015-510 to XYL).

  • Competing interests The University of Glasgow holds a research agreement with Siemens Healthcare UK Ltd.

  • Ethics approval UK Research Ethics Service.

  • Provenance and peer review Commissioned; externally peer reviewed.

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