Advances in computational modelling for personalised medicine after myocardial infarction

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.

Effect of myofibre architecture on ventricular pump function by using a neonatal porcine heart model: from DT-MRI to rule-based methods

“…The remaining challenges are to deal with the complex geometry, myofibre structure and material characterization of the myocardium [6,7]. Recent reviews on heart modelling can be found in [3,8,9].…”

“…(39.37% (LV), 45.89% (RV)), and still lower in exRBM2 (47.86% (LV), 51.72% (RV)). Only exRBM3 with DT-MRI-derived dispersion parameters can achieve Myofibre stress and strain distributions at end-systole for cases LDDMM, RBM17 and RBM uni . The solid lines in (d-f ) are the long-axis which links the LV basal centre and the LV apex, and the longitudinal axis is represented by the dash line passing the LV basal centre and perpendicular to the basal plane.…”

Strategies for calibrating models of biology

“…We would demand greater fidelity of a model directing clinical practice than one employed in notional, preliminary exploration [81]. The cost of capturing finer biological nuance is model complexity, which poses conceptual and calibration challenges; consider another adage, "everything should be made as simple as possible, but not simpler."…”

The multiscale simulation of apical myocardial infarction and shape variation of the left ventricle of the heart

“…Multiscale models could be used in clinics for more detailed diagnostics and the choice of adequate treatment of heart diseases. In particular, myocardial infarction (MI) is a heart disease of interest for computer simulation [2]. Earlier we had developed a model of the cardiovascular system with a focus on the left ventricle (LV) of the heart [3].…”