From evidence-based medicine to digital twin technology for predicting ventricular tachycardia in ischaemic cardiomyopathy

Abstract

Survivors of myocardial infarction are at risk of life-threatening ventricular tachycardias (VTs) later in their lives. Current guidelines for implantable cardioverter defibrillators (ICDs) implantation to prevent VT-related sudden cardiac death is solely based on symptoms and left ventricular ejection fraction. Catheter ablation of scar-related VTs is performed following ICD therapy, reducing VTs, painful shocks, anxiety, depression and worsening heart failure. We postulate that better prediction of the occurrence and circuit of VT, will improve identification of patients at risk for VT and boost preventive ablation, reducing mortality and morbidity. For this purpose, multiple time-evolving aspects of the underlying pathophysiology, including the anatomical substrate, triggers and modulators, should be part of VT prediction models. We envision digital twins as a solution combining clinical expertise with three prediction approaches: evidence-based medicine (clinical practice), data-driven models (data science) and mechanistic models (biomedical engineering). This paper aims to create a mutual understanding between experts in the different fields by providing a comprehensive description of the clinical problem and the three approaches in an understandable manner, leveraging future collaborations and technological innovations for clinical decision support. Moreover, it defines open challenges and gains for digital twin solutions and discusses the potential of hybrid modelling.

Using the Non-Adoption, Abandonment, Scale-Up, Spread, and Sustainability (NASSS) Framework to Identify Barriers and Facilitators for the Implementation of Digital Twins in Cardiovascular Medicine

“…With increases in cardiovascular diseases, clinical experts are looking to digital twins to help enhance their diagnostic and monitoring toolkit in order to help prevent and reduce the risk of people suffering cardiovascular diseases [ 1 , 11 , 14 , 39 , 47 , 48 , 49 ]. This rise in cardiovascular disease is facilitating the development of digital twins in cardiovascular medicine.…”

“…Indeed, the literature has revealed what types of cardiovascular diseases are of pressing concern to cardiologists, revealing a variety of specific cardiovascular disease-related clinical problems. In much of the literature on digital twins and cardiovascular disease, digital twins for detecting and preventing Ischaemic Heart Disease (IHD, also known as coronary heart disease) are proposed as one of the core models for what is one of the top ten causes of death in the Western world [ 1 , 11 , 14 , 49 ]. For example, Coorey et al [ 14 ] indicate a concentrated effort by cardiologists to foster the potential of digital twins to tackle IHD, highlighting how it surpasses all other types of cardiovascular disease “as a cause of premature mortality, with access to, and adoption of, proven treatments being context specific”.…”

“…With the adoption of the technology to simulate actual MRI and CT workflow, the digital twin optimised the turnaround time for producing images which improved patient waiting times ( ‘the identified improvement potential was a nearly half-hour reduction in patient waiting time, and significantly reduced staff overtime costs’ : [ 78 ]). Many publications shared this perspective, viewing personalised digital twins in positive value terms as supporting better (more personalised) care and making it easier and quicker for clinical experts to diagnose and treat their patients [ 1 , 6 , 11 , 31 , 34 , 35 , 36 , 42 , 58 , 60 ].…”

“…Digital twins—a ‘virtual’ replica of a complex system which is iteratively updated with real-time data—are increasingly being seen as a solution to the present cardiovascular disease crisis [ 1 ], a problem much exacerbated by COVID-19, austerity policies, and increasing health and social inequalities [ 2 ]. This enthusiasm is widespread in journals, articles, and media, frequently conveyed through the metaphor of promise and potential that encapsulates the sense that new insights will be generated and better medical decisions will be made, resulting in better outcomes for patients.…”

Simultaneous data assimilation and cardiac electrophysiology model correction using differentiable physics and deep learning

“…Computational cardiology is a multi-disciplinary field that has seen extensive progress in the past decade [1][2][3]. In particular, recent advances in numerical analysis and the development of virtual patient-specific models (known as 'digital twin') have allowed researchers to address critical challenges related to limitations of clinical methods routinely employed to diagnose arrhythmia, as well as to help plan the best therapy on an individual basis [4]. However, in order to build such accurate predictive heart models, one needs to select the most suitable theoretical framework, balancing the degree of mathematical complexity needed for the specific problem studied, the correct parameterization of model from measurements, and the validation of predictions.…”