Key result
Digital twin technology integrating AI and mechanistic models may improve personalized VT prediction and treatment.
Why the study?
Current ICD guidelines to prevent VT-related sudden cardiac death rely solely on symptoms and LVEF, highlighting the need for better prediction of VT occurrence and circuits to guide preventive ablation.
Can digital twin technology improve the prediction of ventricular tachycardia in patients with ischaemic cardiomyopathy compared to current guidelines?
Can digital twin technology improve the prediction of ventricular tachycardia in patients with ischaemic cardiomyopathy compared to current guidelines?
Digital twin technology integrating clinical, data-driven, and mechanistic models holds potential to improve ventricular tachycardia prediction and guide preventive ablation in ischaemic cardiomyopathy.
May support personalized VT prediction in ischaemic cardiomyopathy; leaves open prospective validation before clinical adoption.
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.
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Lepper et al. (2022) conducted a review in Ventricular tachycardia in ischaemic cardiomyopathy. Digital twin technology vs. Evidence-based medicine was evaluated. Digital twin technology integrating clinical data, artificial intelligence, and mechanistic models is proposed to improve personalized prediction and treatment of ventricular tachycardia.
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