ArticleJournal of the Royal Society, Interface2022
From evidence-based medicine to digital twin technology for predicting ventricular tachycardia in ischaemic cardiomyopathy.
Article in Journal of the Royal Society, Interface, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed, 36 citations in OpenAlex.
- Randomized single-blinded study comparing sedation effectiveness and hemodynamic stability of remifentanil vs dexmedetomidine infusion for electrophysiology procedures in patients of National Heart Institute cathlab.Journal of interventional cardiac electrophysiology : an international journal of arrhythmias and pacing · 2024Trial
- A Real-Time Digital Twin for Human Cardiovascular Applications.International journal for numerical methods in biomedical engineering · 2026Article
- Current progress of digital twin construction using medical imaging.Journal of applied clinical medical physics · 2025Review
- Digital Cardiovascular Twins, AI Agents, and Sensor Data: A Narrative Review from System Architecture to Proactive Heart Health.Sensors (Basel, Switzerland) · 2025Review
- Conditional universal differential equations capture population dynamics and interindividual variation in c-peptide production.NPJ systems biology and applications · 2025Article
- Cardiovascular care with digital twin technology in the era of generative artificial intelligence.European heart journal · 2024Review
- Interpretable Clinical Decision-Making Application for Etiological Diagnosis of Ventricular Tachycardia Based on Machine Learning.Diagnostics (Basel, Switzerland) · 2024Article
- Bridging the gap between human beings and digital twins in radiology.European radiology · 2024Article
- Digital twins for cardiac electrophysiology: state of the art and future challenges.Herzschrittmachertherapie & Elektrophysiologie · 2024Review
- Ventricular Tachycardia Catheter Ablation: Retrospective Analysis and Prospective Outlooks-A Comprehensive Review.Biomedicines · 2024Review
- Simultaneous data assimilation and cardiac electrophysiology model correction using differentiable physics and deep learning.Interface focus · 2023Article
- 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.Sensors (Basel, Switzerland) · 2023Article
Corrections and comments
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Authors and funding
6 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.