In Silico Pace Mapping 1.0

  • Research type

    Research Study

  • Full title

    Development and Clinical Validation of an In Silico Pace Mapping Approach Utilising Implanted Device EGMs to Accurately Guide VT Ablation

  • IRAS ID

    312913

  • Contact name

    C Aldo Rinaldi

  • Contact email

    aldo.rinaldi@kcl.ac.uk

  • Sponsor organisation

    Guy's and St Thomas' Foundation NHS Trust

  • Duration of Study in the UK

    2 years, 5 months, 28 days

  • Research summary

    People who suffer from incessant cardiac arrhythmias receive a small electrical device implanted into their chest that automatically senses when the heart beats arrhythmically and applies electrical pulse to re-establish normal activity. However, if problems persist, people can have an operation called catheter ablation therapy, which involves ‘burning’ small areas of the heart tissue in order to permanently disrupt the problematic electrical pathways driving these arrhythmias.
    However, procedure times and complication rates are high, whist success rates are punitively low (~50% success), largely due to the significant challenge clinicians face in identifying the ideal ‘target’ to ablate within the patient’s heart. In this project, we aim to develop, and clinically validate, an in silico tool that reconstructs a personalised computational model of a patient’s heart using advanced MRI data, upon which a virtual ‘mapping’ procedure is then performed in order to identify (in the model) the optimal ablation target. This pre-procedural planning tool utilises stored information about the patient’s specific arrhythmia from their implanted device, ensuring optimal targets are selected. Our approach aims to reduce procedure times whilst increasing their safety, and ensure significantly increased long-term effectiveness of these invasive ablation procedures, increasing survival rates and quality-of-life.

    This application is concerned with the clinical arm of the study, specifically, in the collection of data from patients in order to (retrospectively) validate our computational model. The model itself will not be applied or used to treat these patients.

  • REC name

    London - Camden & Kings Cross Research Ethics Committee

  • REC reference

    22/LO/0907

  • Date of REC Opinion

    22 Dec 2022

  • REC opinion

    Favourable Opinion