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Related Experiment Video

Updated: Jul 12, 2025

In Silico Clinical Trials for Cardiovascular Disease

In Silico Clinical Trials for Cardiovascular Disease

Published on: May 27, 2022

Computational Modelling Enabling In Silico Trials for Cardiac Physiologic Pacing.

Marina Strocchi^1,2, Nadeev Wijesuriya^3,4, Vishal Mehta^3,4

¹National Heart and Lung Institute, Imperial College London, 72 Du Cane Road, W12 0HS, London, UK. m.strocchi@imperial.ac.uk.

Journal of Cardiovascular Translational Research

|October 23, 2023

Summary

Conduction system pacing (CSP) offers physiological activation but needs more trials. In silico studies help optimize CSP delivery, battery life, and compare pacing methods for different patients.

Keywords:

Cardiac resynchronization therapy Conduction system pacing His bundle pacing In silico In silico trials Left bundle pacing Modelling

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Last Updated: Jul 12, 2025

In Silico Clinical Trials for Cardiovascular Disease

In Silico Clinical Trials for Cardiovascular Disease

Published on: May 27, 2022

Patient-specific Modeling of the Heart: Estimation of Ventricular Fiber Orientations

Patient-specific Modeling of the Heart: Estimation of Ventricular Fiber Orientations

Published on: January 8, 2013

Translational Rabbit Model of Chronic Cardiac Pacing

Translational Rabbit Model of Chronic Cardiac Pacing

Published on: January 6, 2023

Area of Science:

Cardiology
Biomedical Engineering
Computational Biology

Background:

Conduction system pacing (CSP) aims for physiological ventricular activation.
Large trials are needed to compare CSP safety and efficacy against biventricular pacing (BVP).
Optimal pacing thresholds and patient selection for CSP remain key questions.

Purpose of the Study:

To review in silico studies investigating conduction system pacing.
To explore how computational models advance understanding of CSP delivery and optimization.
To discuss the non-invasive comparison of pacing strategies using in silico methods.

Main Methods:

Review of published in silico studies on conduction system pacing.
Analysis of computational modeling approaches for CSP.
Examination of simulations comparing CSP and BVP in various patient models.

Main Results:

In silico studies have improved understanding of conduction system capture.
Computational models aid in optimizing CSP delivery and battery longevity.
In silico methods allow non-invasive assessment of different pacing strategies across patient groups.

Conclusions:

In silico studies are valuable tools for advancing conduction system pacing research.
Computational modeling can guide clinical trial design and patient selection for CSP.
Further in silico investigations are crucial for the widespread adoption of CSP.