Characterisation of human AV-nodal properties using a network model.

Area of Science:

• Cardiovascular Physiology
• Computational Biology
• Medical Modeling

Background:

• Accurate characterization of the atrioventricular (AV) node is crucial for treating supraventricular tachycardias.
• Mathematical modeling offers a valuable approach for integrating and analyzing patient-specific measurements.

Purpose of the Study:

• To develop and evaluate a novel mathematical model of the human AV node.
• To capture both spatial and temporal dynamics of the AV node for clinical applications.

Main Methods:

• A series of interacting nodes with distinct refractory time and conduction delay dynamics were modeled.
• The model was validated using simulated and measured data from atrial fibrillation (AF) and clinical pacing scenarios.
• Parameter estimation uncertainty was quantified through sampling 1000 parameter sets.

Main Results:

• The human AV-node model successfully replicated signals from clinical ECG and invasive measurements during AF and pacing.
• Model outputs consistent with data were found to correspond to limited regions within the model's parameter space.
• The model demonstrates efficiency for interactive use on clinical timescales.

Conclusions:

• This is the first human AV-node model to integrate spatial and temporal dynamics.
• The model provides a valuable tool for understanding AV node function and advancing clinical research in supraventricular tachycardias.
• It enables parameter estimation and uncertainty quantification, filling a unique niche in existing models.