Using cardiac ionic cell models to interpret clinical data.

Area of Science:

• Cardiovascular Diseases
• Biomedical Engineering
• Computational Physiology

Background:

• Clinical measurement of cardiac electrophysiology has a long history, spanning noninvasive electrocardiography (ECG) to invasive tissue measurements.
• While crucial for diagnosis and monitoring, quantitatively linking clinical electrophysiology to cellular biophysical function remains a challenge.

Purpose of the Study:

• To review the application of multi-scale biophysical computational models in interpreting clinical electrophysiology signals.
• To highlight how these models bridge the gap between cellular function and organ-level electrical activity in the heart.

Main Methods:

• Reviewing recent advancements in computational modeling of cardiac electrophysiology.
• Simulating human cardiac myocyte electrophysiology in both atria and ventricles.
• Analyzing the linkage of organ-scale function to patient disease mechanisms and therapy responses.

Main Results:

• Multi-scale biophysical models are increasingly used to interpret clinical cardiac electrophysiology data.
• These models facilitate the connection between organ-scale function and patient-specific disease mechanisms.
• Applications include understanding conditions like atrial fibrillation, ventricular tachycardia, and responses to therapies such as defibrillators and cardiac resynchronization therapy.

Conclusions:

• Computational models offer a powerful framework for linking cellular electrophysiology to clinical observations.
• This integration aids cardiologists in understanding cardiac pathophysiology at a deeper level.
• The use of these models can lead to the identification of novel treatment strategies for cardiovascular diseases.