Optimizing atrial fibrillation management using a novel patient-level computational model.

Area of Science:

• Computational modeling in cardiovascular disease
• Health informatics and predictive analytics
• Epidemiology of atrial fibrillation

Background:

• Early detection of atrial fibrillation (AF) is difficult due to episode variability and poor symptom correlation.
• The optimal screening strategy for AF and its impact on stroke prevention remain unclear.

Purpose of the Study:

• To analyze the impact of screening-mediated AF detection on stroke risk using a computational model.
• To assess the effectiveness of different screening strategies for early AF diagnosis and stroke risk modulation.

Main Methods:

• A Markov-like computer model simulating seven clinical states and AF-related atrial remodeling was developed.
• Model calibration and validation were performed by replicating clinical studies.
• The model assessed screening strategies' effects on AF diagnosis and simulated oral anticoagulation's impact on stroke rates.

Main Results:

• The model accurately simulates patient lifetime AF episodes and clinical outcomes.
• Intermittent AF screening benefits were frequency and duration-dependent; thrice-daily ECGs yielded the highest detection rates.
• Screening groups showed comparable 5-year and lower 25-year stroke rates, with benefits amplified by effective anticoagulation and higher baseline stroke risk.

Conclusions:

• A novel computational patient-level AF model was developed, consistent with real-world data.
• This model allows systematic assessment of AF management strategies.
• While frequent screening increases AF detection, stroke reduction is contingent on patient and healthcare system characteristics.