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

Background: The dynamic, heterogeneous nature of atrial fibrillation (AF) episodes and poor symptom-rhythm correlation make early AF detection challenging. The optimal screening strategy for early AF detection and its role in stroke prevention are unknown. Methods: To analyze the impact of screening-mediated AF detection on stroke risk, a Markov-like computer model was created that captured seven clinical states. AF-related atrial remodeling was incorporated, which influenced the age-/sex-dependent transition probabilities between states. Model calibration/validation was performed by replicating clinical studies. The effect of screening strategies on early AF diagnosis and subsequent modulation of stroke rate by simulated oral anticoagulation were assessed. Findings: The model simulates the entire lifetime of virtual patients with 30-min resolution and provides precise information on the occurrence of AF episodes and clinical outcomes. It replicates numerous age/sex-specific episode- and population-level AF metrics and clinical outcomes. The benefits of intermittent AF screening were frequency and duration dependent, with systematic thrice-daily single electrocardiogram providing the highest detection rates. Screening groups had comparable 5-year and lower 25-year stroke rates than the control group. These differences were increased by more effective anticoagulation therapy, in patients with higher baseline stroke risk, or with delayed clinical AF diagnosis. Conclusions: We present a novel computational patient-level AF model consistent with a large body of real-world data, enabling for the first time the systematic assessment of AF-management strategies. More frequent and longer screening has higher AF-detection rates, but stroke reduction is highly dependent on patients’ and healthcare-systems’ characteristics. Funding: Funding information is shown in the acknowledgments section.