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.