A novel patient-level computational model of atrial fibrillation patterns and clinical outcomes for evaluating screening strategies

BackgroundThe dynamic, heterogenous 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 in different subpopulations are unknown. We developed a computational patient-level AF model able to simulate the dynamic occurrence of AF and AF-associated outcomes during the entire lifetime of a virtual patient cohort to elucidate the impact of AF screening in virtual randomized clinical trials (V-RCTs). MethodsThe Markov-like computer model has 7 clinical states (sinus rhythm, symptomatic/asymptomatic AF, each with/without previous stroke, and death). AF-related atrial remodeling was incorporated, which influenced the age-/sex-dependent transition probabilities between states. Model calibration/validation was performed by replicating a wide range of clinical studies. AF screening strategies and stroke rates in the presence of defined interventions were assessed in V-RCTs. ResultsThe model simulates the entire lifetime of virtual patients with minute-level resolution and provides perfect information on the occurrence of AF episodes and clinical outcomes (stroke, death). It replicates numerous age/sex-specific episode- and population-level AF metrics, including AF incidence/prevalence, progression rates, burden, episode duration, and stroke/mortality incidence. The benefits of intermittent AF screening in V-RCTs were frequency- and duration-dependent, with systematic thrice-daily single-ECG recordings providing the highest detection rates (64.6% of AF patients being diagnosed before their symptom-based clinical diagnosis). Screening groups had comparable 5-year stroke rates 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 in patients with delayed clinical AF diagnosis. ConclusionsWe 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. Screening protocols with more frequent and longer monitoring have higher AF-detection rates, but stroke reduction in screening-detected individuals is highly dependent on patients and healthcare-systems characteristics. V-RCTs suggest that frequent AF screening with effective anticoagulation can reduce stroke incidence in patients with high likelihood of delayed AF diagnosis.