Background The 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). Methods The 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. Results The 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. 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. 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.

U.S. received consultancy fees or honoraria from Università della Svizzera Italiana (USI, Switzerland), Roche Diagnostics (Switzerland), EP Solutions Inc. (Switzerland), Johnson & Johnson Medical Limited, (United Kingdom), Bayer Healthcare (Germany). U.S. is co-founder and shareholder of YourRhythmics BV, a spin-off company of the University Maastricht. The other authors have nothing to declare.

No prospective interventional clinical study or trial was performed as part of this manuscript, so registration of the study does not apply.

The current work is funded by the Cardiovascular Research Institute Maastricht (CARIM PhD call 2020 to J.H.), the European Union (MAESTRIA: Machine Learning Artificial Intelligence Early Detection Stroke Atrial Fibrillation, grant number 965286 to U.S.), the Netherlands Organization for Scientific Research (NWO/ZonMW Vidi 09150171910029 to J.H.) and the Dutch Heart Foundation (CVON2014-09, RACE V Reappraisal of Atrial Fibrillation: Interaction between hyperCoagulability, Electrical remodeling, and Vascular Destabilisation in the Progression of AF to H.C., M.R., and U.S. and Grant number 01-002-2022-0118, EmbRACE consortium to M.R., U.S. and J.H.).

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No new clinical data were obtained for this manuscript, so IRB approval does not apply.

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The code required to generate all data is made publicly available on an open repository (GitHub) at the time of publication. The link to the code is provided in the manuscript.