Economic, Clinical, and Societal Impact of Early Thromboembolic -Risk Detection in High-Risk Atrial Fibrillation: A Model -Based Evaluation of the MATHIAS Strategy.

Atrial fibrillation and its thromboembolic complications represent a growing clinical and socioeconomic challenge in Europe. AF is strongly associated with stroke, major adverse cardiovascular events (MACE), disability, and mortality, disproportionately affecting older adults. As the European population ages, the prevalence of AF and AF-related stroke is projected to increase substantially, leading to escalating healthcare expenditures and societal burden. Stroke care alone costs an average of €22,605.66 in the first year, largely driven by hospitalization and long-term dependency, with 45-50% of survivors experiencing residual disability. Preventing AF-related thromboembolic events therefore represents both a clinical and economic priority.

From a clinical standpoint, there is a critical unmet need for improved upstream thromboembolic risk stratification in individuals at high risk of AF. Although several AF prediction models can estimate the likelihood of incident AF over 5-10 years, and systematic screening of adults aged ≥65 years has demonstrated cost savings through stroke prevention, a validated tool to guide anticoagulation initiation in high-risk individuals without established AF is lacking. Current standard practice relies on the CHA₂DS₂-VASc score once AF is diagnosed; however, this score has recognized limitations. It does not incorporate several relevant risk modifiers such as chronic kidney disease, cancer, biomarkers, electrocardiographic abnormalities, or ethnicity, and it may inadequately discriminate risk in certain subgroups, including women and patients with multimorbidity. Consequently, clinical decision-making often extends beyond the score, reflecting the need for more comprehensive and precise tools.

Emerging evidence supports the concept of AF as a clinical continuum. A prothrombotic atrial substrate may precede overt arrhythmia, creating a "pre-AF" stage during which thromboembolic risk is already elevated. The 2023 ACC/AHA/ACCP/HRS guidelines formally recognize "at-risk" and "pre-AF" stages, highlighting an opportunity for earlier preventive intervention. However, practical tools to identify and stratify this population in routine primary care remain limited.

Artificial intelligence (AI) offers a promising strategy to address these gaps. By leveraging high-dimensional electronic health record (EHR) data, AI models can capture complex, non-linear interactions among classical and emerging risk factors, potentially providing more accurate individualized thromboembolic risk prediction than traditional scores. Early machine learning (ML) approaches have demonstrated improved discrimination for AF and cardiovascular events compared with conventional models.

The MATHIAS project (throMboembolic risk Associated To High atrIal fibrillation riSk) aims to develop and prospectively validate an AI-based model to estimate thromboembolic risk in adults aged ≥65 years at high risk of AF using real-world primary care EHR data. This model will be integrated into a digitally enabled care pathway incorporating targeted, risk-guided photoplethysmography screening and individualized anticoagulation decisions. The objective is to enhance early detection, refine anticoagulation strategies, and personalize rhythm control and comorbidity management.