throMboembolic Risk Associated To High atrIal Fibrillation riSk

Condition(s) studied

Investigational drug(s) / intervention(s)

AI_MATHIAS: MATHIAS-guided strategy (intervention): This approach was applied to the high-risk cohort (Q4) \[10,24\] to estimate individual thromboembolic risk. The process included a subsequent clinical evaluation and device-based photoplethysmography screening \[5,11\], followed by AI-driven thromboembolic risk stratification using the MATHIAS AI prototype \[35,36\] with initiation of oral anticoagulation according to the predicted risk profile, regardless of whether atrial fibrillation was confirmed or not.

Study summary

Cardiovascular diseases are the leading cause of mortality from treatable conditions in the European Union and the second from preventable causes, with a standardized mortality rate of 257.8 deaths per 100,000 inhabitants. In 2022, more than 1.11 million deaths in individuals under 75 years could have been avoided. Atrial fibrillation (AF) and major adverse cardiovascular events (MACE) are highly prevalent in the elderly and generate substantial healthcare costs. AF significantly increases the risk of MACE and is projected to rise markedly in the coming decades.

In Europe, AF prevalence is expected to increase 2.5-fold over the next 50 years, with a lifetime risk of 1 in 3-5 individuals after age 55. AF-related strokes are projected to increase by 34%, and ischemic strokes in individuals over 80 are expected to triple between 2016 and 2060. Additionally, a 27% increase is anticipated among stroke survivors who subsequently develop AF or related conditions. AF substantially impacts morbidity, mortality, and disease progression, and early detection and treatment are crucial to prevent severe outcomes.

European action plans (2018-2030) and the 2024 ESC/ESO guidelines emphasize early detection and management of AF in primary care. Although several AF prediction models exist, their integration into clinical practice remains challenging. AF represents a clinical continuum, with thrombotic risk present even before arrhythmia onset. High-risk patients for AF also show a high incidence of MACE, defined as a composite of myocardial infarction, stroke, systemic embolic events, and cardiovascular death.

The proposed strategy involves developing and clinically validating an Artificial Intelligence (AI) model to improve early thrombotic risk prediction in patients at high risk of AF, using MACE as the primary outcome. This model aims to outperform the traditional CHA₂DS₂-VASc score by incorporating both classical and emerging clinical factors. The estimated timeline from clinical validation to commercialization is approximately 48 months.

AI-based prediction is expected to enable personalized treatment, reduce the incidence of MACE, hospitalizations, and disability, and improve cost-effectiveness, ultimately decreasing the social and economic burden of AF and stroke in Europe.

Eligibility

Primary outcome measure(s)

• Primary Outcome Measures 1. Incidence of First-Ever and Recurrent Stroke — Through study completion, an average of 1 year
  Annual rate of first-ever and recurrent stroke events per 100,000 inhabitants, measured using population-based registries and clinical records.
• Major Adverse Cardiovascular Events (MACE) — Through study completion, an average of 1 year
  Incidence of composite cardiovascular endpoint comprising myocardial infarction, stroke, extracranial systemic embolic events (SEEs), or cardiovascular death

Trial sites (1)

More Fundacio d'Investigacio en Atencio Primaria Jordi Gol i Gurina trials in Spain

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