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.
Adults aged 65-95 years without prior AF and at High risk of AF, according to the risk score validated in the AFRICAT (Atrial Fibrilation Research in CATalonia) study. This scale considers the following variables for risk calculation: sex, age, weight, cardiac rate and CHA2DS2-VASc (congestive heart failure, hypertension, age ≥75 (doubled), diabetes mellitus, prior stroke or transient ischemic attack (doubled), vascular disease, age 65-74, female) score.
with active records in the HCC3/CMBD systems
CHA2DS2-VASc score≥2.
Ability to use a smart phone (or at least the caregiver).
Previous diagnosis of AF.
Previous diagnosis of stroke.
Severe cognitive impairment, with a score on the Global Deterioration Scale (GDS)≥3.
Severe functional impairment, with a Barthel score ≤60, or modified Rankin score≥4.
This study will use routinely collected data from the SAP Terres de l'Ebre primary-care database (Catalonia, Spain) covering 178,112 inhabitants (49.6% women) managed in 11 primary-care health centers. The region is characterized by advanced population aging \[19\] (aging index 159.5 vs 131.3 in Catalonia and 118.4 in Spain) and lower average per-capita income \[20\] (77.4% of the Catalan mean). Adults aged 65-95 years without prior AF and with active records in the HCC3/CMBD systems at baseline. This cohort is characterized by multimorbidity and high-predicted risk of AF and related complications reflecting patients typically managed in primary care in European health systems, providing a real-world setting with high cardiovascular burden and constrained resources.
Case-control
Prospective
AI_MATHIAS
Procedure/SurgeryMATHIAS-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.
Primary Outcome Measures 1. Incidence of First-Ever and Recurrent Stroke
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)
Incidence of composite cardiovascular endpoint comprising myocardial infarction, stroke, extracranial systemic embolic events (SEEs), or cardiovascular death
Early Detection of Atrial Fibrillation
Proportion of newly diagnosed atrial fibrillation cases identified at early or asymptomatic stages, defined as diagnosis prior to the occurrence of thromboembolic complications.
Systematic Bleeding Risk Assessment in Complex Chronic Patients
Proportion of high-complexity chronic patients (GMA-4 category) who undergo standardized bleeding risk evaluation using validated clinical risk scores (HAS-BLED).
Sex-Based Differences in Cardiovascular Care and Outcomes
Differences between women and men in: Time to atrial fibrillation diagnosis Proportion receiving guideline-recommended anticoagulation Stroke prevention coverage Cardiovascular outcomes (stroke recurrence, MACE, mortality)
Post-Stroke Healthcare and Socio-Healthcare Costs
Mean total direct healthcare and socio-healthcare costs per stroke episode during the first year after the index event.
Preventable Cardiovascular Hospitalizations
Rate of avoidable hospital admissions due to cardiovascular complications among high-risk patients, defined according to standardized preventability criteria.
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Fundacio d'Investigacio en Atencio Primaria Jordi Gol i Gurina
Lead sponsor
Institut Catala de Salut
Collaborator
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