throMboembolic Risk Associated To High atrIal Fibrillation riSk (MATHIAS)

February 27, 2026 updated by: Josep Lluís Clua Espuny, Fundacio d'Investigacio en Atencio Primaria Jordi Gol i Gurina

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

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.

Study Overview

Status

Not yet recruiting

Conditions

Intervention / Treatment

Detailed Description

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.

Preliminary retrospective analyses based on validated AF risk stratification cohorts (AFRICAT NCT03188484 and PREFATE NCT05772806) and five pilot ML models demonstrated promising results. The Adaboost model significantly outperformed CHA₂DS₂-VASc in predicting MACE (AUC 99.99% vs. 81.71%; p = 0.0034). While these findings are encouraging, prospective, multicenter evaluation is required to assess generalizability, optimal follow-up intervals, patient selection (including high-risk, TIA/stroke, and varying CHA₂DS₂-VASc strata), and impact on patient-important outcomes such as stroke, bleeding, quality of life, and cost-effectiveness.

The project also incorporates a Markov decision-analytic model to estimate MACE, stroke, disability, quality-adjusted life years (QALYs), and costs from both healthcare payer and societal perspectives. Scenario analyses will evaluate whether integrating the AI model into routine care is cost-effective compared with usual care, opportunistic screening, or wearable-first strategies. Special attention will be given to sex-specific outcomes and potential inequities in benefit distribution.

The anticipated clinical impact includes reduction of MACE incidence to below 50 per 1,000 person-years in high-risk populations, lowering AF-related stroke proportion to under 10%, improving anticoagulation appropriateness in up to 65% of eligible patients, and ensuring that at least 90% of high-risk individuals receive appropriate oral anticoagulant therapy. Adherence to structured AF care pathways (e.g., AF-CARE) has already been associated with significant reductions in all-cause mortality and MACE; integrating AI-driven risk stratification may further enhance these benefits.

Economically, although screening and broader anticoagulant use may initially increase direct costs, stroke prevention is expected to generate substantial long-term savings. In Catalonia alone, estimated annual savings for high-risk AF populations range from €12.3 million to €79.2 million. Reductions in stroke severity, disability, and hospitalizations will mitigate both direct medical costs and indirect societal costs related to loss of productivity and long-term dependency.

In summary, this project addresses a major gap in cardiovascular prevention by developing and implementing an AI-driven thromboembolic risk stratification model for individuals at high risk of AF. By aligning with contemporary European guidelines and precision medicine strategies, the initiative seeks to improve early identification, personalize therapeutic decisions, reduce MACE and stroke burden, preserve patient autonomy and quality of life, and ensure sustainable healthcare resource utilization.

Study Type

Observational

Enrollment (Estimated)

1000

Contacts and Locations

This section provides the contact details for those conducting the study, and information on where this study is being conducted.

Study Contact

Study Contact Backup

Study Locations

  • Spain
    • Tarragona
      • Tortosa, Tarragona, Spain, 43500
        • EAP Tortose est. Servei d'Atencio Primaria i Comunitària. Institut Catala de la Salit
        • Contact:
        • Contact:
        • Principal Investigator:
          • Eulalia Muria-Subirats, PhD
        • Sub-Investigator:
          • Anna Panisello-Tafalla, pre-PhD

Participation Criteria

Researchers look for people who fit a certain description, called eligibility criteria. Some examples of these criteria are a person's general health condition or prior treatments.

Eligibility Criteria

Ages Eligible for Study

  • Older Adult

Accepts Healthy Volunteers

Yes

Sampling Method

Non-Probability Sample

Study Population

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.

Description

Inclusion Criteria (PREFATE study): assessed at baseline

  • 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).

Exclusion Criteria: Patients with the following conditions will be excluded (exclusion criteria):

  • 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.
  • Active anticoagulant treatment at the inclusion.
  • Vital prognosis less than 1 year.
  • Pacemaker carriers.

Study Plan

This section provides details of the study plan, including how the study is designed and what the study is measuring.

How is the study designed?

Design Details

Cohorts and Interventions

Group / Cohort
Intervention / Treatment
control
Usual care (comparator): Opportunistic AF detection during routine clinical encounters and anticoagulation guided by the CHA₂DS₂VA score in patients with documented AF, without any AI-based pre-AF risk assessment. This approach reflects current guideline-concordant practice in many European primary care settings, where AF digital screening has not yet been implemented.
Procedure: 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.

What is the study measuring?

Primary Outcome Measures

Outcome Measure
Measure Description
Time Frame
Primary Outcome Measures 1. Incidence of First-Ever and Recurrent Stroke
Time Frame: 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.
Through study completion, an average of 1 year
Major Adverse Cardiovascular Events (MACE)
Time Frame: 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
Through study completion, an average of 1 year

Secondary Outcome Measures

Outcome Measure
Measure Description
Time Frame
Early Detection of Atrial Fibrillation
Time Frame: Baseline and through study completion, an average of 1 year.
Proportion of newly diagnosed atrial fibrillation cases identified at early or asymptomatic stages, defined as diagnosis prior to the occurrence of thromboembolic complications.
Baseline and through study completion, an average of 1 year.
Systematic Bleeding Risk Assessment in Complex Chronic Patients
Time Frame: through study completion, an average of 1 year
Proportion of high-complexity chronic patients (GMA-4 category) who undergo standardized bleeding risk evaluation using validated clinical risk scores (HAS-BLED).
through study completion, an average of 1 year
Sex-Based Differences in Cardiovascular Care and Outcomes
Time Frame: Through study completion, an average of 1 year

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)
Through study completion, an average of 1 year

Other Outcome Measures

Outcome Measure
Measure Description
Time Frame
Post-Stroke Healthcare and Socio-Healthcare Costs
Time Frame: From the index stroke to 12 months post-event
Mean total direct healthcare and socio-healthcare costs per stroke episode during the first year after the index event.
From the index stroke to 12 months post-event
Preventable Cardiovascular Hospitalizations
Time Frame: Through study completion, an average of 1 year
Rate of avoidable hospital admissions due to cardiovascular complications among high-risk patients, defined according to standardized preventability criteria.
Through study completion, an average of 1 year

Collaborators and Investigators

This is where you will find people and organizations involved with this study.

Sponsor

Fundacio d'Investigacio en Atencio Primaria Jordi Gol i Gurina

Collaborators

Institut Catala de Salut

Investigators

  • Principal Investigator: Josep Clua-Espuny, PhD, FUNDACIO INSTITUT UNIVERSITARI PERA LA RECERCA A L'ATENCIO PRIMARIA DE SALUT JORDI GOL I GURINA

Publications and helpful links

The person responsible for entering information about the study voluntarily provides these publications. These may be about anything related to the study.

General Publications

Helpful Links

Study record dates

These dates track the progress of study record and summary results submissions to ClinicalTrials.gov. Study records and reported results are reviewed by the National Library of Medicine (NLM) to make sure they meet specific quality control standards before being posted on the public website.

Study Major Dates

Study Start (Estimated)

July 6, 2026

Primary Completion (Estimated)

October 29, 2027

Study Completion (Estimated)

December 31, 2028

Study Registration Dates

First Submitted

February 20, 2026

First Submitted That Met QC Criteria

February 27, 2026

First Posted (Actual)

March 2, 2026

Study Record Updates

Last Update Posted (Actual)

March 2, 2026

Last Update Submitted That Met QC Criteria

February 27, 2026

Last Verified

February 1, 2026

More Information

Terms related to this study

Keywords

Additional Relevant MeSH Terms

Other Study ID Numbers

  • IDIAPJGol 25/332-P
  • CEIm IDIAPJGol 25/332-P (Other Identifier: PREDOC_ECO-24/11 Grant number: 7Z25/024-1 Institut Catala Salut/IDIAP)

Plan for Individual participant data (IPD)

Plan to Share Individual Participant Data (IPD)?

UNDECIDED

IPD Plan Description

The data that support the findings of this study will be available from Institut Catala de la Salut. There are restrictions about the availability of these datasets, which will be used under license for the current study, and so are not publicly available. Datasets generated and analysed during the current study may be available with permission of the Institut Catala de la Salut (sensitive data).

Study Data/Documents

  1. Video-recording
    Information identifier: jlclua.ebre.ics@gencat.cat

Drug and device information, study documents

Studies a U.S. FDA-regulated drug product

No

Studies a U.S. FDA-regulated device product

No

This information was retrieved directly from the website clinicaltrials.gov without any changes. If you have any requests to change, remove or update your study details, please contact register@clinicaltrials.gov. As soon as a change is implemented on clinicaltrials.gov, this will be updated automatically on our website as well.

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