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Thromb Haemost
DOI: 10.1055/a-2761-5903
TH Scientific Statement

Digital Twins for Predictive Modelling of Thrombosis and Stroke Risk: Current Approaches and Future Directions

A TH Scientific Statement

Authors

  • Adelaide de Vecchi

    1   Department of Biomedical Engineering and Imaging Sciences, King's College London, London, England, United Kingdom of Great Britain and Northern Ireland

  • Oscar Camara

    2   Department of Engineering, Universitat Pompeu Fabra, Barcelona, Spain

  • Riccardo Cavarra

    1   Department of Biomedical Engineering and Imaging Sciences, King's College London, London, England, United Kingdom of Great Britain and Northern Ireland

  • Juan Carlos del Alamo

    3   Department of Mechanical Engineering, Center for Cardiovascular Biology, University of Washington, Seattle, Washington, United States

  • Wahbi El-Bouri

    4   Department of Cardiovascular and Metabolic Medicine, University of Liverpool, Liverpool, Merseyside, United Kingdom of Great Britain and Northern Ireland

  • Albert Ferro

    5   School of Cardiovascular and Metabolic Medicine and Sciences, King's College London, London, England, United Kingdom of Great Britain and Northern Ireland

  • Henry Horng-Shing Lu

    6   Department of Biomedical Artificial Intelligence Academy, Kaohsiung Medical University, Kaohsiung, Kaohsiung City, Taiwan

  • Paolo Melidoro 

    7   School of Biomedical Engineering and Imaging Sciences, King's College, London, United Kingdom of Great Britain and Northern Ireland

  • Shaheim Ogbomo-Harmitt

    1   Department of Biomedical Engineering and Imaging Sciences, King's College London, London, England, United Kingdom of Great Britain and Northern Ireland

  • Ivan Olier

    8   Data Science Research Centre, Liverpool John Moores University, Liverpool, England, United Kingdom of Great Britain and Northern Ireland
    9   Liverpool Centre for Cardiovascular Science, Liverpool John Moores University, Liverpool, England, United Kingdom of Great Britain and Northern Ireland

  • Sandra Ortega-Martorell 

    8   Data Science Research Centre, Liverpool John Moores University, Liverpool, England, United Kingdom of Great Britain and Northern Ireland
    9   Liverpool Centre for Cardiovascular Science, Liverpool John Moores University, Liverpool, England, United Kingdom of Great Britain and Northern Ireland

  • Rushad Patell

    10   Beth Israel Deaconess Medical Center, Boston, Massachusetts, United States

  • Christian Vergara

    11   LABS, Dipartimento di Chimica, Materiali e Ingegneria Chimica, Politecnico di Milano, Milan, Italy

  • Vitaly Volpert

    12   Institut Camille Jordan, University Claude Bernard Lyon 1, Villeurbanne, Auvergne-Rhône-Alpes, France

  • Gregory Y. H. Lip 

    13   Liverpool Centre for Cardiovascular Science at University of Liverpool, Liverpool John Moores University and Liverpool Heart & Chest Hospital, Liverpool, United Kingdom
    14   Danish Center for Health Services Research, Department of Clinical Medicine, Aalborg University, Aalborg, Denmark
    15   Department of Cardiology, Lipidology and Internal Medicine with Intensive Coronary Care Unit, Medical University of Bialystok, Bialystok, Poland

  • Oleg Aslanidi

    1   Department of Biomedical Engineering and Imaging Sciences, King's College London, London, England, United Kingdom of Great Britain and Northern Ireland
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Graphical Abstract

Abstract

Thrombosis drives substantial global mortality across atrial fibrillation, venous thromboembolism, and atherosclerosis. However, clinical scores treat risk as a static variable and omit evolving comorbidities, functional biomarkers, anatomy, and treatment exposure, leading to misclassification and preventable events. This statement advances a unified scientific agenda for patient-specific digital twins that dynamically integrate multimodal longitudinal data with mechanistic insight to predict thrombogenesis risks. We position these digital twins as hybrid models anchored in physics and data-driven algorithms that can simulate disease progression and therapy. The goal of this approach is to refine stroke and bleeding estimation beyond current clinical rules. Continuous updating from imaging data, laboratory test results, wearables, and electronic health records supports dynamic risk trajectories and adaptive care pathways, facilitating continuous risk reassessment. This statement analyzes gaps in data quality, calibration, validation, and uncertainty quantification that presently limit the clinical translation of this technology. Research priorities are then proposed for multiscale thrombosis modelling, physics-informed learning, probabilistic forecasting, and regulatory-compliant data stewardship. Finally, we outline translation to in silico trials, regulatory alignment, and hospital workflows that link predictions to decisions. By articulating shared challenges across thrombosis-driven diseases and reframing risk as a time-varying measurable quantity, this statement lays a foundation for developing digital twin approaches that support a shift from population heuristics towards precise, timely thrombosis care. These advances are essential for translating digital twin technology from research to clinical practice, enabling dynamic risk prediction and personalized anticoagulation therapy.

Keywords

digital twins - computational modelling - computational fluid dynamics - thrombus - thrombosis - venous thromboembolism - deep vein thrombosis - stroke - atrial fibrillation - clinical risk prediction


Publication History

Received: 12 August 2025

Accepted after revision: 02 December 2025

Article published online:
09 February 2026

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