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

Area of Science:

• Cardiovascular Medicine
• Computational Biology
• Biomedical Engineering

Background:

• Thrombosis causes significant global mortality through conditions like atrial fibrillation, venous thromboembolism, and atherosclerosis.
• Current clinical risk scores are static, failing to account for dynamic patient factors, leading to misclassification and preventable thrombotic events.

Purpose of the Study:

• To advance a scientific agenda for patient-specific digital twins that dynamically integrate multimodal longitudinal data with mechanistic insights for predicting thrombogenesis risks.
• To refine stroke and bleeding risk estimation beyond existing clinical guidelines.

Main Methods:

• Proposing hybrid models combining physics-based principles with data-driven algorithms for simulating disease progression and therapy.
• Utilizing continuous updates from imaging, lab results, wearables, and electronic health records for dynamic risk assessment.
• Identifying research priorities in multiscale modeling, physics-informed learning, probabilistic forecasting, and data stewardship.

Main Results:

• Digital twins offer a framework for dynamic risk trajectories and adaptive care pathways, enabling continuous risk reassessment.
• Analysis identifies gaps in data quality, calibration, validation, and uncertainty quantification hindering clinical translation.
• Proposed research priorities address multiscale modeling, physics-informed learning, probabilistic forecasting, and regulatory-compliant data stewardship.

Conclusions:

• Digital twin technology holds promise for personalized anticoagulation therapy by shifting from population heuristics to precise, timely risk prediction.
• Translating digital twins into clinical practice requires addressing challenges in data, modeling, validation, and regulatory pathways.
• This approach facilitates a transition towards dynamic risk assessment and adaptive treatment strategies for thrombosis-driven diseases.