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Patient-specific digital twins in aortic disease: integrating computational hemodynamics, immune profiling, and
1INVAMED Medical Innovation Institute, New York, NY, United States.
Insights
Patient-specific digital twins for aortic aneurysms integrate biomechanics, hemodynamics, and immunology. This approach predicts disease progression and guides personalized treatment, moving beyond static size measurements for better outcomes.
Area of Science:
- Cardiovascular Medicine
- Biomedical Engineering
- Computational Biology
Background:
- Aortic aneurysms and acute aortic syndromes cause significant cardiovascular morbidity and mortality.
- Current risk stratification relies on static anatomical thresholds, neglecting disease dynamics.
Purpose of the Study:
- To propose a five-domain architecture for patient-specific aortic digital twins.
- To enable precise decision-making for aortic disease management.
Main Methods:
- Integrating multimodal patient data into a continuously updated computational aorta model.
- Incorporating structural-biomechanics, computational hemodynamics, and immunobiology.
- Utilizing predictive intelligence for individualized growth and complication predictions.
Main Results:
- A framework linking inflammation and mechanics as a feedback ecosystem.
- Potential for individualized prediction of aortic disease trajectories.
- Translation of digital twin outputs into procedural planning and surveillance.
Conclusions:
- Aortic digital twins offer a paradigm shift from static measurements to dynamic, individualized disease prediction.
- This approach facilitates earlier, personalized, and lasting prevention of adverse aortic events.
- Clinical translation requires validation, workflow integration, and management considerations.
Abstract:
Aortic aneurysms and acute aortic syndromes continue to be significant contributors to cardiovascular morbidity and mortality; however, current risk stratification and treatment timing rely heavily on static anatomical thresholds that do not fully reflect the dynamic biology and mechanics of disease progression. Patient-specific digital twins offer a unifying paradigm where multimodal patient data are integrated into a continuously updated computational representation of an individual aorta to predict trajectories and support precise decision-making. In this review, we propose a five-domain architecture for an aortic digital twin (1): a structural-biomechanical substrate that reconstructs patient geometry and predicts wall stress and remodeling tendency; (2) computational hemodynamics to quantify flow-derived descriptors such as wall shear stress, oscillatory shear, and stagnation; (3) immunobiological integration to incorporate inflammatory activity, immune cell heterogeneity, and proteolytic remodeling signals; (4) predictive intelligence that combines multimodal features, generates individualized growth and complication predictions through uncertainty quantification, and updates predictions with longitudinal data; and (5) a sophisticated endovascular strategy layer that translates twin outputs into procedural planning, device selection, and risk-corrected surveillance. This framework highlights how the bidirectional link between inflammation and mechanics can be functionalized as a feedback ecosystem rather than a set of independent analyses, and outlines the evidence requirements for clinical translation, including validation pathways, workflow integration, and management considerations. By shifting the clinical focus from "Is the aorta large enough?" to "How will this patient's disease develop, and how can we best modify the course?", aortic digital twins can enable earlier, more individualized, and more lasting prevention of adverse aortic events.
