Key influencers in an aneurysmal thrombosis model: A sensitivity analysis and validation study.

Area of Science:

• Biomedical Engineering
• Computational Biology
• Medical Physics

Background:

• Thrombosis is critical in intracranial aneurysms and impacts endovascular therapy outcomes.
• Understanding thrombus formation mechanisms is incomplete, necessitating advanced modeling techniques.
• Computational models require rigorous validation for clinical applicability.

Purpose of the Study:

• To perform global sensitivity analysis on a computational thrombosis model.
• To validate the model using patient-specific clinical data.
• To assess the model's predictive capability for thrombus formation in intracranial aneurysms.

Main Methods:

• Global sensitivity analysis of a previously developed thrombosis model.
• Utilized thrombus composition, flow-induced platelet index, and bound platelet concentration as output metrics.
• Validated the model against a real patient case with patient-specific data.

Main Results:

• Resting platelet concentration was identified as the most influential parameter on final thrombus composition.
• The flow-induced platelet index quantifies blood flow effects on platelet transport and thrombus content.
• The validated model accurately predicted thrombus formation pre- and post-endovascular treatment.

Conclusions:

• Computational modeling, supported by sensitivity analysis and clinical validation, is crucial for understanding intracranial aneurysm thrombosis.
• The developed model demonstrates reliability and potential for future clinical use in predicting treatment outcomes.
• Identifying key parameters like resting platelet concentration refines thrombosis prediction accuracy.