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The Helsinki Rat Microsurgical Sidewall Aneurysm Model

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

Qiongyao Liu¹, Toni Lassila¹, Fengming Lin¹

¹Centre for Computational Imaging and Simulation Technologies in Biomedicine (CISTIB), School of Computing, University of Leeds, Leeds, United Kingdom.

APL Bioengineering

|February 17, 2025

Summary

Computational modeling enhances understanding of thrombosis in intracranial aneurysms. Sensitivity analysis reveals resting platelet concentration is key, and the model accurately predicts thrombus formation before and after treatment.

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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.