Liu, Q., Lassila, T. orcid.org/0000-0001-8947-1447, Lin, F. et al. (9 more authors) (2025) Key influencers in an aneurysmal thrombosis model: A sensitivity analysis and validation study. APL Bioengineering, 9 (1). 016107. ISSN 2473-2877
Abstract
Thrombosis is a biological response closely related to intracranial aneurysms, and the formation of thrombi inside the aneurysm is an important determinant of outcome after endovascular therapy. As the regulation of thrombosis is immensely complicated and the mechanisms governing thrombus formation are not fully understood, mathematical and computational modeling has been increasingly used to gain insight into thrombosis over the last 30 years. To have a robust computational thrombosis model for possible clinical use in the future, it is essential to assess the model's reliability through comprehensive sensitivity analysis of model parameters and validation studies based on clinical information of real patients. Here, we conduct a global sensitivity analysis on a previously developed thrombosis model, utilizing thrombus composition, the flow-induced platelet index, and the bound platelet concentration as output metrics. These metrics are selected for their relevance to thrombus stability. The flow-induced platelet index quantifies the effect of blood flow on the transport of platelets to and from the site of thrombus formation and thus on the final platelet content of the formed thrombus. The sensitivity analysis of the thrombus composition indicates that the concentration of resting platelets most influences the final thrombus composition. Then, for the first time, we validate the thrombosis model based on a real patient case using patient-specific resting platelet concentration and two previously calibrated trigger thresholds for thrombosis initiation. We show that our thrombosis model is capable of predicting thrombus formation both before and after endovascular treatment.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © Author(s) 2025. This is an open access article under the terms of the Creative Commons Attribution-NonCommercial License (CC-BY-NC 4.0). |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Computing (Leeds) > Biomedical & Health |
Funding Information: | Funder Grant number Royal Academy of Engineering CiET1819\19 |
Depositing User: | Symplectic Publications |
Date Deposited: | 10 Jan 2025 11:20 |
Last Modified: | 06 Mar 2025 11:44 |
Status: | Published |
Publisher: | AIP Publishing |
Identification Number: | 10.1063/5.0223753 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:221597 |