Evidence map›Paper›PMID 39959383›Full record

ArticleAPL bioengineering2025

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

Qiongyao Liu, Toni Lassila, Fengming Lin, Michael MacRaild, Tufail Patankar, Fathallah Islim, Shuang Song, Huanming Xu, Xiang Chen, Zeike A Taylor and 2 more

Abstract read
In one paragraph

Article in APL bioengineering, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

What it found

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

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0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

12 authors.

Qiongyao LiuCentre for Computational Imaging and Simulation Technologies in Biomedicine (CISTIB), School of Computing, University of Leeds, Leeds, United Kingdom.ORCID https://orcid.org/0000-0001-8534-8791
Toni LassilaCentre for Computational Imaging and Simulation Technologies in Biomedicine (CISTIB), School of Computing, University of Leeds, Leeds, United Kingdom.ORCID https://orcid.org/0000-0001-8947-1447
Fengming LinCentre for Computational Imaging and Simulation Technologies in Biomedicine (CISTIB), School of Computing, University of Leeds, Leeds, United Kingdom.ORCID https://orcid.org/0000-0001-8944-6890
Fathallah IslimLeeds General Infirmary, Leeds Teaching Hospitals NHS Trust, Leeds, United Kingdom.ORCID https://orcid.org/0009-0004-1806-8537
Shuang SongSchool of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing, China.
Huanming XuSchool of Life Science, Beijing Institute of Technology, Beijing, China.
Xiang ChenCollege of Electrical and Information Engineering, Hunan University, Changsha, China.ORCID https://orcid.org/0000-0003-4203-4578
Zeike A TaylorCentre for Computational Imaging and Simulation Technologies in Biomedicine (CISTIB), School of Mechanical Engineering, University of Leeds, Leeds, United Kingdom.ORCID https://orcid.org/0000-0002-0718-1663
Ali Sarrami-Foroushani

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Identifiers

PMID39959383
PMCPMC11826514

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.