Predictive Methods for Thrombus Formation in the Treatment of Aortic Dissection and Cerebral Aneurysms: A Comprehensive Review
Thrombus formation plays a crucial role in the clinical treatment of certain diseases. In conditions such as aortic dissection and cerebral aneurysm, complete thrombus occlusion in the affected region is desired to reduce blood flow into the false lumen or aneurysm sac, leading to a decrease in the...
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MDPI AG
2024-08-01
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| Series: | Bioengineering |
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| author | Kenji Komiya Shuta Imada Yoshihiro Ujihara Shukei Sugita Masanori Nakamura |
| author_facet | Kenji Komiya Shuta Imada Yoshihiro Ujihara Shukei Sugita Masanori Nakamura |
| author_sort | Kenji Komiya |
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| description | Thrombus formation plays a crucial role in the clinical treatment of certain diseases. In conditions such as aortic dissection and cerebral aneurysm, complete thrombus occlusion in the affected region is desired to reduce blood flow into the false lumen or aneurysm sac, leading to a decrease in the tension exerted on the vascular wall and making it less likely to rupture. However, desired thrombosis sometimes fails to occur. Predicting thrombus formation can provide valuable information in such cases. This article offers a comprehensive review of conventional methods for predicting thrombus formation. In reviews conducted from the year 2000 to the present, the number of published related papers every five years has increased more than tenfold. We also found that the predictive methods can be classified into two categories: those based on the hemodynamic evaluation parameters and those based on hemodynamic and mathematical models that simulate the transport and reaction of blood components. Through our discussions, we identified several challenges that need to be resolved, including predictions based on patient-specific condition, model validation, multi-scale problems, the mechanisms of thrombus formation, and ensuring cost effectiveness. This review aims to guide researchers interested in exploring thrombus formation prediction within clinical treatments. |
| format | Article |
| id | doaj-art-2a8e072f64a84a8dbf7dd9a0addafcec |
| institution | OA Journals |
| issn | 2306-5354 |
| language | English |
| publishDate | 2024-08-01 |
| publisher | MDPI AG |
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| series | Bioengineering |
| spelling | doaj-art-2a8e072f64a84a8dbf7dd9a0addafcec2025-08-20T01:56:05ZengMDPI AGBioengineering2306-53542024-08-0111987110.3390/bioengineering11090871Predictive Methods for Thrombus Formation in the Treatment of Aortic Dissection and Cerebral Aneurysms: A Comprehensive ReviewKenji Komiya0Shuta Imada1Yoshihiro Ujihara2Shukei Sugita3Masanori Nakamura4Department of Electrical and Mechanical Engineering, Graduate School of Engineering, Nagoya Institute of Technology, Nagoya 466-8555, JapanDepartment of Electrical and Mechanical Engineering, Graduate School of Engineering, Nagoya Institute of Technology, Nagoya 466-8555, JapanDepartment of Electrical and Mechanical Engineering, Graduate School of Engineering, Nagoya Institute of Technology, Nagoya 466-8555, JapanDepartment of Electrical and Mechanical Engineering, Graduate School of Engineering, Nagoya Institute of Technology, Nagoya 466-8555, JapanDepartment of Electrical and Mechanical Engineering, Graduate School of Engineering, Nagoya Institute of Technology, Nagoya 466-8555, JapanThrombus formation plays a crucial role in the clinical treatment of certain diseases. In conditions such as aortic dissection and cerebral aneurysm, complete thrombus occlusion in the affected region is desired to reduce blood flow into the false lumen or aneurysm sac, leading to a decrease in the tension exerted on the vascular wall and making it less likely to rupture. However, desired thrombosis sometimes fails to occur. Predicting thrombus formation can provide valuable information in such cases. This article offers a comprehensive review of conventional methods for predicting thrombus formation. In reviews conducted from the year 2000 to the present, the number of published related papers every five years has increased more than tenfold. We also found that the predictive methods can be classified into two categories: those based on the hemodynamic evaluation parameters and those based on hemodynamic and mathematical models that simulate the transport and reaction of blood components. Through our discussions, we identified several challenges that need to be resolved, including predictions based on patient-specific condition, model validation, multi-scale problems, the mechanisms of thrombus formation, and ensuring cost effectiveness. This review aims to guide researchers interested in exploring thrombus formation prediction within clinical treatments.https://www.mdpi.com/2306-5354/11/9/871thrombogenesisaortic dissectioncerebral aneurysmmathematical modelsimulationprediction |
| spellingShingle | Kenji Komiya Shuta Imada Yoshihiro Ujihara Shukei Sugita Masanori Nakamura Predictive Methods for Thrombus Formation in the Treatment of Aortic Dissection and Cerebral Aneurysms: A Comprehensive Review Bioengineering thrombogenesis aortic dissection cerebral aneurysm mathematical model simulation prediction |
| title | Predictive Methods for Thrombus Formation in the Treatment of Aortic Dissection and Cerebral Aneurysms: A Comprehensive Review |
| title_full | Predictive Methods for Thrombus Formation in the Treatment of Aortic Dissection and Cerebral Aneurysms: A Comprehensive Review |
| title_fullStr | Predictive Methods for Thrombus Formation in the Treatment of Aortic Dissection and Cerebral Aneurysms: A Comprehensive Review |
| title_full_unstemmed | Predictive Methods for Thrombus Formation in the Treatment of Aortic Dissection and Cerebral Aneurysms: A Comprehensive Review |
| title_short | Predictive Methods for Thrombus Formation in the Treatment of Aortic Dissection and Cerebral Aneurysms: A Comprehensive Review |
| title_sort | predictive methods for thrombus formation in the treatment of aortic dissection and cerebral aneurysms a comprehensive review |
| topic | thrombogenesis aortic dissection cerebral aneurysm mathematical model simulation prediction |
| url | https://www.mdpi.com/2306-5354/11/9/871 |
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