Predictors of Abdominal Aortic Aneurysm Risks
Computational biomechanics via finite element analysis (FEA) has long promised a means of assessing patient-specific abdominal aortic aneurysm (AAA) rupture risk with greater efficacy than current clinically used size-based criteria. The pursuit stems from the notion that AAA rupture occurs when wal...
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2020-07-01
|
| Series: | Bioengineering |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2306-5354/7/3/79 |
| _version_ | 1797561683878608896 |
|---|---|
| author | Stephen J. Haller Amir F. Azarbal Sandra Rugonyi |
| author_facet | Stephen J. Haller Amir F. Azarbal Sandra Rugonyi |
| author_sort | Stephen J. Haller |
| collection | DOAJ |
| description | Computational biomechanics via finite element analysis (FEA) has long promised a means of assessing patient-specific abdominal aortic aneurysm (AAA) rupture risk with greater efficacy than current clinically used size-based criteria. The pursuit stems from the notion that AAA rupture occurs when wall stress exceeds wall strength. Quantification of peak (maximum) wall stress (PWS) has been at the cornerstone of this research, with numerous studies having demonstrated that PWS better differentiates ruptured AAAs from non-ruptured AAAs. In contrast to wall stress models, which have become progressively more sophisticated, there has been relatively little progress in estimating patient-specific wall strength. This is because wall strength cannot be inferred non-invasively, and measurements from excised patient tissues show a large spectrum of wall strength values. In this review, we highlight studies that investigated the relationship between biomechanics and AAA rupture risk. We conclude that combining wall stress and wall strength approximations should provide better estimations of AAA rupture risk. However, before personalized biomechanical AAA risk assessment can become a reality, better methods for estimating patient-specific wall properties or surrogate markers of aortic wall degradation are needed. Artificial intelligence methods can be key in stratifying patients, leading to personalized AAA risk assessment. |
| first_indexed | 2024-03-10T18:18:24Z |
| format | Article |
| id | doaj.art-ee1b374e961546daad6df901a628101d |
| institution | Directory Open Access Journal |
| issn | 2306-5354 |
| language | English |
| last_indexed | 2024-03-10T18:18:24Z |
| publishDate | 2020-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Bioengineering |
| spelling | doaj.art-ee1b374e961546daad6df901a628101d2023-11-20T07:32:52ZengMDPI AGBioengineering2306-53542020-07-01737910.3390/bioengineering7030079Predictors of Abdominal Aortic Aneurysm RisksStephen J. Haller0Amir F. Azarbal1Sandra Rugonyi2Department of Biomedical Engineering, Oregon Health & Science University, Portland, OR 97239, USADivision of Vascular Surgery, Oregon Health & Science University, Portland, OR 97239, USADepartment of Biomedical Engineering, Oregon Health & Science University, Portland, OR 97239, USAComputational biomechanics via finite element analysis (FEA) has long promised a means of assessing patient-specific abdominal aortic aneurysm (AAA) rupture risk with greater efficacy than current clinically used size-based criteria. The pursuit stems from the notion that AAA rupture occurs when wall stress exceeds wall strength. Quantification of peak (maximum) wall stress (PWS) has been at the cornerstone of this research, with numerous studies having demonstrated that PWS better differentiates ruptured AAAs from non-ruptured AAAs. In contrast to wall stress models, which have become progressively more sophisticated, there has been relatively little progress in estimating patient-specific wall strength. This is because wall strength cannot be inferred non-invasively, and measurements from excised patient tissues show a large spectrum of wall strength values. In this review, we highlight studies that investigated the relationship between biomechanics and AAA rupture risk. We conclude that combining wall stress and wall strength approximations should provide better estimations of AAA rupture risk. However, before personalized biomechanical AAA risk assessment can become a reality, better methods for estimating patient-specific wall properties or surrogate markers of aortic wall degradation are needed. Artificial intelligence methods can be key in stratifying patients, leading to personalized AAA risk assessment.https://www.mdpi.com/2306-5354/7/3/79abdominal aneurysmaortic aneurysmaorta biomechanicsrisk assessmentrupture potential indexaortic wall stress |
| spellingShingle | Stephen J. Haller Amir F. Azarbal Sandra Rugonyi Predictors of Abdominal Aortic Aneurysm Risks Bioengineering abdominal aneurysm aortic aneurysm aorta biomechanics risk assessment rupture potential index aortic wall stress |
| title | Predictors of Abdominal Aortic Aneurysm Risks |
| title_full | Predictors of Abdominal Aortic Aneurysm Risks |
| title_fullStr | Predictors of Abdominal Aortic Aneurysm Risks |
| title_full_unstemmed | Predictors of Abdominal Aortic Aneurysm Risks |
| title_short | Predictors of Abdominal Aortic Aneurysm Risks |
| title_sort | predictors of abdominal aortic aneurysm risks |
| topic | abdominal aneurysm aortic aneurysm aorta biomechanics risk assessment rupture potential index aortic wall stress |
| url | https://www.mdpi.com/2306-5354/7/3/79 |
| work_keys_str_mv | AT stephenjhaller predictorsofabdominalaorticaneurysmrisks AT amirfazarbal predictorsofabdominalaorticaneurysmrisks AT sandrarugonyi predictorsofabdominalaorticaneurysmrisks |