Wall Stress and Geometry Measures in Electively Repaired Abdominal Aortic Aneurysms. View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2019-04-08

AUTHORS

Wei Wu, Balaji Rengarajan, Mirunalini Thirugnanasambandam, Shalin Parikh, Raymond Gomez, Victor De Oliveira, Satish C Muluk, Ender A Finol

ABSTRACT

Abdominal aortic aneurysm (AAA) is a vascular disease characterized by the enlargement of the infrarenal segment of the aorta. A ruptured AAA can cause internal bleeding and carries a high mortality rate, which is why the clinical management of the disease is focused on preventing aneurysm rupture. AAA rupture risk is estimated by the change in maximum diameter over time (i.e., growth rate) or if the diameter reaches a prescribed threshold. The latter is typically 5.5 cm in most clinical centers, at which time surgical intervention is recommended. While a size-based criterion is suitable for most patients who are diagnosed at an early stage of the disease, it is well known that some small AAA rupture or patients become symptomatic prior to a maximum diameter of 5.5 cm. Consequently, the mechanical stress in the aortic wall can also be used as an integral component of a biomechanics-based rupture risk assessment strategy. In this work, we seek to identify geometric characteristics that correlate strongly with wall stress using a sample space of 100 asymptomatic, unruptured, electively repaired AAA models. The segmentation of the clinical images, volume meshing, and quantification of up to 45 geometric measures of each AAA were done using in-house Matlab scripts. Finite element analysis was performed to compute the first principal stress distributions from which three global biomechanical parameters were calculated: peak wall stress, 99th percentile wall stress and spatially averaged wall stress. Following a feature reduction approach consisting of Pearson's correlation matrices with Bonferroni correction and linear regressions, a multivariate stepwise regression analysis was conducted to find the geometric measures most highly correlated with each of the biomechanical parameters. Our findings indicate that wall stress can be predicted by geometric indices with an accuracy of up to 94% when AAA models are generated with uniform wall thickness and up to 67% for patient specific, non-uniform wall thickness AAA. These geometric predictors of wall stress could be used in lieu of complex finite element models as part of a geometry-based protocol for rupture risk assessment. More... »

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s10439-019-02261-w

DOI

http://dx.doi.org/10.1007/s10439-019-02261-w

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1113301116

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/30963384


Indexing Status Check whether this publication has been indexed by Scopus and Web Of Science using the SN Indexing Status Tool
Incoming Citations Browse incoming citations for this publication using opencitations.net

JSON-LD is the canonical representation for SciGraph data.

TIP: You can open this SciGraph record using an external JSON-LD service: JSON-LD Playground Google SDTT

[
  {
    "@context": "https://springernature.github.io/scigraph/jsonld/sgcontext.json", 
    "about": [
      {
        "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/1102", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Cardiorespiratory Medicine and Haematology", 
        "type": "DefinedTerm"
      }, 
      {
        "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/11", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Medical and Health Sciences", 
        "type": "DefinedTerm"
      }
    ], 
    "author": [
      {
        "affiliation": {
          "alternateName": "The University of Texas at San Antonio", 
          "id": "https://www.grid.ac/institutes/grid.215352.2", 
          "name": [
            "Department of Mechanical Engineering, University of Texas at San Antonio, One UTSA Circle, San Antonio, TX, 78249, USA."
          ], 
          "type": "Organization"
        }, 
        "familyName": "Wu", 
        "givenName": "Wei", 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "The University of Texas at San Antonio", 
          "id": "https://www.grid.ac/institutes/grid.215352.2", 
          "name": [
            "Department of Mechanical Engineering, University of Texas at San Antonio, One UTSA Circle, San Antonio, TX, 78249, USA."
          ], 
          "type": "Organization"
        }, 
        "familyName": "Rengarajan", 
        "givenName": "Balaji", 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "The University of Texas at San Antonio", 
          "id": "https://www.grid.ac/institutes/grid.215352.2", 
          "name": [
            "UTSA/UTHSA Joint Graduate Program in Biomedical Engineering, University of Texas at San Antonio, One UTSA Circle, San Antonio, TX, 78249, USA."
          ], 
          "type": "Organization"
        }, 
        "familyName": "Thirugnanasambandam", 
        "givenName": "Mirunalini", 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "The University of Texas at San Antonio", 
          "id": "https://www.grid.ac/institutes/grid.215352.2", 
          "name": [
            "UTSA/UTHSA Joint Graduate Program in Biomedical Engineering, University of Texas at San Antonio, One UTSA Circle, San Antonio, TX, 78249, USA."
          ], 
          "type": "Organization"
        }, 
        "familyName": "Parikh", 
        "givenName": "Shalin", 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "The University of Texas at San Antonio", 
          "id": "https://www.grid.ac/institutes/grid.215352.2", 
          "name": [
            "Department of Mechanical Engineering, University of Texas at San Antonio, One UTSA Circle, San Antonio, TX, 78249, USA."
          ], 
          "type": "Organization"
        }, 
        "familyName": "Gomez", 
        "givenName": "Raymond", 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "The University of Texas at San Antonio", 
          "id": "https://www.grid.ac/institutes/grid.215352.2", 
          "name": [
            "Department of Management Science and Statistics, University of Texas at San Antonio, One UTSA Circle, San Antonio, TX, 78249, USA."
          ], 
          "type": "Organization"
        }, 
        "familyName": "De Oliveira", 
        "givenName": "Victor", 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Allegheny General Hospital", 
          "id": "https://www.grid.ac/institutes/grid.413621.3", 
          "name": [
            "Department of Thoracic & Cardiovascular Surgery, Allegheny Health Network, Allegheny General Hospital, 320 E. North Ave., Pittsburgh, PA, 15212, USA."
          ], 
          "type": "Organization"
        }, 
        "familyName": "Muluk", 
        "givenName": "Satish C", 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "The University of Texas at San Antonio", 
          "id": "https://www.grid.ac/institutes/grid.215352.2", 
          "name": [
            "Department of Mechanical Engineering, University of Texas at San Antonio, One UTSA Circle, San Antonio, TX, 78249, USA. ender.finol@utsa.edu.", 
            "UTSA/UTHSA Joint Graduate Program in Biomedical Engineering, University of Texas at San Antonio, One UTSA Circle, San Antonio, TX, 78249, USA. ender.finol@utsa.edu."
          ], 
          "type": "Organization"
        }, 
        "familyName": "Finol", 
        "givenName": "Ender A", 
        "type": "Person"
      }
    ], 
    "citation": [
      {
        "id": "https://doi.org/10.1067/mva.2003.119", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1007965623"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.jvs.2010.05.117", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1011349505"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.jmbbm.2015.07.029", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1012948177"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s10439-010-0175-3", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1016650194", 
          "https://doi.org/10.1007/s10439-010-0175-3"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s10439-010-0175-3", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1016650194", 
          "https://doi.org/10.1007/s10439-010-0175-3"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.jvs.2015.11.051", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1018088724"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s10439-012-0691-4", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1018683007", 
          "https://doi.org/10.1007/s10439-012-0691-4"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/s0021-9290(99)00201-8", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1021842866"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1001/jama.287.22.2968", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1025353701"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.ejvs.2008.09.007", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1026149368"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.jvs.2010.12.053", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1030640456"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1114/1.202", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1031437224", 
          "https://doi.org/10.1114/1.202"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s10439-010-0165-5", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1031967822", 
          "https://doi.org/10.1007/s10439-010-0165-5"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s10439-010-0165-5", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1031967822", 
          "https://doi.org/10.1007/s10439-010-0165-5"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s10439-010-0067-6", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1032495765", 
          "https://doi.org/10.1007/s10439-010-0067-6"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s10439-010-0067-6", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1032495765", 
          "https://doi.org/10.1007/s10439-010-0067-6"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1098/rsif.2015.0852", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1040393798"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1146/annurev.ps.46.020195.003021", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1041487701"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.jvs.2013.10.097", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1042037849"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1002/bjs.9578", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1043859248"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.ejvs.2009.09.026", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1044312471"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1118/1.3284976", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1044922678"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1177/0003319706290741", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1045559008"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1177/0003319706290741", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1045559008"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s10439-010-0094-3", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1045969195", 
          "https://doi.org/10.1007/s10439-010-0094-3"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s10439-010-0094-3", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1045969195", 
          "https://doi.org/10.1007/s10439-010-0094-3"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1067/mva.2002.125478", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1049778061"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1067/mva.2002.125478", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1049778061"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.jbiomech.2005.10.021", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1051593106"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1115/1.3127256", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1062102280"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1115/1.3127256", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1062102280"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://app.dimensions.ai/details/publication/pub.1078388662", 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s10439-017-1837-1", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1085068265", 
          "https://doi.org/10.1007/s10439-017-1837-1"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s10439-017-1837-1", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1085068265", 
          "https://doi.org/10.1007/s10439-017-1837-1"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1038/s41598-017-04699-1", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1090349985", 
          "https://doi.org/10.1038/s41598-017-04699-1"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://app.dimensions.ai/details/publication/pub.1106889177", 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1002/9781118033050", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1106889177"
        ], 
        "type": "CreativeWork"
      }
    ], 
    "datePublished": "2019-04-08", 
    "datePublishedReg": "2019-04-08", 
    "description": "Abdominal aortic aneurysm (AAA) is a vascular disease characterized by the enlargement of the infrarenal segment of the aorta. A ruptured AAA can cause internal bleeding and carries a high mortality rate, which is why the clinical management of the disease is focused on preventing aneurysm rupture. AAA rupture risk is estimated by the change in maximum diameter over time (i.e., growth rate) or if the diameter reaches a prescribed threshold. The latter is typically 5.5\u00a0cm in most clinical centers, at which time surgical intervention is recommended. While a size-based criterion is suitable for most patients who are diagnosed at an early stage of the disease, it is well known that some small AAA rupture or patients become symptomatic prior to a maximum diameter of 5.5\u00a0cm. Consequently, the mechanical stress in the aortic wall can also be used as an integral component of a biomechanics-based rupture risk assessment strategy. In this work, we seek to identify geometric characteristics that correlate strongly with wall stress using a sample space of 100 asymptomatic, unruptured, electively repaired AAA models. The segmentation of the clinical images, volume meshing, and quantification of up to 45 geometric measures of each AAA were done using in-house Matlab scripts. Finite element analysis was performed to compute the first principal stress distributions from which three global biomechanical parameters were calculated: peak wall stress, 99th percentile wall stress and spatially averaged wall stress. Following a feature reduction approach consisting of Pearson's correlation matrices with Bonferroni correction and linear regressions, a multivariate stepwise regression analysis was conducted to find the geometric measures most highly correlated with each of the biomechanical parameters. Our findings indicate that wall stress can be predicted by geometric indices with an accuracy of up to 94% when AAA models are generated with uniform wall thickness and up to 67% for patient specific, non-uniform wall thickness AAA. These geometric predictors of wall stress could be used in lieu of complex finite element models as part of a geometry-based protocol for rupture risk assessment.", 
    "genre": "research_article", 
    "id": "sg:pub.10.1007/s10439-019-02261-w", 
    "inLanguage": [
      "en"
    ], 
    "isAccessibleForFree": false, 
    "isFundedItemOf": [
      {
        "id": "sg:grant.3933415", 
        "type": "MonetaryGrant"
      }
    ], 
    "isPartOf": [
      {
        "id": "sg:journal.1087247", 
        "issn": [
          "0145-3068", 
          "1573-9686"
        ], 
        "name": "Annals of Biomedical Engineering", 
        "type": "Periodical"
      }
    ], 
    "name": "Wall Stress and Geometry Measures in Electively Repaired Abdominal Aortic Aneurysms.", 
    "productId": [
      {
        "name": "doi", 
        "type": "PropertyValue", 
        "value": [
          "10.1007/s10439-019-02261-w"
        ]
      }, 
      {
        "name": "dimensions_id", 
        "type": "PropertyValue", 
        "value": [
          "pub.1113301116"
        ]
      }, 
      {
        "name": "nlm_unique_id", 
        "type": "PropertyValue", 
        "value": [
          "0361512"
        ]
      }, 
      {
        "name": "pubmed_id", 
        "type": "PropertyValue", 
        "value": [
          "30963384"
        ]
      }
    ], 
    "sameAs": [
      "https://doi.org/10.1007/s10439-019-02261-w", 
      "https://app.dimensions.ai/details/publication/pub.1113301116"
    ], 
    "sdDataset": "articles", 
    "sdDatePublished": "2019-04-15T08:53", 
    "sdLicense": "https://scigraph.springernature.com/explorer/license/", 
    "sdPublisher": {
      "name": "Springer Nature - SN SciGraph project", 
      "type": "Organization"
    }, 
    "sdSource": "s3://com-uberresearch-data-dimensions-target-20181106-alternative/cleanup/v134/2549eaecd7973599484d7c17b260dba0a4ecb94b/merge/v9/a6c9fde33151104705d4d7ff012ea9563521a3ce/jats-lookup/v90/0000000374_0000000374/records_119750_00000001.jsonl", 
    "type": "ScholarlyArticle", 
    "url": "http://link.springer.com/10.1007/s10439-019-02261-w"
  }
]
 

Download the RDF metadata as:  json-ld nt turtle xml License info

HOW TO GET THIS DATA PROGRAMMATICALLY:

JSON-LD is a popular format for linked data which is fully compatible with JSON.

curl -H 'Accept: application/ld+json' 'https://scigraph.springernature.com/pub.10.1007/s10439-019-02261-w'

N-Triples is a line-based linked data format ideal for batch operations.

curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1007/s10439-019-02261-w'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/s10439-019-02261-w'

RDF/XML is a standard XML format for linked data.

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/s10439-019-02261-w'


 

This table displays all metadata directly associated to this object as RDF triples.

201 TRIPLES      20 PREDICATES      53 URIs      16 LITERALS      6 BLANK NODES

Subject Predicate Object
1 sg:pub.10.1007/s10439-019-02261-w schema:about anzsrc-for:11
2 anzsrc-for:1102
3 schema:author N99576b8b5c01439da3d366ceb731e834
4 schema:citation sg:pub.10.1007/s10439-010-0067-6
5 sg:pub.10.1007/s10439-010-0094-3
6 sg:pub.10.1007/s10439-010-0165-5
7 sg:pub.10.1007/s10439-010-0175-3
8 sg:pub.10.1007/s10439-012-0691-4
9 sg:pub.10.1007/s10439-017-1837-1
10 sg:pub.10.1038/s41598-017-04699-1
11 sg:pub.10.1114/1.202
12 https://app.dimensions.ai/details/publication/pub.1078388662
13 https://app.dimensions.ai/details/publication/pub.1106889177
14 https://doi.org/10.1001/jama.287.22.2968
15 https://doi.org/10.1002/9781118033050
16 https://doi.org/10.1002/bjs.9578
17 https://doi.org/10.1016/j.ejvs.2008.09.007
18 https://doi.org/10.1016/j.ejvs.2009.09.026
19 https://doi.org/10.1016/j.jbiomech.2005.10.021
20 https://doi.org/10.1016/j.jmbbm.2015.07.029
21 https://doi.org/10.1016/j.jvs.2010.05.117
22 https://doi.org/10.1016/j.jvs.2010.12.053
23 https://doi.org/10.1016/j.jvs.2013.10.097
24 https://doi.org/10.1016/j.jvs.2015.11.051
25 https://doi.org/10.1016/s0021-9290(99)00201-8
26 https://doi.org/10.1067/mva.2002.125478
27 https://doi.org/10.1067/mva.2003.119
28 https://doi.org/10.1098/rsif.2015.0852
29 https://doi.org/10.1115/1.3127256
30 https://doi.org/10.1118/1.3284976
31 https://doi.org/10.1146/annurev.ps.46.020195.003021
32 https://doi.org/10.1177/0003319706290741
33 schema:datePublished 2019-04-08
34 schema:datePublishedReg 2019-04-08
35 schema:description Abdominal aortic aneurysm (AAA) is a vascular disease characterized by the enlargement of the infrarenal segment of the aorta. A ruptured AAA can cause internal bleeding and carries a high mortality rate, which is why the clinical management of the disease is focused on preventing aneurysm rupture. AAA rupture risk is estimated by the change in maximum diameter over time (i.e., growth rate) or if the diameter reaches a prescribed threshold. The latter is typically 5.5 cm in most clinical centers, at which time surgical intervention is recommended. While a size-based criterion is suitable for most patients who are diagnosed at an early stage of the disease, it is well known that some small AAA rupture or patients become symptomatic prior to a maximum diameter of 5.5 cm. Consequently, the mechanical stress in the aortic wall can also be used as an integral component of a biomechanics-based rupture risk assessment strategy. In this work, we seek to identify geometric characteristics that correlate strongly with wall stress using a sample space of 100 asymptomatic, unruptured, electively repaired AAA models. The segmentation of the clinical images, volume meshing, and quantification of up to 45 geometric measures of each AAA were done using in-house Matlab scripts. Finite element analysis was performed to compute the first principal stress distributions from which three global biomechanical parameters were calculated: peak wall stress, 99th percentile wall stress and spatially averaged wall stress. Following a feature reduction approach consisting of Pearson's correlation matrices with Bonferroni correction and linear regressions, a multivariate stepwise regression analysis was conducted to find the geometric measures most highly correlated with each of the biomechanical parameters. Our findings indicate that wall stress can be predicted by geometric indices with an accuracy of up to 94% when AAA models are generated with uniform wall thickness and up to 67% for patient specific, non-uniform wall thickness AAA. These geometric predictors of wall stress could be used in lieu of complex finite element models as part of a geometry-based protocol for rupture risk assessment.
36 schema:genre research_article
37 schema:inLanguage en
38 schema:isAccessibleForFree false
39 schema:isPartOf sg:journal.1087247
40 schema:name Wall Stress and Geometry Measures in Electively Repaired Abdominal Aortic Aneurysms.
41 schema:productId N2b43d4b049494d23921602142a482a86
42 Naf8ec484129c446797e3aef199831734
43 Nb178fd28cf5a4a69afb49b1356b139e2
44 Nf9bd7112c0a24a7ab18ce327453d8842
45 schema:sameAs https://app.dimensions.ai/details/publication/pub.1113301116
46 https://doi.org/10.1007/s10439-019-02261-w
47 schema:sdDatePublished 2019-04-15T08:53
48 schema:sdLicense https://scigraph.springernature.com/explorer/license/
49 schema:sdPublisher N4e43083f3fd74275b1910be47b6d8dcd
50 schema:url http://link.springer.com/10.1007/s10439-019-02261-w
51 sgo:license sg:explorer/license/
52 sgo:sdDataset articles
53 rdf:type schema:ScholarlyArticle
54 N1bd6d80d87f64f3ebd927e0e16817bab schema:affiliation https://www.grid.ac/institutes/grid.215352.2
55 schema:familyName Parikh
56 schema:givenName Shalin
57 rdf:type schema:Person
58 N1eb16c792dea46a3a2251b45e8766a10 schema:affiliation https://www.grid.ac/institutes/grid.215352.2
59 schema:familyName Gomez
60 schema:givenName Raymond
61 rdf:type schema:Person
62 N26e4cb5c12a34c1997f153bec64e74ab rdf:first N1eb16c792dea46a3a2251b45e8766a10
63 rdf:rest N6dbd438e63ac4815a51018a7749a47e9
64 N2b43d4b049494d23921602142a482a86 schema:name dimensions_id
65 schema:value pub.1113301116
66 rdf:type schema:PropertyValue
67 N3e75ed2978874739b4f8d9670f85bc8c schema:affiliation https://www.grid.ac/institutes/grid.215352.2
68 schema:familyName Rengarajan
69 schema:givenName Balaji
70 rdf:type schema:Person
71 N4e43083f3fd74275b1910be47b6d8dcd schema:name Springer Nature - SN SciGraph project
72 rdf:type schema:Organization
73 N6dbd438e63ac4815a51018a7749a47e9 rdf:first Nfbccf188b4b845ffabab1fb257ddc190
74 rdf:rest N785d8e5a9f13484aafdce53b86e663a7
75 N785d8e5a9f13484aafdce53b86e663a7 rdf:first Nbeee3b52727e493a922cfce7d48fbdc5
76 rdf:rest Nee752db19b1c4f348d360b70ac1ec807
77 N8dc2ad39e2564020b44ee69f38f6a19e rdf:first N1bd6d80d87f64f3ebd927e0e16817bab
78 rdf:rest N26e4cb5c12a34c1997f153bec64e74ab
79 N94766192443545e2a49d920b51029f04 schema:affiliation https://www.grid.ac/institutes/grid.215352.2
80 schema:familyName Thirugnanasambandam
81 schema:givenName Mirunalini
82 rdf:type schema:Person
83 N99576b8b5c01439da3d366ceb731e834 rdf:first Na5529e8c2bdc4938a8c570ed34199f7f
84 rdf:rest Nb213457415de4d4aad5e3fbbf9ded41d
85 Na5529e8c2bdc4938a8c570ed34199f7f schema:affiliation https://www.grid.ac/institutes/grid.215352.2
86 schema:familyName Wu
87 schema:givenName Wei
88 rdf:type schema:Person
89 Naf8ec484129c446797e3aef199831734 schema:name nlm_unique_id
90 schema:value 0361512
91 rdf:type schema:PropertyValue
92 Nb178fd28cf5a4a69afb49b1356b139e2 schema:name doi
93 schema:value 10.1007/s10439-019-02261-w
94 rdf:type schema:PropertyValue
95 Nb213457415de4d4aad5e3fbbf9ded41d rdf:first N3e75ed2978874739b4f8d9670f85bc8c
96 rdf:rest Ne1093a26fa8c47649d0dea42a76b6f53
97 Nbeee3b52727e493a922cfce7d48fbdc5 schema:affiliation https://www.grid.ac/institutes/grid.413621.3
98 schema:familyName Muluk
99 schema:givenName Satish C
100 rdf:type schema:Person
101 Ne1093a26fa8c47649d0dea42a76b6f53 rdf:first N94766192443545e2a49d920b51029f04
102 rdf:rest N8dc2ad39e2564020b44ee69f38f6a19e
103 Nee752db19b1c4f348d360b70ac1ec807 rdf:first Neeed72de5e3d4d87b7cd7e4a18fe80a6
104 rdf:rest rdf:nil
105 Neeed72de5e3d4d87b7cd7e4a18fe80a6 schema:affiliation https://www.grid.ac/institutes/grid.215352.2
106 schema:familyName Finol
107 schema:givenName Ender A
108 rdf:type schema:Person
109 Nf9bd7112c0a24a7ab18ce327453d8842 schema:name pubmed_id
110 schema:value 30963384
111 rdf:type schema:PropertyValue
112 Nfbccf188b4b845ffabab1fb257ddc190 schema:affiliation https://www.grid.ac/institutes/grid.215352.2
113 schema:familyName De Oliveira
114 schema:givenName Victor
115 rdf:type schema:Person
116 anzsrc-for:11 schema:inDefinedTermSet anzsrc-for:
117 schema:name Medical and Health Sciences
118 rdf:type schema:DefinedTerm
119 anzsrc-for:1102 schema:inDefinedTermSet anzsrc-for:
120 schema:name Cardiorespiratory Medicine and Haematology
121 rdf:type schema:DefinedTerm
122 sg:grant.3933415 http://pending.schema.org/fundedItem sg:pub.10.1007/s10439-019-02261-w
123 rdf:type schema:MonetaryGrant
124 sg:journal.1087247 schema:issn 0145-3068
125 1573-9686
126 schema:name Annals of Biomedical Engineering
127 rdf:type schema:Periodical
128 sg:pub.10.1007/s10439-010-0067-6 schema:sameAs https://app.dimensions.ai/details/publication/pub.1032495765
129 https://doi.org/10.1007/s10439-010-0067-6
130 rdf:type schema:CreativeWork
131 sg:pub.10.1007/s10439-010-0094-3 schema:sameAs https://app.dimensions.ai/details/publication/pub.1045969195
132 https://doi.org/10.1007/s10439-010-0094-3
133 rdf:type schema:CreativeWork
134 sg:pub.10.1007/s10439-010-0165-5 schema:sameAs https://app.dimensions.ai/details/publication/pub.1031967822
135 https://doi.org/10.1007/s10439-010-0165-5
136 rdf:type schema:CreativeWork
137 sg:pub.10.1007/s10439-010-0175-3 schema:sameAs https://app.dimensions.ai/details/publication/pub.1016650194
138 https://doi.org/10.1007/s10439-010-0175-3
139 rdf:type schema:CreativeWork
140 sg:pub.10.1007/s10439-012-0691-4 schema:sameAs https://app.dimensions.ai/details/publication/pub.1018683007
141 https://doi.org/10.1007/s10439-012-0691-4
142 rdf:type schema:CreativeWork
143 sg:pub.10.1007/s10439-017-1837-1 schema:sameAs https://app.dimensions.ai/details/publication/pub.1085068265
144 https://doi.org/10.1007/s10439-017-1837-1
145 rdf:type schema:CreativeWork
146 sg:pub.10.1038/s41598-017-04699-1 schema:sameAs https://app.dimensions.ai/details/publication/pub.1090349985
147 https://doi.org/10.1038/s41598-017-04699-1
148 rdf:type schema:CreativeWork
149 sg:pub.10.1114/1.202 schema:sameAs https://app.dimensions.ai/details/publication/pub.1031437224
150 https://doi.org/10.1114/1.202
151 rdf:type schema:CreativeWork
152 https://app.dimensions.ai/details/publication/pub.1078388662 schema:CreativeWork
153 https://app.dimensions.ai/details/publication/pub.1106889177 schema:CreativeWork
154 https://doi.org/10.1001/jama.287.22.2968 schema:sameAs https://app.dimensions.ai/details/publication/pub.1025353701
155 rdf:type schema:CreativeWork
156 https://doi.org/10.1002/9781118033050 schema:sameAs https://app.dimensions.ai/details/publication/pub.1106889177
157 rdf:type schema:CreativeWork
158 https://doi.org/10.1002/bjs.9578 schema:sameAs https://app.dimensions.ai/details/publication/pub.1043859248
159 rdf:type schema:CreativeWork
160 https://doi.org/10.1016/j.ejvs.2008.09.007 schema:sameAs https://app.dimensions.ai/details/publication/pub.1026149368
161 rdf:type schema:CreativeWork
162 https://doi.org/10.1016/j.ejvs.2009.09.026 schema:sameAs https://app.dimensions.ai/details/publication/pub.1044312471
163 rdf:type schema:CreativeWork
164 https://doi.org/10.1016/j.jbiomech.2005.10.021 schema:sameAs https://app.dimensions.ai/details/publication/pub.1051593106
165 rdf:type schema:CreativeWork
166 https://doi.org/10.1016/j.jmbbm.2015.07.029 schema:sameAs https://app.dimensions.ai/details/publication/pub.1012948177
167 rdf:type schema:CreativeWork
168 https://doi.org/10.1016/j.jvs.2010.05.117 schema:sameAs https://app.dimensions.ai/details/publication/pub.1011349505
169 rdf:type schema:CreativeWork
170 https://doi.org/10.1016/j.jvs.2010.12.053 schema:sameAs https://app.dimensions.ai/details/publication/pub.1030640456
171 rdf:type schema:CreativeWork
172 https://doi.org/10.1016/j.jvs.2013.10.097 schema:sameAs https://app.dimensions.ai/details/publication/pub.1042037849
173 rdf:type schema:CreativeWork
174 https://doi.org/10.1016/j.jvs.2015.11.051 schema:sameAs https://app.dimensions.ai/details/publication/pub.1018088724
175 rdf:type schema:CreativeWork
176 https://doi.org/10.1016/s0021-9290(99)00201-8 schema:sameAs https://app.dimensions.ai/details/publication/pub.1021842866
177 rdf:type schema:CreativeWork
178 https://doi.org/10.1067/mva.2002.125478 schema:sameAs https://app.dimensions.ai/details/publication/pub.1049778061
179 rdf:type schema:CreativeWork
180 https://doi.org/10.1067/mva.2003.119 schema:sameAs https://app.dimensions.ai/details/publication/pub.1007965623
181 rdf:type schema:CreativeWork
182 https://doi.org/10.1098/rsif.2015.0852 schema:sameAs https://app.dimensions.ai/details/publication/pub.1040393798
183 rdf:type schema:CreativeWork
184 https://doi.org/10.1115/1.3127256 schema:sameAs https://app.dimensions.ai/details/publication/pub.1062102280
185 rdf:type schema:CreativeWork
186 https://doi.org/10.1118/1.3284976 schema:sameAs https://app.dimensions.ai/details/publication/pub.1044922678
187 rdf:type schema:CreativeWork
188 https://doi.org/10.1146/annurev.ps.46.020195.003021 schema:sameAs https://app.dimensions.ai/details/publication/pub.1041487701
189 rdf:type schema:CreativeWork
190 https://doi.org/10.1177/0003319706290741 schema:sameAs https://app.dimensions.ai/details/publication/pub.1045559008
191 rdf:type schema:CreativeWork
192 https://www.grid.ac/institutes/grid.215352.2 schema:alternateName The University of Texas at San Antonio
193 schema:name Department of Management Science and Statistics, University of Texas at San Antonio, One UTSA Circle, San Antonio, TX, 78249, USA.
194 Department of Mechanical Engineering, University of Texas at San Antonio, One UTSA Circle, San Antonio, TX, 78249, USA.
195 Department of Mechanical Engineering, University of Texas at San Antonio, One UTSA Circle, San Antonio, TX, 78249, USA. ender.finol@utsa.edu.
196 UTSA/UTHSA Joint Graduate Program in Biomedical Engineering, University of Texas at San Antonio, One UTSA Circle, San Antonio, TX, 78249, USA.
197 UTSA/UTHSA Joint Graduate Program in Biomedical Engineering, University of Texas at San Antonio, One UTSA Circle, San Antonio, TX, 78249, USA. ender.finol@utsa.edu.
198 rdf:type schema:Organization
199 https://www.grid.ac/institutes/grid.413621.3 schema:alternateName Allegheny General Hospital
200 schema:name Department of Thoracic & Cardiovascular Surgery, Allegheny Health Network, Allegheny General Hospital, 320 E. North Ave., Pittsburgh, PA, 15212, USA.
201 rdf:type schema:Organization
 




Preview window. Press ESC to close (or click here)


...