A robust approach for exploring hemodynamics and thrombus growth associations in abdominal aortic aneurysms.

Longitudinal studies of vascular diseases often need to establish correspondence between follow-up images, as the diseased regions may change shape over time. In addition, spatial data structures should be taken into account in the statistical analyses to avoid inferential errors. This study investi...

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Publicado en:Medical & Biological Engineering & Computing Vol. 55; no. 8; pp. 1493 - 1507
Autores principales: Tzirakis, Konstantinos, Kamarianakis, Yiannis, Metaxa, Eleni, Kontopodis, Nikolaos, Ioannou, Christos, Papaharilaou, Yannis, Ioannou, Christos V
Formato: case study diagnostic images equations & formulas research tables/charts Journal Article
Publicado: Springer Nature Aug2017
Acceso en línea:Ver este registro en EBSCOhost
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          Tzirakis, Konstantinos
          Kamarianakis, Yiannis
          Metaxa, Eleni
          Kontopodis, Nikolaos
          Ioannou, Christos
          Papaharilaou, Yannis
          Ioannou, Christos V
        affil: Institute of Applied and Computational Mathematics , Foundation for Research and Technology , 100 Nikolaou Plastira str, Vassilika Vouton 700 13 Heraklion Greece
      sug:
        subj:
          Thrombosis Physiopathology
          Aorta, Abdominal Pathology
          Thrombosis Pathology
          Models, Biological
          Aortic Aneurysm, Abdominal Pathology
          Aortic Aneurysm, Abdominal Physiopathology
          Aorta, Abdominal Physiopathology
          Stress, Mechanical
          Thrombosis
          Disease Progression
          Blood Flow Velocity
          Aortic Aneurysm, Abdominal
          Aged, 80 and Over
          Hemodynamics
          Biomechanics
          Computer Simulation
          Aorta, Abdominal
          Aged
          Blood Pressure
          Human
          Aged, 80 & over
          Aged: 65+ years
      ab: Longitudinal studies of vascular diseases often need to establish correspondence between follow-up images, as the diseased regions may change shape over time. In addition, spatial data structures should be taken into account in the statistical analyses to avoid inferential errors. This study investigates the association between hemodynamics and thrombus growth in abdominal aortic aneurysms (AAAs) while emphasizing on the abovementioned methodological issues. Six AAA surfaces and their follow-ups were three-dimensionally reconstructed from computed-tomography images. AAA surfaces were mapped onto a rectangular grid which allowed identification of corresponding regions between follow-ups. Local thrombus thickness was measured at initial and follow-up surfaces and computational fluid dynamic simulations provided time-average wall shear stress (TAWSS), oscillatory shear index (OSI), and relative residence time. Six Bayesian regression models, which account for spatially correlated measurements, were employed to explore associations between hemodynamics and thrombus growth. Results suggest that spatial regression models based on TAWSS and OSI offer superior predictive performance for thrombus growth relative to alternative specifications. Ignoring the spatial data structure may lead to improper assessment with regard to predictor significance.
      pubtype: Academic Journal
      doctype:
        case study
        diagnostic images
        equations & formulas
        research
        tables/charts
        Journal Article
      ougenre: Article
    language: English
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