title: Prediction of Atherosclerotic Plaque Development in an In Vivo Coronary Arterial Segment Based on a Multilevel Modeling Approach.
abstract: The aim of this study is to explore major mechanisms of atherosclerotic plaque growth, presenting a proof-of-concept numerical model.
  
  
  
  To this aim, a human reconstructed left circumflex coronary artery is utilized for a multilevel modeling approach. More specifically, the first level consists of the modeling of blood flow and endothelial shear stress (ESS) computation. The second level includes the modeling of low-density lipoprotein (LDL) and high-density lipoprotein and monocytes transport through the endothelial membrane to vessel wall. The third level comprises of the modeling of LDL oxidation, macrophages differentiation, and foam cells formation. All modeling levels integrate experimental findings to describe the major mechanisms that occur in the arterial physiology. In order to validate the proposed approach, we utilize a patient specific scenario by comparing the baseline computational results with the changes in arterial wall thickness, lumen diameter, and plaque components using follow-up data.
  
  
  
  The results of this model show that ESS and LDL concentration have a good correlation with the changes in plaque area [R2= 0.365 (P = 0.029, adjusted R2= 0.307) and R2= 0.368 (P = 0.015, adjusted R2= 0.342), respectively], whereas the introduction of the variables of oxidized LDL, macrophages, and foam cells as independent predictors improves the accuracy in predicting regions potential for atherosclerotic plaque development [R2= 0.847 (P = 0.009, adjusted R2= 0.738)].
  
  
  
  Advanced computational models can be used to increase the accuracy to predict regions which are prone to plaque development.
  
  
  
  Atherosclerosis is one of leading causes of death worldwide. For this purpose computational models have to be implemented to predict disease progression.
authors.forename: Antonis I
authors.initials: AI
authors.lastname: Sakellarios
authors.forename: Lorenz
authors.initials: L
authors.lastname: Raber
authors.forename: Christos V
authors.initials: CV
authors.lastname: Bourantas
authors.forename: Themis P
authors.initials: TP
authors.lastname: Exarchos
authors.forename: Lambros S
authors.initials: LS
authors.lastname: Athanasiou
authors.forename: Gualtiero
authors.initials: G
authors.lastname: Pelosi
authors.forename: Konstantinos C
authors.initials: KC
authors.lastname: Koskinas
authors.forename: Oberdan
authors.initials: O
authors.lastname: Parodi
authors.forename: Katerina K
authors.initials: KK
authors.lastname: Naka
authors.forename: Lampros K
authors.initials: LK
authors.lastname: Michalis
authors.forename: Patrick W
authors.initials: PW
authors.lastname: Serruys
authors.forename: Hector M
authors.initials: HM
authors.lastname: Garcia-Garcia
authors.forename: Stephan
authors.initials: S
authors.lastname: Windecker
authors.forename: Dimitrios I
authors.initials: DI
authors.lastname: Fotiadis
mesh_terms: D001783:Blood Flow Velocity; D001794:Blood Pressure; D003198:Computer Simulation; D003324:Coronary Artery Disease / Q000503:physiopathology*; D003331:Coronary Vessels / Q000503:physiopathology*; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D004730:Endothelium, Vascular / Q000503:physiopathology*; D006801:Humans; D008077:Lipoproteins, LDL / Q000097:blood; D008955:Models, Cardiovascular; D009000:Monocytes / Q000378:metabolism; D058226:Plaque, Atherosclerotic / Q000503:physiopathology*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D033081:Shear Strength
chemical_list: D008077:Lipoproteins, LDL