Individualized Rupture Risk Assessment and Analysis of Mechanobiological Interactions in Abdominal Aortic Aneurysms

腹主动脉直径增加> 30 mm称为腹主动脉瘤（AAA），其中危及生命的破裂风险随直径增加而增加。因此，既定指南建议对45- 55 mm的AAA进行介入治疗。在这种情况下，必须考虑到只有1/3的AAA > 50 mm实际破裂，而破裂也可能出现在更小的AAA中。因此，精确、可靠和患者特异性的破裂风险预测是非常有意义的。临床破裂风险分层主要基于AAA直径标准，该标准不是很具体。通常不考虑潜在高度相关的参数，例如形态、壁厚、血栓、钙化和主动脉壁强度。在该项目的第一阶段，我们开发了一个模型，用于个性化的破裂风险预测。它基于一个计算模型，该模型考虑了基于我们患者队列的多线性回归模型的患者特定几何形状和材料特性。创建了包含80名患者和221份组织样本的数据库。对组织样本进行机械、组织学和组织化学表征。一种机械生物学替代模型已经被开发出来，它可以预测AAA的特性，而这些特性不能通过非侵入性方法进行评估。这些信息进入数值模型计算壁应力，与来自组织样本测试的壁强度相比，产生破裂风险量化。研究表明，新的风险分层可能优于临床确定的风险分层上级。在本项目期间，我们将通过重要的附加方面来增强破裂风险模型，即AAA的时间进展和模型预测质量的严格量化。此外，我们增加了数据库的大小，并通过额外的血清和样本，组织学和免疫组织学的结果。将在原代腹主动脉平滑肌细胞中进行体外实验，以鉴定人AAA壁中机械转导的功能关系。我们开发了一种新的机械生物学生长和重塑（G&R）材料法，不仅考虑了机械状态，还考虑了AAA壁变性的生物学方面。将通过基于纵向医学图像数据集的逆分析程序来校准G&R模型参数。高斯过程回归将应用于生物和机械参数，以识别统计学上显著的替代模型参数，以纳入G&R材料配方和壁强度预测。同时，高斯过程回归也将通过预测G&R和材料性能值的置信区间来评估预测能力。将在一个小型患者队列中对最终破裂风险分层模型进行回顾性和前瞻性评价。

A diameter increase of the abdominal aorta of >30mm is called an abdominal aortic aneurysm (AAA), where risk of a life threateningrupture increases with diameter. Established guidelines therefore recommend interventional therapy for AAA with 45-55mm. In thiscontext, it has to be considered that only 1/3 of AAA >50mm actually rupture, while rupture might also appear in much smaller AAA.Therefore, a refined, reliable and patient specific rupture risk prediction is of great interest. Clinically rupture risk stratification ismainly based on a AAA diameter criterion, which is not very specific. Consideration of potentially highly relevant parameters as e.g.morphology, wall thickness, thrombus, calcifications and aortic wall strength usually is not included. In the first period of this project, we developed a model for individualized rupture risk prediction. It is based on a computational model that considers patient specificgeometries and material properties from multilinear regression models based on our patient cohort. A data base of 80 patients and 221 tissue samples was created. Tissue samples were characterized mechanically, histologically and immunohistochemically. Amechanobiological surrogate model has been developed that predicts AAA properties that can not be assessed non-invasively. Theinformation enters the numerical models to compute wall stresses, which, compared to wall strength derived from tissue sample testing, yields a rupture risk quantification. It has been shown that the novel risk stratification can be superior to the clinically established. In this project period, we will enhance the rupture risk model by important additional aspects, namely the AAA progression in time and a rigorous quantification of prediction quality of the model. Additionally, we increase the data base in size and by additional serum and sample based, histological and immunohistological findings. In vitro experiments will be performed in primary aortal smooth muscle cells to identify functional relationships of mechanotransduction in human AAA wall. We develop a novel mechanobiological growth & remodeling (G&R) material law that not only considers mechanical state but also considers biological aspects of AAA wall degeneration. The G&R model parameters will be calibrated through an inverse analysis procedure based on longitudinal medical image data sets. Gaussian process regression will be applied to the biological and mechanical parameters to identify statistically significant surrogate model parameters to incorporate in the G&R material formulation andin wall strength prediction. Simultaneously, Gaussian process regression will also serve for the assessment of the predictivecapabilities by means of confidence intervals for the predicted G&R and material properties values. The final rupture risk stratification model will be evaluated retrospectively and prospectively on a small patient cohort.