Risk stratification of uncomplicated type B aortic dissection using clinical and engineering analysis

使用临床和工程分析对简单的 B 型主动脉夹层进行风险分层

• 批准号: 10673753
• 负责人: Bradley Graham Leshnower
• 金额: $ 54.8万
• 依托单位: EMORY UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-09-20 至 2025-07-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10673753
• 关键词: 4D MRI; Acute; Alternative Therapies; Anatomy; Aneurysm; Antihypertensive Agents; Aorta; Aortic Aneurysm; Aortic Rupture; Biomechanics; Chest; Chronic Phase; Classification; Clinical; Computerized Medical Record; Data; Databases; Development; Diagnosis; Disease; Dissection; Early Intervention; Echocardiography; Engineering; Failure; Follow-Up Studies; Goals; Growth; Harvest; Hospital Mortality; Image; Incidence; Intervention; Liquid substance; Machine Learning; Magnetic Resonance Imaging; Maps; Mechanics; Medical; Medical Imaging; Modeling; Morbidity - disease rate; Operative Surgical Procedures; Organ; Outcome; Patients; Performance; Phase; Predictive Factor; Procedures; Property; Risk; Risk Factors; Rupture; Secondary to; Series; Shapes; Stress; Structure; Survival Rate; Techniques; Tissues; Treatment Failure; clinical risk; data repository; demographics; experimental study; hemodynamics; high risk; imaging study; improved; indexing; machine learning model; mortality; predictive modeling; prospective; recruit; repaired; risk stratification; serial imaging; surveillance imaging

Project Summary Type B Aortic Dissection (TBAD) is a lethal disease which occurs when a tear develops in the inner lining (intimal layer) of the aorta, causing the layers of the aortic wall to separate (dissect) creating “true” and “false” lumens. Complicated TBADs with presence of either organ malperfusion or aortic rupture have a high in-hospital mortality rate and require emergent surgical or endovascular therapy. Uncomplicated TBADs have been traditionally managed with optimal medical therapy (OMT) consisting of aggressive anti-hypertensive therapy and surveillance imaging. OMT results in low in-hospital mortality rates, but dismal long-term survival rates of 48- 66%, and overall intervention-free survival rates of less than 50% secondary to aortic aneurysm formation and rupture. These poor long-term outcomes support a paradigm change in the treatment of the uncomplicated TBADs. Thus, there is an urgent and unmet clinical need for promptly identifying those uncomplicated TBAD patients that will likely fail OMT in the acute phase, and thus benefit from early intervention such as Thoracic Endovascular Aortic Repair (TEVAR). Therefore, the objective of this project is to develop a risk stratification model for predicting both failure of OMT and the optimal timing of intervention in uncomplicated TBAD patients. To achieve this goal, a retrospective analysis will be conducted for about 500 uncomplicated TBAD patients from the Emory Aortic Databank. Clinical and anatomic data will be harvested from the electronic medical record and image studies to identify predictors of OMT failure. Next, using the same patient database, a series of mechanical experiments will be performed to obtain hyperelastic and failure properties of the TBAD tissues, from which rupture/tear risk metrics will be developed. Fluid-structure interaction (FSI) analyses will be validated and applied to obtain “heat maps” of hemodynamic and wall stress fields. The risk indices will be consequently extracted. For patients with longitudinal imaging data, TBAD progression will be predicted using an integrated growth and remodeling (G&R) and dissection propagation model. Critical biomechanical parameters will be identified as potential predictors of OMT failure. Finally, machine learning (ML) techniques will be used to combine clinical and biomechanical predictors to develop a multi-factorial, personalized TBAD risk stratification model. To evaluate the performance of the proposed approach, we will recruit and perform a longitudinal follow-up study of 35 acute uncomplicated TBAD patients to validate our approach by comparing the ML-model- prediction results with actual clinical outcomes.

项目概要 B 型主动脉夹层 (TBAD) 是一种致命疾病，当主动脉撕裂时就会发生这种疾病。 主动脉内壁（内膜层），导致主动脉壁各层分离（解剖） 创建“真”和“假”流明。任一器官存在的复杂 TBAD 灌注不良或主动脉破裂的院内死亡率很高，需要紧急手术 或血管内治疗。传统上，不复杂的 TBAD 均采用最佳管理方式进行管理 药物治疗 (OMT)，包括积极的抗高血压治疗和监测 成像。 OMT 导致院内死亡率较低，但长期生存率却很低 48- 66%，继发主动脉瘤的总体无干预生存率低于 50% 形成和破裂。这些不良的长期结果支持了范式的改变 治疗不复杂的 TBAD。因此，存在迫切且未满足的临床需求 及时识别那些在急性期可能无法进行 OMT 的无并发症 TBAD 患者， 从而受益于胸主动脉腔内修复术 (TEVAR) 等早期干预。 因此，该项目的目标是开发一个风险分层模型来预测 OMT 失败和无并发症 TBAD 患者干预的最佳时机。到 为实现这一目标，将对约500个不复杂的TBAD进行回顾性分析 来自埃默里主动脉数据库的患者。临床和解剖数据将从 电子病历和图像研究以确定 OMT 失败的预测因素。接下来，使用 相同的患者数据库，将进行一系列机械实验以获得 TBAD 组织的超弹性和失效特性，从中可以得出破裂/撕裂风险指标 得到开发。流固耦合 (FSI) 分析将得到验证并应用以获得 血流动力学和壁应力场的“热图”。风险指数将因此 提取的。对于有纵向影像数据的患者，将使用以下方法预测 TBAD 进展 集成的生长和重塑（G＆R）和解剖传播模型。批判的 生物力学参数将被确定为 OMT 失败的潜在预测因素。最后， 机器学习（ML）技术将用于结合临床和生物力学预测因子 开发多因素、个性化的 TBAD 风险分层模型。评估 拟议方法的表现，我们将招募并进行纵向后续研究 对 35 名急性无并发症 TBAD 患者进行研究，通过比较 ML 模型来验证我们的方法 预测结果与实际临床结果。

项目成果

The role of anatomic shape features in the prognosis of uncomplicated type B aortic dissection initially treated with optimal medical therapy.

解剖形状特征在最初采用最佳药物治疗的简单 B 型主动脉夹层预后中的作用。

• DOI: 10.1016/j.compbiomed.2024.108041
• 发表时间: 2024
• 期刊: Computers in biology and medicine
• 影响因子: 7.7
• 作者: Liu,Minliang;Dong,Hai;Mazlout,Adam;Wu,Yuxuan;Kalyanasundaram,Asanish;Oshinski,JohnN;Sun,Wei;Elefteriades,JohnA;Leshnower,BradleyG;GleasonJr,RudolphL
• 通讯作者: GleasonJr,RudolphL