Data-Driven Approaches to Identify Biomarkers for Guiding Coronary Artery Bifurcation Lesion Interventions from Patient-Specific Hemodynamic Models

摘要 冠状动脉疾病(CAD)在美国非常流行，2017年导致超过36万人死亡 独自一人。冠心病是由斑块引起的(又名病变)沿冠状动脉壁堆积，限制 血腥fl哦。在20%的病例中，这些损害发生在动脉分叉处。冠状动脉分叉部病变的治疗 由于其支架置入术有较高的心脏不良事件风险，例如 支架内再狭窄、支架血栓形成、心肌梗死或需要复发的经皮冠状动脉介入治疗 发明(冠状动脉介入治疗)。对于单支血管病变(不在分叉处)，血流储备分数与血管造影相比 多支血管评估(FAME)试验在建立生物标记物(分数flow Reserve，FFR)中发挥了关键作用 指导和改进他们的治疗。然而，迫切需要一种典型的fi阳离子方案来评估 分叉处病变的生理严重程度和缺血负荷，特别是在主干后的侧支 分支机构干预。在这一知识鸿沟被纠正之前，分叉病变的患者将继续 与单支主干病变相比，fi的远期心脏并发症发生率明显更高。 目前基于FFR的用于治疗较简单的主干病变的PCI协议不能转化为有效的 更复杂的分叉病变的治疗方案。在提取相似指标方面的差异是由于在fi中- 病变几何形状的复杂性增加(通常由两个不同的病变组成，一个在主支和 一个在侧枝中)，并且在潜在的患者解剖的fl方面更强。虽然众所周知，款待- 主干病变可改善预后，对侧支病变何时治疗缺乏明确指导 布兰奇。我们的长期目标是建立一个基于病变和患者特异性fic的多层次Classifi阳离子系统 可用于更精确地指导治疗决策，并最终降低高 分叉部病变患者的不良并发症发生率。我们的中心假设是，描述 分叉病变解剖学可用于对缺血负荷进行分类，进而指导支架置入决策。 通过使用系统的、经过验证的计算模型，我们现在可以准确地确定控制- 各解剖特征与生理严重度的关系。我们现在拥有计算能力、经过验证的工具和 机器学习的成熟度需要进行大规模的研究，在计算机研究中不仅要隔离fl的需求 单个特征，但特征集之间的基本关系。这项提议的主要目标是 通过识别病变特定的fic特征，实现对分叉支架手术的个性化指导 这在fl患者的功能严重程度以及可能加重负担的患者特有的fic生物标志物中也是如此。

ABSTRACT Coronary artery disease (CAD) is highly prevalent in the US, causing more than 360,000 deaths in 2017 alone. CAD is caused by plaques (a.k.a. lesions) that build up along the walls of coronary arteries, restricting blood flow. In 20% of cases, these lesions occur at arterial bifurcations. Treatment of coronary bifurcation le- sions remains particularly challenging, as their stenting carries a higher risk for adverse cardiac events such as in-stent restenosis, stent thrombosis, myocardial infarction, or need for recurrent percutaneous coronary inter- vention (PCI). For single vessel lesions (not at bifurcations), the Fractional Flow Reserve Versus Angiography for Multivessel Evaluation (FAME) trial played a critical role in establishing a biomarker (fractional flow reserve, FFR) to guide and improve their treatment. However, there is an urgent need for a classification scheme to assess physiological severity and ischemic burden of lesions at bifurcations, particularly in the side branches after main branch intervention. Until this knowledge gap is corrected, patients with bifurcation lesions will continue to have a significantly higher rate of long-term cardiac complications compared to those with single, main branch lesions. Current PCI protocols based on FFR for treating simpler main branch lesions do not translate into effective protocols for more complicated bifurcation lesions. The difficulty in extracting similar metrics is due to the in- creased complexity of the lesion geometry (typically consisting of two distinct lesions, one in the main branch and one in the side branch) and stronger influence of the underlying patient anatomy. While it is known that treat- ing the main branch lesion can improve the outcome, clear guidance is lacking regarding when to treat the side branch. Our long-term goal is to establish a multi-level classification system based on lesion- and patient-specific features that can be used to guide treatment decisions with better precision, and ultimately to reduce the high rate of adverse complications in patients with bifurcation lesions. Our central hypothesis is that criteria describing bifurcation lesion anatomy can be identified to classify ischemic burden and, in turn, guide stenting decisions. Through the use of a systematic, validated computational model, we can now accurately determine the contri- bution of each anatomic feature to physiologic severity. We now have the computing power, validated tools, and machine learning maturity required to undertake a large-scale, in silico study to isolate not only the influence of individual features, but underlying relationships between sets of features. The major objective of this proposal is to enable personalized guidance of bifurcation stenting procedures by identifying both the lesion-specific features that influence functional severity as well as the patient-specific biomarkers that may exacerbate burden.