Left Ventricular Distribution Patterns of the Regionally varying Ischemic Myocardial Contractile Substrates Associated with Ischemic Mitral Regurgitation

项目摘要-摘要 限制性腱索栓系导致二尖瓣瓣叶接合表面积损失， 心肌壁节段是缺血性二尖瓣返流（MR）的公认机制。准确 表征左心室（LV）的分布模式、幅度和收缩的可逆性， 损伤基质易导致缺血性MR的发生，可能会提高治疗的准确性， 干预直到最近，高分辨率LV区域收缩指标才在临床上可用， 在患者特定LV几何结构中绘制心肌缺血基质（冬眠、梗死）。的应用 在我们的缺血性MR研究小组中，基于MRI的多参数应变分析表明，高分辨率 LV收缩损伤的3D地形图可能揭示了一系列更复杂的相关区域性损伤。 收缩性损伤比超声心动图可辨别的要多。这项初步研究确定了一个“前哨”LV区域， （左心室后部和后外侧区域的基底和中部亚区），其中存在严重的 收缩性损伤明显易导致缺血性MR的发展。 我们将入组有（≥3+ MR; n=90）和无（≤1+ MR; n=90）的缺血性冠状动脉疾病患者 计划进行标准化手术的缺血性MR患者（ACC/AHA临床指南）。术前MRI- 基于多参数应变分析将提供高分辨率的三维LV地形图的区域 收缩性损伤与缺血性MR的发生和术后研究在统计学上相关， 3-月和年。一个独立的核心实验室将对所有基于超声心动图的指标进行分类， 缺血性MR与所有其他识别的临床变量一起沿着纳入支持向量机分析。 使用导航门控螺旋，在30分钟内获得基于MRI的LV位移数据集 刺激回波位移编码（DENSE）。使用以下公式计算患者特定LV应变场 径向点插值法（Radial Point Interpolation Method，RPIM）区域收缩功能“正常化” 通过将多个患者特异性应变度量值（在11，520个LV网格点中的每一个处）与它们各自的 来自我们的正常人菌株数据库的平均+/- SD值，用z-评分（SD）计算（全计算机 分析时间<20秒）。支持向量机分析将搜索所有度量变量（多参数 应变、基于回波的度量和所有临床变量）来预测缺血性MR复发的模式。 我们将使用“标准化”LV收缩功能的高分辨率3D地形图， 表征局部收缩性损伤基质的分布、大小和可逆性 （冬眠;梗死）与缺血性MR相关。然后，我们将测试新的应用程序的假设， 支持向量机分析可以识别两个区域的混合组合 准确预测缺血性MR修复后复发的收缩性损伤模式和临床变量。

PROJECT SUMMARY - ABSTRACT The loss of mitral leaflet coaptation surface area caused by restrictive chordal tethering to dysfunctional myocardial wall segments is the well-recognized mechanism of ischemic mitral regurgitation (MR). An accurate characterization of the left ventricular (LV) distribution pattern, magnitude, and reversibility of the contractile injury substrates that predispose to the occurrence of ischemic MR may improve the accuracy of therapeutic intervention. Only recently have high-resolution LV regional contractile metrics become clinically available to map myocardial ischemic substrates (hibernating, infarcted) across patient-specific LV geometry. Application of MRI-based multiparametric strain analysis in our pilot ischemic MR study group suggested that high-resolution 3D topographical mapping of LV contractile injury may reveal a more complex array of associated regional contractile injury than is discernible from echocardiography. This initial study identified a “sentinel” LV region (basilar and mid subregions of the posterior and posterolateral LV regions) in which the presence of severe contractile injury clearly predisposes to the development of ischemic MR. We will enroll ischemic coronary artery disease patients with (≥3+ MR; n=90) and without (≤1+ MR; n=90) ischemic MR who are scheduled for standardized surgery (ACC/AHA Clinical Guidelines). Preoperative MRI- based multiparametric strain analysis will provide high-resolution 3D LV topographical maps of regional contractile injury to statistically correlate to occurrence of ischemic MR and to postoperative studies obtained at 3-months and yearly. An independent core laboratory will catalogue all echocardiography-based metrics of ischemic MR for inclusion in Support Vector Machine analyses, along with all other identified clinical variables. MRI-based LV displacement datasets are obtained in <30 minutes using Navigator-gated Spiral Displacement ENcoding with Stimulated Echoes (DENSE). Patient-specific LV strain fields are calculated using the recently developed Radial Point Interpolation Method (RPIM). Regional contractile function is “normalized” by comparing multiple patient-specific strain metric values (at each of 11,520 LV grid points) to their respective average +/- SD values from our normal human strain database, with z- score (SD) calculation (total computer analysis <20 seconds). Support Vector Machine analyses will search all metric variables (multiparametric strain, echo-based metrics, and all clinical variables) for patterns that predict ischemic MR recurrence. We will use high-resolution 3D topographical mapping of “normalized” LV contractile function to characterize the distribution, magnitude, and reversibility of the regional contractile injury substrates (hibernating; infarcted) associated with ischemic MR. We will then test the hypothesis that the novel application of machine learning Support Vector Machine analyses can identify hybrid combinations of both regional contractile injury patterns and clinical variables that accurately predict post-repair recurrence of ischemic MR.