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

项目摘要-摘要 限制性脊索拴系致功能障碍的二尖瓣叶结合表面积的损失 心肌壁节段是公认的缺血性二尖瓣返流(MR)机制。一个准确的 左心室(LV)分布模式、收缩幅度和可逆性的特征 易发生缺血性磁共振的损伤底物可提高治疗的准确性 干预。直到最近，高分辨率的LV局部收缩指标才开始临床应用 通过患者特定的左室构型绘制心肌缺血底物(冬眠、梗死)图。应用程序 我们的先导性缺血MR研究组基于MRI的多参数应变分析表明，高分辨率 左心室收缩损伤的3D地形图可能揭示更复杂的相关区域阵列 收缩损伤比超声心动图所能辨别的更明显。这项初步研究确定了一个“前哨”的LV区域 (LV后区和后外侧区的基底区和中部亚区)，其中严重的 收缩损伤明显易导致缺血性MR的发生。 我们将纳入有(≥3+MR；n=90)和无(≤1+MR；n=90)的缺血性冠心病患者。 计划进行标准化手术的缺血性MR患者(ACC/AHA临床指南)。术前核磁共振检查- 基于多参数的应变分析将提供高分辨率的区域三维LV地形图 收缩损伤与缺血性MR的发生和术后研究在统计学上相关 3个月和每年。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 包括在支持向量机分析中的缺血性磁共振，以及所有其他已识别的临床变量。 使用导航器门控螺旋在&lt；30分钟内获得基于MRI的左心室位移数据集 带有刺激回波的位移编码(密集)。使用以下公式计算患者特定的LV应变场 最近发展起来的径向点插值法(RPIM)。区域收缩功能“正常化” 通过将多个患者特定的应变度量值(在11,520个LV网格点的每一个处)与其各自的 来自我们的正常人类应变数据库的平均+/-SD值，带有z-Score(SD)计算(总计算机 分析&lt；20秒。支持向量机分析将搜索所有度量变量(多参数 应变、基于回声的指标和所有临床变量)，用于预测缺血性MR复发的模式。 我们将使用高分辨率的3D地形图来测量LV收缩功能 描述局部收缩损伤底物的分布、大小和可逆性 (冬眠的；梗死的)与缺血有关的先生然后我们将检验这一新应用的假设 机器学习支持向量机分析可以识别两个区域的混合组合 可准确预测缺血性MR修复后复发的收缩损伤类型和临床变量

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.