James T. Willerson Center for Cardiovascular Modeling and Simulation, Oden Institute for Computational Engineering and Sciences, Department of Biomedical Engineering, The University of Texas at Austin, Austin, TX, USA.
Gorman Cardiovascular Research Group, Smilow Center for Translational Research, Department of Surgery, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
Ann Biomed Eng. 2021 Dec;49(12):3711-3723. doi: 10.1007/s10439-021-02772-5. Epub 2021 Apr 9.
Ischemic mitral regurgitation (IMR) is a prevalent cardiac disease associated with substantial morbidity and mortality. Contemporary surgical treatments continue to have limited long-term success, in part due to the complex and multi-factorial nature of IMR. There is thus a need to better understand IMR etiology to guide optimal patient specific treatments. Herein, we applied our finite element-based shape-matching technique to non-invasively estimate peak systolic leaflet strains in human mitral valves (MVs) from in-vivo 3D echocardiographic images taken immediately prior to and post-annuloplasty repair. From a total of 21 MVs, we found statistically significant differences in pre-surgical MV size, shape, and deformation patterns between the with and without IMR recurrence patient groups at 6 months post-surgery. Recurrent MVs had significantly less compressive circumferential strains in the anterior commissure region compared to the recurrent MVs (p = 0.0223) and were significantly larger. A logistic regression analysis revealed that average pre-surgical circumferential leaflet strain in the Carpentier A1 region independently predicted 6-month recurrence of IMR (optimal cutoff value - 18%, p = 0.0362). Collectively, these results suggest greater disease progression in the recurrent group and underscore the highly patient-specific nature of IMR. Importantly, the ability to identify such factors pre-surgically could be used to guide optimal treatment methods to reduce post-surgical IMR recurrence.
缺血性二尖瓣反流(IMR)是一种常见的心脏疾病,与较高的发病率和死亡率相关。当代的外科治疗方法仍然具有有限的长期成功率,部分原因是 IMR 的复杂和多因素性质。因此,需要更好地了解 IMR 的病因,以指导最佳的个体化治疗。在这里,我们应用基于有限元的形态匹配技术,从术前即刻和瓣环成形术修复后的活体 3D 超声心动图图像中,无创地估计人类二尖瓣(MV)的收缩期瓣叶峰值应变。在总共 21 个 MV 中,我们发现术后 6 个月时,有和无 IMR 复发的患者组之间,在 MV 术前大小、形态和变形模式方面存在统计学显著差异。与复发 MV 相比,前交界区的 MV 压缩周向应变明显减少(p=0.0223),且复发 MV 明显更大。逻辑回归分析显示,Carpentier A1 区域的平均术前周向瓣叶应变独立预测了 6 个月时 IMR 的复发(最佳截断值为-18%,p=0.0362)。总的来说,这些结果表明复发组的疾病进展更大,并强调了 IMR 的高度个体化性质。重要的是,术前识别这些因素的能力可用于指导最佳的治疗方法,以降低术后 IMR 的复发率。