Data_Sheet_1_Etiology-Specific Remodeling in Ventricular Tissue of Heart Failure Patients and Its Implications for Computational Modeling of Electrical Conduction.pdf

With an estimated 64.3 million cases worldwide, heart failure (HF) imposes an enormous burden on healthcare systems. Sudden death from arrhythmia is the major cause of mortality in HF patients. Computational modeling of the failing heart provides insights into mechanisms of arrhythmogenesis, risk stratification of patients, and clinical treatment. However, the lack of a clinically informed approach to model cardiac tissues in HF hinders progress in developing patient-specific strategies. Here, we provide a microscopy-based foundation for modeling conduction in HF tissues. We acquired 2D images of left ventricular tissues from HF patients (n = 16) and donors (n = 5). The composition and heterogeneity of fibrosis were quantified at a sub-micrometer resolution over an area of 1 mm2. From the images, we constructed computational bidomain models of tissue electrophysiology. We computed local upstroke velocities of the membrane voltage and anisotropic conduction velocities (CV). The non-myocyte volume fraction was higher in HF than donors (39.68 ± 14.23 vs. 22.09 ± 2.72%, p < 0.01), and higher in ischemic (IC) than nonischemic (NIC) cardiomyopathy (47.2 ± 16.18 vs. 32.16 ± 6.55%, p < 0.05). The heterogeneity of fibrosis within each subject was highest for IC (27.1 ± 6.03%) and lowest for donors (7.47 ± 1.37%) with NIC (15.69 ± 5.76%) in between. K-means clustering of this heterogeneity discriminated IC and NIC with an accuracy of 81.25%. The heterogeneity in CV increased from donor to NIC to IC tissues. CV decreased with increasing fibrosis for longitudinal (R2 = 0.28, p < 0.05) and transverse conduction (R2 = 0.46, p < 0.01). The tilt angle of the CV vectors increased 2.1° for longitudinal and 0.91° for transverse conduction per 1% increase in fibrosis. Our study suggests that conduction fundamentally differs in the two etiologies due to the characteristics of fibrosis. Our study highlights the importance of the etiology-specific modeling of HF tissues and integration of medical history into electrophysiology models for personalized risk stratification and treatment planning.

全球范围内，心力衰竭（HF）病例估计达6430万，给医疗体系带来了巨大的负担。在心力衰竭患者中，心律失常导致的突发死亡是主要的死亡原因。对衰竭心脏的计算模型构建，有助于揭示心律失常的发病机制、患者的风险评估以及临床治疗的策略。然而，由于缺乏基于临床信息的心脏组织模型构建方法，阻碍了针对个体化策略的发展。在本研究中，我们为心力衰竭组织的传导建模提供了基于显微技术的坚实基础。我们收集了16名心力衰竭患者和5名供体的左心室组织二维图像。在1平方毫米的区域内，以亚微米分辨率量化了纤维化的组成和异质性。通过对图像的分析，我们构建了组织电生理学的计算双域模型。计算了膜电压的局部上升速度和各向异性传导速度（CV）。与供体相比，心力衰竭患者的非心肌细胞体积分数更高（39.68 ± 14.23% vs. 22.09 ± 2.72%，p < 0.01），而在缺血性（IC）心肌病中高于非缺血性（NIC）心肌病（47.2 ± 16.18% vs. 32.16 ± 6.55%，p < 0.05）。每个受试者中纤维化的异质性，以IC最高（27.1 ± 6.03%），供体（7.47 ± 1.37%）最低，NIC（15.69 ± 5.76%）居中。K-means聚类分析这种异质性，将IC和NIC区分开来，准确率达到81.25%。CV的异质性从供体到NIC到IC组织逐渐增加。随着纤维化的增加，纵向（R2 = 0.28，p < 0.05）和横断面传导（R2 = 0.46，p < 0.01）的CV降低。随着纤维化程度的每增加1%，CV向量的倾角分别增加了2.1°（纵向）和0.91°（横断面）。我们的研究表明，由于纤维化的特性，两种病因的传导存在根本性的差异。本研究强调了心力衰竭组织病因特异性建模以及将病史整合到电生理模型中对于个体化风险评估和治疗计划制定的重要性。