Modeling Pathologies of Diastolic and Systolic Heart Failure.
Modeling Pathologies of Diastolic and Systolic Heart Failure.
复制标题
舒张性和收缩性心力衰竭的病理学建模。
DOI：
10.1007/s10439-015-1351-2
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发表时间：
2016-01
影响因子：
3.8
通讯作者：
Kuhl E
中科院分区：
文献类型：
--
作者：
Genet M;Lee LC;Baillargeon B;Guccione JM;Kuhl E
慢性心力衰竭是一种涉及心脏结构和功能改变和心输出量进行性减少的医学状况。心力衰竭分为两类：舒张性心力衰竭，心室壁增厚与充盈受损有关；收缩性心力衰竭，心室扩张与泵功能降低有关。从理论上讲，心衰的病理生理学是很清楚的。然而，在实践中，心力衰竭对心脏微观结构、几何形状和负荷高度敏感。这使得预测患者心力衰竭的时间几乎是不可能的。在这里，我们展示了计算建模使我们能够整合来自不同尺度的知识，以创建慢性心力衰竭期间心脏生长和重塑的个性化模型。我们的模型自然地将平行和连续的肌瘤沉积的分子事件与肌原纤维形成和肌瘤形成的细胞现象与整个器官功能联系起来。我们的模拟预测了壁厚、腔室大小和心脏几何形状的慢性改变，这与舒张期和收缩期心力衰竭患者的临床观察结果非常吻合。与现有的单室或双室模型相比，我们的新四室模型还可以预测包括乳头肌脱位、环扩张、反流和流出梗阻在内的特征性继发性效应。我们的原型研究表明，计算模型为心力衰竭的进展提供了一个针对患者的窗口，以实现个性化的治疗计划。
Chronic heart failure is a medical condition that involves structural and functional changes of the heart and a progressive reduction in cardiac output. Heart failure is classified into two categories: diastolic heart failure, a thickening of the ventricular wall associated with impaired filling; and systolic heart failure, a dilation of the ventricles associated with reduced pump function. In theory, the pathophysiology of heart failure is well understood. In practice, however, heart failure is highly sensitive to cardiac microstructure, geometry, and loading. This makes it virtually impossible to predict the time line of heart failure for a diseased individual. Here we show that computational modeling allows us to integrate knowledge from different scales to create an individualized model for cardiac growth and remodeling during chronic heart failure. Our model naturally connects molecular events of parallel and serial sarcomere deposition with cellular phenomena of myofibrillogenesis and sarcomerogenesis to whole organ function. Our simulations predict chronic alterations in wall thickness, chamber size, and cardiac geometry, which agree favorably with the clinical observations in patients with diastolic and systolic heart failure. In contrast to existing single- or bi-ventricular models, our new four-chamber model can also predict characteristic secondary effects including papillary muscle dislocation, annular dilation, regurgitant flow, and outflow obstruction. Our prototype study suggests that computational modeling provides a patient-specific window into the progression of heart failure with a view towards personalized treatment planning.