Scheff Jeremy D, Griffel Benjamin, Corbett Siobhan A, Calvano Steve E, Androulakis Ioannis P
Department of Biomedical Engineering, Rutgers University, 599 Taylor Road, Piscataway, NJ 08854, USA.
Department of Surgery, Robert Wood Johnson Medical School, Clinical Academic Building, 125 Patterson Street, New Brunswick, NJ 08901, USA.
Math Biosci. 2014 Jun;252:36-44. doi: 10.1016/j.mbs.2014.03.010. Epub 2014 Mar 26.
Analysis of heart rate variability (HRV) is a promising diagnostic technique due to the noninvasive nature of the measurements involved and established correlations with disease severity, particularly in inflammation-linked disorders. However, the complexities underlying the interpretation of HRV complicate understanding the mechanisms that cause variability. Despite this, such interpretations are often found in literature. In this paper we explored mathematical modeling of the relationship between the autonomic nervous system and the heart, incorporating basic mechanisms such as perturbing mean values of oscillating autonomic activities and saturating signal transduction pathways to explore their impacts on HRV. We focused our analysis on human endotoxemia, a well-established, controlled experimental model of systemic inflammation that provokes changes in HRV representative of acute stress. By contrasting modeling results with published experimental data and analyses, we found that even a simple model linking the autonomic nervous system and the heart confound the interpretation of HRV changes in human endotoxemia. Multiple plausible alternative hypotheses, encoded in a model-based framework, equally reconciled experimental results. In total, our work illustrates how conventional assumptions about the relationships between autonomic activity and frequency-domain HRV metrics break down, even in a simple model. This underscores the need for further experimental work towards unraveling the underlying mechanisms of autonomic dysfunction and HRV changes in systemic inflammation. Understanding the extent of information encoded in HRV signals is critical in appropriately analyzing prior and future studies.
心率变异性（HRV）分析是一种很有前景的诊断技术，这是因为所涉及的测量具有非侵入性，且与疾病严重程度存在已确立的相关性，尤其是在与炎症相关的疾病中。然而，HRV解释背后的复杂性使得理解导致变异性的机制变得复杂。尽管如此，此类解释在文献中经常可以见到。在本文中，我们探讨了自主神经系统与心脏之间关系的数学建模，纳入了诸如扰动振荡性自主活动的平均值以及使信号转导通路饱和等基本机制，以探究它们对HRV的影响。我们将分析重点放在人类内毒素血症上，这是一种成熟的、可控的全身炎症实验模型，会引发代表急性应激的HRV变化。通过将建模结果与已发表的实验数据及分析进行对比，我们发现，即使是一个将自主神经系统与心脏联系起来的简单模型，也会使对人类内毒素血症中HRV变化的解释变得混乱。在基于模型的框架中编码的多个看似合理的替代假设同样能与实验结果相契合。总体而言，我们的工作说明了关于自主活动与频域HRV指标之间关系的传统假设是如何失效的，即使在一个简单模型中也是如此。这凸显了开展进一步实验工作以阐明全身炎症中自主神经功能障碍及HRV变化潜在机制的必要性。了解HRV信号中编码的信息程度对于恰当地分析既往和未来的研究至关重要。
