Lu Yan, Burykin Anton, Deem Michael W, Buchman Timothy G
Department of Physics and Astronomy, Rice University, Houston, TX 77005, USA.
J Crit Care. 2009 Sep;24(3):347-61. doi: 10.1016/j.jcrc.2009.01.014.
Analysis of heart rate (HR) dynamics before, during, and after a physiologic stress has clinical importance. For example, the celerity of heart rate recovery (HRR) after a cardiac stress test (eg, treadmill exercise test) has been shown to be an independent predictor of all-cause mortality. Heart rate dynamics are modulated, in part, by the autonomic nervous system. These dynamics are commonly abstracted using metrics of heart rate variability (HRV), which are known to be sensitive to the influence of the autonomic nervous system on HR. The patient-specific modulators of HR should be reflected both in the response to stress as well as in the recovery from stress. We therefore hypothesized that the patient-specific HR response to stress could be used to predict the HRR after the stress. We devised a Markov chain model to predict the poststress HRR dynamics using the parameters (transition matrix) calculated from HR data during the stress. The model correctly predicts the exponential shape of poststress HRR. This model features a simple analytical relationship linking poststress HRR time constant (T(off)) with a standard measure of HRV, namely the correlation coefficient of the Poincaré plot (first return map) of the HR recorded during the stress. A corresponding relationship exists between the time constant (T(on)) of R-R interval decrease at the onset of stress and the correlation coefficient of the Poincaré plot of prestress R-R intervals. Consequently, the model can be used for the prediction of poststress HRR using the HRV measured during the stress. This direct relationship between the event-to-event microscopic fluctuations (HRV) during the stress and the macroscopic response (HRR) after the stress terminates can be interpreted as an instance of a fluctuation-dissipation relationship. We have thus applied the fluctuation-dissipation theorem to the analysis of heart rate dynamics. The approach is specific neither to cardiac physiology nor to transitions between mechanical and free ventilation as a specific stress. It may therefore have wider applicability to physiologic systems subject to modest stresses.
分析生理应激前、应激期间和应激后的心率(HR)动态变化具有临床意义。例如,心脏应激试验(如跑步机运动试验)后心率恢复(HRR)的速度已被证明是全因死亡率的独立预测指标。心率动态变化部分受自主神经系统调节。这些动态变化通常使用心率变异性(HRV)指标进行提取,已知HRV对自主神经系统对心率的影响敏感。特定患者的心率调节因素应在应激反应以及应激恢复中得到体现。因此,我们假设特定患者对应激的心率反应可用于预测应激后的HRR。我们设计了一个马尔可夫链模型,使用应激期间从心率数据计算出的参数(转移矩阵)来预测应激后HRR动态变化。该模型正确预测了应激后HRR的指数形状。该模型的特点是,应激后HRR时间常数(T(off))与HRV的一个标准测量指标(即应激期间记录的心率的庞加莱图(首次返回映射)的相关系数)之间存在简单的分析关系。应激开始时R-R间期缩短的时间常数(T(on))与应激前R-R间期的庞加莱图的相关系数之间也存在相应关系。因此,该模型可用于利用应激期间测量的HRV预测应激后HRR。应激期间逐事件微观波动(HRV)与应激终止后宏观反应(HRR)之间的这种直接关系可解释为涨落耗散关系的一个实例。我们因此将涨落耗散定理应用于心率动态变化分析。该方法既不特定于心脏生理学,也不特定于作为特定应激的机械通气和自主通气之间的转换。因此,它可能在更广泛的适度应激生理系统中具有适用性。