CARDIOPULMONARY TIME SERIES ANALYSIS--DRUG AND AGING EFFECTS

我们提出生物电时间序列，如心电(ECG)， 血压记录和呼吸记录包含信息 关于人的状态，以及药物对人的状态的影响 患者认为单变量分析技术和简单均方差度量 都不足以刻画。我们已经证明了快速傅里叶变换 基于变换(FFT)的节拍变异性分析方法 人体服用维拉帕米时的心率(心电图) 已证明的效果(与以下方面相关的频率变异性降低 呼吸或迷走神经介导的副交感神经调节) 在平均心率反应或其他时间域分析中检测到 心率反应。此外，我们和其他人已经证明了这种频率 心电数据的域分析可以识别压力反射反应的激活 -有可能对间接药物进行鉴定和定量 通过反射反应激活的效应，可能显著影响Net 体内药物效应。我们还使用了这些分析技术来说明 衰老改变基础心率变异性和频率含量 心率对姿势动作的反应。在这些初始/试行中 研究表明，呼吸的方法(以及心率) 改变心率数据的频率内容，需要考虑 在评估心率数据时，以及最佳解释方法 的频域数据还没有开发出来。 我们还展示了与年龄相关的药效学变化。 作为药代动力学。特别是，压力反射反应似乎是 随着年龄的增长而改变(减少)。因为对药物的反应改变可以 由不同的药物演示、组织敏感性、不同的 动态平衡反射，或者这些因素的组合，它是 重要的是要评估改变的反射反应对 老年人对药物的药效学反应改变。这是 对于可能激活压力感受器的血管活性药物尤其重要 和是老年人最常用的处方药 人口。 因此，我们现在建议进一步开发和验证以下方法 生物电时间序列信息的分析。除了提炼之外 分析心率节拍变化的方法，我们将 建立一种分析血液中搏动变异性的方法 压力，以及呼吸中呼吸之间的变异性。到时候我们会的 开发一个模型，在该模型中心率、血压和呼吸时间 级数通过传递函数相互关联，其中相互作用 可以对变量之间的时间进程进行评估和量化， 大小和对体内效应的贡献。然后我们将使用这个 组合生物电时间序列模型在药物效应分析中的应用 给药以识别和量化反射的作用 (副交感或β-肾上腺素能)与药物联合反应 剂量或浓度数据在体内的净药物效应和(2)测试 衰老改变血压和血压时间序列含量的假说 呼吸与心率、血压之间的相互关系 和呼吸；而与年龄相关的反射反应的变化是 老年人药物反应改变的主要决定因素可能是 可预测和可量化的。最后，根据这些研究的结果 调查，我们将把模型简化为最简单的形式 充分描述和预测人类的生理和药物反应。

We propose that bioelectrical time series such as electrocardiograms (ECG), blood pressure recordings, and respiratory recordings, contain information regarding the state of a human, and, the effect of a drug on the state of a patient that univariate analytic techniques and simple mean effect measures are inadequate to characterize. We have shown that Fast Fourier Transformation (FFT)-based methods to analyze beat-to-beat variability of heart rate (ECG data) during administration of verapamil to man demonstrated effects (decreased variability in frequencies associated with respiratory or vagally-mediated parasympathetic modulation) which were not detected in average heart rate responses or other time domain analyses of heart rate responses. In addition, we and others have shown that frequency domain analysis of ECG data can identify activation of baroreflex responses - potentially allowing identification and quantitation of indirect drug effects via reflex response activation which may markedly influence net drug effect in vivo. We have also used these analytic techniques to show that aging alters basal heart rate variability and frequency content of heart rate responses to postural maneuvers. In these initial/pilot studies, it became apparent that method of respiration (as well as rate) alters the frequency content of heart rate data, and needs to be considered in evaluating heart rate data, and, that optimal methods for interpretation of frequency domain data were not yet developed. We have also demonstrated age-related changes in pharmacodynamics as well as pharmacokinetics. In particular, baroreflex responses appear to be altered (diminished) with aging. Since altered responses to drugs can result from differing drug demonstrations, tissue sensitivities, differing homeostatic reflex responses, or a combination of these factors, it is important to evaluate the contributions of altered reflex responses to the altered pharmacodynamic responses of elderly individuals to drugs. This is especially important with vasoactive drugs which may activate baroreceptor responses and are the most frequently prescribed drugs in the elderly population. Therefore, we now propose to further develop and validate methods for analysis of bioelectric time series information. In addition to refining methods for analysis of beat-to-beat variation in heart rate, we will develop a method for analysis of beat-to-beat variability in blood pressure, and breath-to-breath variability in respiration. We will then develop a model in which heart rate, blood pressure, and respiration time series are interrelated by transfer functions and in which the interactions between variables can be evaluated and quantitated as to time course, magnitude, and contribution to in vivo effects. We will then use this combined bioelectric time series model to (1) analyze effects during drug administration to identify and quantitate contributions of reflex (parasympathetic or beta-adrenergic) responses in combination with drug dose or concentration data to net drug effect in vivo and (2) test the hypotheses that aging changes time series content of blood pressure and respiration and the interrelationship between heart rate, blood pressure and respiration; and, that age-related changes in reflex responses are a major determinant of altered drug responses in the elderly that may be predictable and quantifiable. Finally, based on results from these investigations, we will reduce the model to the simplest form that adequately describes and predicts physiologic and drug responses in humans.