HEART RATE VARIABILITY, BAROREFLEX FUNCTION AND HEART RATE TURBULENCE

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Slide 1 : HEART RATE VARIABILITY, BAROREFLEX FUNCTION AND HEART RATE TURBULENCE: POSSIBLE ORIGIN AND IMPLICATIONS Papaioannou Vasilios MD, MSc, PhD Lecturer in Intensive Care Medicine Democritus University of Thrace Alexandroupolis Medical School, Intensive Care Unit Greece
Slide 2 : Background Heart rate variability (HRV) describes variations in both instantaneous heart rate and RR intervals. Beat-to-beat fluctuations reflect the dynamic response of the cardiovascular control systems to a host of naturally occurring physiological perturbations. Although oscillations in heart rate and blood pressure were identified over 100 years ago, it was the notion that certain frequencies may be indicative of either sympathetic or parasympathetic tone, resulting in great clinical interest in describing changes in a range of physiological and pathological conditions, such as heart failure, diabetes, hypertension, sepsis and brain death.
Slide 3 : Background A variety of animal and human research has established two clear frequency bands in heart rate and blood pressure signals. These bands include oscillations associated with respiration between 0.2 to 0.4 Hz (high frequency-HF) and bands with a lower frequency range, below 0.15 Hz. The latter has often been subdivided into low-frequency (LF) range below 0.09 Hz and mid frequency range as well (0.09-0.15 Hz). Akselroad introduced in 1981 power spectrum analysis of heart rate fluctuations in order to quantify beat-to-beat cardiovascular control. Power spectrum density (PSD) analysis provides the basic information of how power (variance) distributes as a function of frequency. Akselrod S, Gordon D, Ubel FA, Shannon DC, Barger AC, Cohen RJ. Power spectrum analysis of heart rate fluctuation: a quantitative probe of beat to beat cardiovascular control. Science. 1981; 213: 220-222.
Slide 4 : Background In 1996, the Task Force of the European Society of Cardiology and the Northern American Society of Pacing and Electrophysiology published guidelines regarding standardization of nomenclature, specification of methods of measurement, definition of physiological and pathophysiological correlates, description of clinical applications and identification of different areas for future research. Task Force of the European Society of Cardiology and the North American Society of Pacing and Electrophysiology. Standards of measurement, physiological interpretation and clinical use. Circulation 1996; 93: 1043-65 and Eur Heart J 75: 354-81.
Slide 5 : Measurement of HRV Time domain methods Time domain variables include the mean NN interval, the mean heart rate or the difference between the longest and the shortest NN interval as well. There are also more complex statistical methods being used, particularly from heart rate signals being recorded for more than 24 hours. The simplest from these metrics is the standard deviation of the NN intervals (SDNN), which is the square root of the variance. The most commonly used time domain methods are the square root of the mean squared differences of successive NN intervals (RMSSD), the number of interval differences of successive NN intervals greater than 50 ms (NN50) and the proportion derived from dividing NN50 by the total NN intervals (pNN50).
Slide 6 : Measurement of HRV Frequency domain methods Spectral analysis of heart rate signals provides their power spectrum density (PSD). It displays in a plot the relative contribution (amplitude) of each frequency. Methods that are mostly used include Fast Fourier Transformation (FFT) and auto-regressive modeling. This plot includes at least three peaks. Fast periodicities in the range 0.15-0.4 Hz [high frequency (HF)] are largely due to the influence of the respiratory phase on vagal tone. Low-frequency periodicities (LF), in the region of 0.04-0.15 Hz, are produced by baroreflex feedback loops, affected by both sympathetic and parasympathetic modulation of the heart and very low frequency periodicities (VLF), in the frequency range less than 0.04 Hz have been variously ascribed to modulation by chemoreception, thermoregulation and the influence of vasomotor activity. The area under the power spectral curve in a particular frequency band is considered to be a measure of HRV at that frequency.
Slide 7 : ECG recorded and analyzed through FFT
Slide 8 : Periodogram and FFT of ECG and blood pressure signal in a control subject
Slide 9 : Table 1. HRV metrics in frequency domain.
Slide 10 : Healthy state exhibits some degree of stochastic variability in physiologic variables, such as heart rate. Loss of such variability means loss of complexity that accompanies cardiovascular disease Figure 1 illustrates the different heart rate dynamics of a healthy subject and of someone suffering from congestive heart failure (CHF). Both patients have nearly identical means and variances of heart rate. However, in the first case there is a complex and rather unpre-dictable behavior of heart rate signal whereas in the second, there are periodic oscillations that are associated with a Cheyne-Stokes pattern of breathing, indicating loss of complexity and low variability within heart rate time series. Adapted from Goldberger AL. Fractals dynamics in physiology: Alterations with disease and aging. PNAS 2002; 99: 2466-2472, downloaded from www.physionet. org).
Slide 11 : Heart rate dynamics in different clinical scenarios Figure 2. Time series of heart rate signals in 4 different situations. The plot of heart rate (beats/min) versus time (min) is called tachogram. In cases A and C we can see a rather periodic signal, with low varia-bility of its values. Especially in case C, there is a pattern of periodic oscillations (1/min), which is asso-ciated with Cheyne-Stokes breathing. The healthy record B is characterized by a rather rough and ‘patchy’ configuration, attributed to fractal properties of the heart rate signal. The breakdown of such behavior (fractal dynamics) can lead to either excessive regula-rity (A &C) or uncorrelated randomness (D). Adapted from Goldberger AL. Fractals dynamics in phy-siology: Alterations with disease and aging. PNAS 2002; 99: 2466-2472, downloaded from www.physionet.org.
Slide 12 : Clinical implications of altered HRV The first large prospective population study that proved the significant prognostic value of low HRV after an acute Myocardial infarction (MI) was the Autonomic Tone and Reflexes After Myocardial Infarction Study (ATRAMI), including 1284 patients and was published in 1987. La Rovera MT, Bigger JT, Marcus FI, Mortara A, Maestri R, Schwartz PJ. Baroreflex sensitivity and heart rate variability in prediction of total cardiac mortality after myocardial infarction. ATRAMI (Autonomic Tone and Reflexes After Myocardial Infarction) Investigators. Lancet 1998; 351: 478-484. A 24 h Holter recording was done to quantify heart rate variability (using SDNN values) and ventricular arrhythmias. Low values of HRV (SDNN<70 ms) carried a significant multivariate risk of cardiac mortality. Furthermore, the association of low SDNN with left ventricular ejection fraction (LVEF) <35% carried a relative risk of 6.7, compared with patients with LVEF above 35%.
Slide 13 : Investigators from the Framingham Heart Study studied heart rate variability time and frequency domain measures in 736 patients and correlated them with all-cause mortality during 4 years of follow-up. They concluded that HRV offers prognostic information independent of that provided by traditional risk factors. Tsuzi H, Venditti F.J, Manders E.S et al: Reduced heart rate variability and mortality risk in an elderly cohort: the Framingham Heart Study. Circulation 1994; 90: 878-83. During the Zutphen study, 885 middle-aged (40-60 years old) and elderly Dutch men (aged 65-85) were followed from 1960 until 1990, whereas SDNN was determined from the resting 12-lead ECG. It was shown that low heart rate variability is predictive of mortality from all causes, indicating that it can be used as an index of compromised health in the general population. Dekker JM, Schouten EG, Klootwijk P, Pool J, Swenne CA, Kromhout D from the ZUTPHEN STUDY: Heart rate variability from short electrocardiographic recordings predicts mortality from all causes in middle-aged and elderly men. Am J Epidemiol 1997; 145: 899-908 Clinical implications of altered HRV
Slide 14 : Except for the value of altered HRV for risk stratification after acute myocardial infarction, it has been recently demonstrated that patients suffering from congestive heart failure (retrospective electrocardiographic data analysis from 127 patients in the Veterans Affairs’ Survival Trial of Antiarrhythmic Therapy in Congestive Heart Failure) with SDNN< 65.3 ms had a significantly increased risk of sudden death (p=0.016), whereas each increase of 10 ms in SDNN conferred a 20% decrease in risk of mortality (p=0.0001). Bilchick KC, Fetics B, Djoukeng R, et al: Prognostic value of heart rate variability in chronic congestive heart failure (Veterans Affairs’ Survival Trial of Antiarrhythmic Therapy in Congestive Heart Failure). Am J Cardiol 2002; 90: 24-28. Clinical implications of altered HRV Power spectral density (PSD) of HRV in a healthy control (right) and in a patient with heart failure (left). Note that the y axis range has been reduced 10-fold for patient data in order to aid visual inspection of peaks. If the same scale had been used the plot would be almost indistingui-shable from the zero line in the patient.
Slide 15 : Table 2. Some selected studies investigating different HRV values in cardiac diseases.
Slide 16 : Baroreflex sensitivity (BRS) Myelinated and unmyelinated fibers in cranial nerves X and XI connect different brain regions to neurons in the dorsal medial region of the nucleus tractus solitarius (NTS). Projections from NTS are connected to the caudal ventral lateral medulla (CVLM) and subsequently synapse to excitatory neurons in the rostral ventral lateral medulla (RVLM). This region increases firing of the sympathetic pregaglionic motor neurons in the spinal cord. Any increase in blood pressure can activate baroreceptors that project to the NTS with subsequent activation of RVLM and CVLM neurons. The final activation of CVLM neurons from RVLM inhibits the descending excitatory input to the spinal cord. The resulting decrease in sympathetic activity (SA) opposes the initial increase in blood pressure. Goldstein B, Toweill D, Lai S, et al. Uncoupling of the autonomic and cardiovascular systems in acute brain injury. Am J Physiol Regul Integr Comp Physiol 1998; 275: 1287-92.
Slide 17 : Figure 3. Coupling between stochastic neural and cardiovascular oscillatorsOmmaya, A.K. J Neurotrauma, 1995, 12, 527 BP Stimulation of baroreceptors Stimulation of NTS Stimulation of CVLM Inhibition of RVLM of sympathetic outflow Hormone and biochemical feedback circuits The same circuits are uncoupled during severe sepsis and cardiovascular diseases Stimulation of nucleus ambiguous Vagal Inhibition of SA Lung strech receptors Respiratory arrhythmia
Slide 18 : Methods of measurement of BRS Figure 4. Phenylephrine bolus technique for measuring baroreflex sensitivity (BRS). After a bolus injection of phenylephrine there is a progressive increase in blood pressure and a reflex slowing of heart rate. BRS is calculated as the slope of the relation between systolic blood pressure (SBP) and the subsequent RR interval during this ramp increase in blood pressure. Adapted from Persson PB. J Hypertens 2001;19 :1699–705.
Slide 19 : Methods of measurement of BRS Figure 5. Cross spectral analysis of BRS using the a index method. Power spectral analysis of blood pressure and heart rate is performed, whereas the squared root ratios of different frequency components (LF&HF) between systolic blood pressure (SBP) and heart rate (or pulse interval-PI) are computed. Adapted from Parati G. J Hypertens 2000;18:7-19.
Slide 20 : Baroreflex gain in cardiovascular disease Except for a reduced variability of heart rate signals, impaired baroreflex sensitivity has been observed in patients with cardiovascular diseases such as post-myocardial infarction, hypertension and heart failure. A depressed HRV and baroreflex gain seems to result from altered responsiveness of sinus node pacemaker cells, cardiac remodelling and finally, reduced parasympathetic cardiovascular control. Di Rienzo M, Parati G, Castiglioni P, et al: Role of sinoaortic afferents in modulating BP and pulse-interval spectral characteristics in unanesthetized cats. Am J Physiol Heart Circ Physiol 1991; 261: 1811-1818. Just A, Wittman U, Nafz B, et al: The blood pressure buffering capacity of nitric oxide by comparison to the baroreceptor reflex. Am J Physiol Heart Circ Physiol 1994; 267: 521-527. Thames MD, Kinugawa T, Smith ML, Dibner-Dunlap ME: Abnormalities of baroreflex control in heart failure. J Am Coll Cardiol 1993; 22: 56-60.
Slide 21 : Heart rate turbulence (HRT) HRT it is a physiologic phenomenon and describes the short term fluctuation in sinus cycle length that follows a ventricular premature complex (VPC). It describes the increase in heart rate for 1 or 2 beats and its subsequent decrease after a VPC. Sinus acceleration and deceleration after a ventricular premature complex can be quantified by two numerical parameters: 1. turbulence onset (TO), which is the amount of acceleration and 2. turbulence slope (TS), which estimates the rate of deceleration. Turbulence onset is the percentage difference between the average value of the first two normal R-R intervals following the ventricular premature complex and the last two normal intervals preceding the VPC. Turbulence slope is the steepest slope of a linear regression line through five consecutive measurement points in the averaged tachogram (plot of heart rate versus time) and is expressed in msec /RR interval. Watanabe MA: Heart rate Turbulence: a Review. Indian Pacing Electrophysiol J 2003; 3(1): 10-22.
Slide 22 : The methodology of measurement has been described by George Schmidt’s research group in Munich and is available with free downloadable software on the website www.h-r-t.org Related to heart rate variability analysis, turbulence measurements imposes opposite problems because in the first method, ectopic beats have to be excluded whereas in the second, measurements cannot be undertaken in subjects who do not have premature ventricular beats, making the study of heart rate turbulence in healthy states unreliable. Schmidt G, Malik M, Barthel P et al: Heart rate turbulence after ventricular premature beats as a predictor of mortality after acute myocardial infarction. Lancet 1999; 353: 1390-96.
Slide 23 : Figure 6. Schematic figures showing measurements of turbu-lence onset (TO) and turbulence slope (TS) after a ventricular pre-mature complex. RR intervals (y axis) are plotted vs beat number (x axis) with 2 beats preceding and 20 beats succeeding the pre-mature beat, with the compen-satory pause being beat 0. In this example, TO is -4.7%. For TS measurement, the slopes of 5 beat RR sequences after the com-pensatory pause are fitted with a straight line. The TS in this e-xample is 16 ms/RR interval. Downloaded from the open-source website: www. h-r-t.org).
Slide 24 : Clinical applications of HRT In the first clinical study of turbulence as a predictor of mortality after myocardial infarction (Lancet study), different threshold values were determined from Holter recordings of 100 patients, three months after an acute myocardial infarction. Turbulence onset =0 and slope =2.5 were considered abnormal, meaning that healthy state is associated with a strong acceleration followed by a rapid deceleration, whereas inverse findings characterize pathologic responses. Schmidt G, Malik M, Barthel P et al: Heart rate turbulence after ventricular premature beats as a predictor of mortality after acute myocardial infarction. Lancet 1999; 353: 1390-96. These metrics were blindly applied to Holter records from a total of 1191 patients from two large clinical trial groups, the placebo arm of the European Myocardial Infarction Amiodarone Trial (EMIAT, number of patients: 614) and the Multicentre Post Infarction Program (MPIP, number of patients: 577). Univariate and multivariate analysis showed that turbulence slope, turbulence onset, previous infarction history, ejection fraction and heart rate >75 were independent predictors of mortality.
Slide 25 : HRT applicationsThe slope was the strongest risk stratifier in EMIAT (relative risk 2.7) and the second strongest in MPIP trial (relative risk 3.5). A relative risk of approximately 3 means that patients with abnormal turbulence slope values are 3 times more likely to die than those with normal measurements Julian DG, Camm AJ, Frangin G, et al: Randomized trial of effect of amiodarone on mortality in patients with left-ventricular dysfunction after recent myocardial infarction. EMIAT. Lancet. 1997; 349: 667–674. Bigger JJ, Fleiss JL, Kleiger R, Miller JP, Rolnitzky LM. The relationships among ventricular arrhythmias, left ventricular dysfunction, and mortality in the 2 years after myocardial infarction. Circulation 1984; 69: 250-58.
Slide 26 : HRT applications Data from the ATRAMI trial were also used to study heart rate turbulence and its relation with cardiac arrest. It was demonstrated that turbulence slope and onset could discriminate survivors vs non-survivors. From these two studies (Lancet and ATRAMI) sensitivity and positive predictive value of the combination of TS and TO were estimated as 30%, meaning that 1 in 3 post-myocardial infarction patients whose slope and onset are both abnormal 2 weeks after their infarction (Holter records in Lancet trial were made in the 2nd week after the event) are likely to die in 2 years (mean follow-up in ATRAMI study).
Slide 27 : Conclusions The Task Force on Sudden Cardiac Death of the European Society of Cardiology recently recommended a risk stratification strategy which combines a marker of structural damage (such as LVEF) with markers of autonomic imbalance. Priori SG, Aliot E, Blomstrom-Lundqvist C, et al: Task Force on Sudden Cardiac Death of the European Society of Cardiology. Eur Heart J 2001; 16: 1374-1450. Such markers include HRV and HRT both being related to baroreflex arc and have been proven to discriminate survivors vs non-survivors in different studies. In conclusion, the most important aspect of research on heart rate dynamics remains the free access of different investigators to international databases of various biosignals, (ECG, ST, QT) that contain different software tools for signal processing, such as the Web site Physionet (www.physionet.org) which is a public service of the Research Resource for Complex Physiologic Signals and the website www.h-r-t.org, for HRT analysis.

 


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