The Heart Rate Variability: new tool for diagnosis and management of cardiovascular diseases?

Until a few years ago the only information that doctors utilized from the sequences of heartbeats (R-R) was only their average for shorter or longer intervals. The heart rate was related to macroscopic states such as fatigue, fever, emotion etc…
Much of the information, represented by the R-R interval, was therefore unused, and two key aspects were overlooked: the RR intervals are not all equal (variability), and that there are laws that organize this variability and distinguish it from background noise related to sympathetic and parasympathetic autonomic control.
Over the past two decades, there has been a growing interest in the application of methods and techniques for analyzing nonlinear dynamics to the historical series of electrocardiograms.
There are currently several methods to assess heart rate variability.
The analysis performed in the time domain is a quantitative estimate of the magnitude of change in the cardiac cycle. The easiest way in this area is to calculate the standard deviation of the average of all successive R- R and its derived parameters SDANN, r-MSSD1; the last two indices are considered as measures which reflect mainly the parasympathetic component of heart rate variability.
The SD of the R-R sequence was shown to be a predictor of important pathophysiological states. A heart attack is preceded by a sharp reduction of SD; aging causes a slight, but significant, reduction of SD of R-R intervals at rest.
The next step was to study the distribution of variability, i.e., whether deviations from the mean of R-R intervals have lengths and identifiable characteristics.
In 1981 Akselrod stated in Science that spectral analysis (HRV) non-invasively provided information on Simpson vagal control of heart, demonstrating the possibility of a direct relationship between fluctuations in heart rate and neurogenic modulation of the heart 2.
The analysis performed in the frequency domain is based on Fourier analysis of the variability of R-R intervals and allows the expression of the sequence of R-R intervals as a sum of regular patterns with different frequencies (periodicity). The relative weight of these different frequencies in determining the signal, and what their weight distribution is in that signal spectrum are calculated. The HRV has the advantage of being a simple and noninvasive method to study the dynamic changes of autonomic control of heart rate.
Depending on the duration of the registration, it is possible to determine their different ranges of oscillation. When the sequence of RR intervals covers a period from about 2 to 5 minutes up to three peaks can be identified:
1. Very low frequencies (0.003 to 0.04 Hz, very low frequency, VLF) - corresponding to similar oscillations in pressure (Mayer waves) and to fluctuations sympathetic outflow;
2. Low frequencies (0.04 to 0.15 Hz, low frequency, LF);
3. High frequencies (0.15 to 0.4 Hz, high frequency, HF);
The HF component of HRV is an index of vagal activity synchronous with the respiratory rhythm, and is in fact reduced by more than 90% by administration of atropine, but not feeling the effects of sympathetic blockade with propranolol; the LF and VLF components reflect variability secondary to a more subtle sympathetic- vagal modulation. (Fig.1).
Classically, in terms of homeostasis, medicine interprets the fluctuations as negative feedback. One might then think that ill individual lose the ability to maintain a constant heart rate at rest with a consequent increase in variability.
When the application of nonlinear dynamics in physiological systems was started, it was expected then that the chaos would be more observable in disease states rather than in normal physiological situations.
However, the analysis of heart rate of healthy young individuals at rest demonstrated a high variability in time with highly irregular and seemingly random series. On a day the heart rate can go from 40 to 180 beats per minute. Instead the paths that preceded pathological conditions such as heart failure, arrhythmias, sudden cardiac death, are more regular 3-6. The total variability of heart rate was strikingly reduced in patients with denervated transplanted heart, and tended to increase during periods of physical activity7.
The disease condition was in fact closely linked to an increase of order, predictability, and a drastic reduction of complexity and dimensionality of the system. Irregular paths typical of physiological situation have been recognized to have chaotic dynamics with strange attractors. The frequency oscillates spontaneously in the absence of external perturbations and does not tend towards a stationary homeostasis only in pathological situations.
Therefore, even if the pathophysiological mechanisms are still not fully clarified, the decrease in HRV has a negative significance in the prognosis of both, patients with cardiovascular disease and the general population, probably expressing a reduced response of the sinus node. These observations have pioneered the use of pharmacological measures that do not enhance HRV.
Analysis of heart rate variability is therefore an important tool for physiological research in the understanding of autonomic and baroreceptor control mechanisms.
From the clinical point of view, it allows the estimation of the degree of degeneration of the control function in the presence of diseases of great social importance such as stroke, hypertension and diabetes, and allows the definition of statistical parameters for risk stratification of cardiac death. However there are still important issues that must be explored before the non-linear techniques reach a large audience of researchers and clinicians and can be considered a useful tool for diagnosis and management of patients.

REFERENCES

1. Toys R Cantini F, Varanini M, Raimondi G, Legramante JM, Macerata A. Revisiting the Potential of time-domain indexes in short-term HRV analysis. Biomed Tech 2006, 51:190-193. 2006.
2. Akselrod S, Gordon D, Ubel FA, Shannon DC, Berger AC and Cohen RJ Power spectrum analysis of heart rate fluctuation: a quantitative probe of beat-to-beat cardiovascular control. Science 1981, Vol 213, 220-222.
3. Kaye DM, Esler M. Sympathetic neuronal regulation of the heart in aging and heart failure. Cardiovasc Res 2005; 66: 256-64
4. La Rovere MT, Bigger JT Jr, Marcus FI, Mortara A, Schwartz PJ. Baroreflex sensitivity and heart rate variability in prediction of total cardiac mortality after infarction. ATRAMI (Autonomic Tone and Reflexes After Myocardial Infarction). Lancet 1998, 351: 478-84
5. Lombardi F. Chaos theory, heart rate variability, and arrhythmic mortality. Circulation 2000, 101: 8-10.
6. Malliani A, Pagani M. The role of the sympathetic nervous system in congestive heart failure. Eur Heart J 1983; 4: 49-54.
7. Bernardi L, Valle F, Coco M, football, Sleight P. Physical activity influences heart rate variability and very-low-frequency components in Holter electrocardiograms. Cardiovasc Res 1996; 32: 234-37