Heart rate contribution to the clinical value of heart rate variability

Copyright © Polskie Towarzystwo Kardiologiczne higher fluctuations of R-R intervals for the slow average HR than for the fast one. Moreover, the fluctuations of R-R intervals for fast HR may not be as high as for slow HR because the R-R intervals should have become negative (Fig. 1) [13–15, 17–20]. Due to these facts, standard HRV analysis may be mathematically biased, particularly if patients exhibit different average HRs. To overcome this problem, one should calculate the variability of R-R intervals with respect to the average R-R interval, i.e. normalise the oscillations with respect to the mean value. One can do that by dividing the sequence of R-R intervals by the corresponding average R-R interval [15, 17–20]. Or, one may divide standard deviation of R-R intervals by average R-R interval (i.e. calculation of coefficient of variation), or divide HRV power spectrum (or its components) by the average R-R interval squared [15]. Such a normalisation is critical for investigations of HRV after different interventions which change HR because by employing this approach, one may differentiate between physiologically and mathematically mediated changes in HRV (i.e. one may exclude the mathematical bias) [15, 17–21]. For example, metoprolol-induced changes of HRV become insignificant after they are normalised to the same R-R interval, suggesting that the increase in HRV after beta-blockade can be explained by a change of HR [22]. Also, in an animal model, it has been shown that beta-adrenergic receptor blockade may reduce rather than increase R-R interval variability after correction for the drug-induced HR reductions [21]. Furthermore, an employment of this correction method has helped to demonstrate that HR is a better indicator of higher fitness than its variability — i.e. an association between HRV indices and maximal oxygen intake (VO2max) exists mainly due to the relationship between HR and VO2max [23]. On the other hand, the same method has shown that an increase in HRV following dengue viral infection does not result from the accompanying reduction in HR, but reflects a real improvement in cardiac autonomic nervous control [24]. Therefore, it is necessary to establish to what extent HRV changes associated with HR alterations are physiologically and INTRODUCTION Heart rate (HR) has been extensively investigated for many years and has been found to be a significant risk factor, especially for cardiovascular events [1–3]. Its predictive ability has been proven in different settings, i.e. both at rest and during or after exercise [1–5]. However, even resting HR is not a constant quantity but one that changes beat by beat, a phenomenon that is commonly called heart rate variability (HRV) [6]. Both HR and HRV are under the influence of the autonomic nervous system activity, and to some extent may reflect autonomic imbalance associated with different pathological states [6–8]. Years of investigations have shown that HRV is an important risk factor associated with adverse outcomes in various diseases [8–12]. However, HRV reveals a significant correlation with HR, and therefore HRV actually provides information on two quantities, i.e. heart rate and its variability [13–15]. The question arises as to which of these two really matters in HRV prognostic value; in other words, what is the HR contribution to the prognostic ability of HRV? This article summarises recent reports concerning this intriguing point as well as methodological aspects of such research.