How to strengthen or weaken the HRV dependence on heart rate — Description of the method and its perspectives

Sacha, Jerzy; Barabach, Szymon; Statkiewicz-Barabach, Gabriela; Sacha, Krzysztof; Müller, Alexander; Piskorski, Jarosław; Barthel, Petra; Schmidt, Georg

doi:10.1016/j.ijcard.2013.03.038

Cited by 71 publications

(113 citation statements)

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“…As described by Sacha et al (2013a), the HRV dependence on HR is strengthened or weakened by multiplying or dividing the HRV indices by the corresponding segment's RR avg , respectively. In addition to normal determination of HRV indices, eight other classes for the indices were assessed in this study: HRV MUL-0.5 —multiplying HRV indices by RR avg to the power 0.5; HRV MUL-2 —multiplying HRV indices by RR avg to the power 2; HRV MUL-4 —multiplying HRV indices by RR avg to the power 4; HRV DIV-0.5 —dividing HRV indices by RR avg to the power 0.5; HRV DIV-2 —dividing HRV indices by RR avg to the power 2; HRV DIV-4 —dividing HRV indices by RR avg to the power 4; HRV DIV-8 —dividing HRV indices by RR avg to the power 8; and HRV DIV-16 —dividing HRV indices by RR avg to the power 16.…”

Section: Methodsmentioning

confidence: 99%

“…Possible physiological mechanisms involved have also been probed (Perini and Veicteinas, 2003; Goldberger et al, 2006). The non-linear relation between HR and HRV has been addressed by Sacha and Pluta (2008) and correction methods have been suggested to strengthen or weaken the influence of HR (Sacha et al, 2013a; Sacha, 2013). By determining whether decreasing dependence on HR improves the prognostic capacity of HRV, we sought to establish the influence of HR in predicting mortality risk.…”

Section: Introductionmentioning

confidence: 99%

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Effect of heart rate correction on pre- and post-exercise heart rate variability to predict risk of mortalityâ€”an experimental study on the FINCAVAS cohort

Pradhapan¹,

Tarvainen²,

Nieminen³

et al. 2014

Front. Physiol.

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The non-linear inverse relationship between RR-intervals and heart rate (HR) contributes significantly to the heart rate variability (HRV) parameters and their performance in mortality prediction. To determine the level of influence HR exerts over HRV parameters' prognostic power, we studied the predictive performance for different HR levels by applying eight correction procedures, multiplying or dividing HRV parameters by the mean RR-interval (RRavg) to the power 0.5–16. Data collected from 1288 patients in The Finnish Cardiovascular Study (FINCAVAS), who satisfied the inclusion criteria, was used for the analyses. HRV parameters (RMSSD, VLF Power and LF Power) were calculated from 2-min segment in the rest phase before exercise and 2-min recovery period immediately after peak exercise. Area under the receiver operating characteristic curve (AUC) was used to determine the predictive performance for each parameter with and without HR corrections in rest and recovery phases. The division of HRV parameters by segment's RRavg to the power 2 (HRVDIV-2) showed the highest predictive performance under the rest phase (RMSSD: 0.67/0.66; VLF Power: 0.70/0.62; LF Power: 0.79/0.65; cardiac mortality/non-cardiac mortality) with minimum correlation to HR (r = −0.15 to 0.15). In the recovery phase, Kaplan-Meier (KM) survival analysis revealed good risk stratification capacity at HRVDIV-2 in both groups (cardiac and non-cardiac mortality). Although higher powers of correction (HRVDIV-4and HRVDIV-8) improved predictive performance during recovery, they induced an increased positive correlation to HR. Thus, we inferred that predictive capacity of HRV during rest and recovery is augmented when its dependence on HR is weakened by applying appropriate correction procedures.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Effect of heart rate correction on pre- and post-exercise heart rate variability to predict risk of mortalityâ€”an experimental study on the FINCAVAS cohort

Pradhapan¹,

Tarvainen²,

Nieminen³

et al. 2014

Front. Physiol.

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“…In fact it is hard to ascertain which of these two (i.e., HR or variability) really matters in the prognostic significance of HRV (Sacha, 2013, 2014a,b). Recently, a new approach which enables to weaken (or even eliminate) or strengthen the association between HR and its variability has been proposed (Sacha et al, 2013a). The principles of this method are simple, i.e., by division of RR interval tachograms by the corresponding average RR intervals, the variability of RR intervals of slow HR is attenuated, but that of fast HR is relatively amplified and the resulting HRV loses its correlation with HR.…”

mentioning

confidence: 99%

“…On the other hand, by multiplication of RR interval tachograms by their average RR intervals, the variability of slow HR is boosted but that of fast HR is relatively suppressed, and consequently, the correlation between HRV and HR is growing. By division or multiplication by higher powers of average RR intervals stronger effect on the HRV/HR relationship can be achieved (Sacha et al, 2013a). Moreover, by division by very high powers, this relationship may even be inverted, i.e., from negative to positive one (Figure 1A) (Sacha et al, 2013b).…”

mentioning

confidence: 99%

“…However, the unraveling of this remarkable game, by using the method of strengthening or weakening the HRV/HR dependence (Sacha et al, 2013a), may yield valuable prognostic information (Sacha et al, 2013c, 2014; Pradhapan et al, 2014; Sacha, 2014a,b). This is particularly important in women, in whom HR is a weak (or even is not) risk factor and actually can cover the prognostic value of HRV—yet, this detrimental effect may be eliminated by the removal of HRV dependence on HR (Sacha et al, 2014).…”

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