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

Abstract

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.

Influence of Heart Rate in Non-linear HRV Indices as a Sampling Rate Effect Evaluated on Supine and Standing

“…Furthermore, different mathematical models have demonstrated a relationship between HRV amplitude and HR correcting it (Chiu et al, 2003; Meste et al, 2005; Bailón et al, 2011; Sacha, 2014; Billman et al, 2015). HR correction effect on HRV analysis was studied to predict risk mortality (Pradhapan et al, 2014). Non linear indices, such as correlation dimension, sample, and approximate entropy, are computed over linearly detrended and normalized series so this effect is already compensated for (Osaka et al, 1993; Pincus et al, 1993; Porta et al, 2007; Voss et al, 2009; Bolea et al, 2014a).…”

Interplay between heart rate and its variability: a prognostic game

“…The study showed that HR right before exercise was not a risk factor of death and elimination of its influence improved the predictive capability of the respective HRV, conversely, HR during recovery phase was a significant mortality predictor and the enhancement of its impact augmented the respective HRV prognostic performance (Pradhapan et al, 2014). …”

Blinded Analysis of an Exercise ECG Database Using High Frequency QRS Analysis

“…Since the FINCAVAS study contains records of thousands of patients and its results have already been published [14][15][16][17] we plan a future study to analyze the entire cohort with two major goals: (a) verifying the results of this study and past studies that were using the same software and (b) determining an optimal configuration for band limited ECG records which may enhance the performance of HFQRS for this specific configuration.…”

“…For this study 132 ECG records of bicycle exercise tests were obtained from the FINCAVAS database [14][15][16][17].…”