- Academic Editor
†These authors contributed equally.
Background: Coronary artery bypass grafting (CABG) is a well-established surgical procedure used to treat significant coronary artery disease. Nevertheless, unfavorable cardiovascular events and complications, including cardiac arrhythmias may be observed in patients after CABG. Previous studies have revealed a relationship between risk of cardiac arrhythmias and abnormal heart rate variability (HRV), which reflects adverse alterations in cardiac autonomic functioning, that may occur in patients after a CABG procedure. The aim of this article was to provide a systematic review of the major research findings in this area. Methods: A literature search was carried out using PubMed, Cochrane, and Embase databases and relevant articles, published in English, were analyzed in detail. Results: Studies performed so far have shown time depending changes in HRV after CABG. Time and frequency domain HRV decrease acutely after CABG but recover almost completely to pre-operative values by 6 months after surgery. Some preoperative clinical states such as: heart failure, type 2 diabetes mellitus and depression adversely affect post-CABG HRV. Finally, post-CABG cardiac rehabilitation appears to improve exercise capacity and speed up recovery of HRV. Conclusions: Generally, traditional time and frequency domain HRV parameters fail to predict complications post-CABG. Altered non-linear measures of HRV may identify subgroups of subjects at increased risk of potential complications, including atrial fibrillation post-CABG. However, data available currently does not appear to unequivocally support the hypothesis that early HRV assessment in post-CABG patients predicts long-term mortality.
Coronary artery disease (CAD) is the third greatest cause of mortality in the
word. Coronary artery bypass grafting (CABG) surgery is one of the leading
therapeutic strategies for patients with significant CAD [1]. CABG surgery
reduces manifestations and improves prognosis, but unfavorable cardiovascular
events, including cardiac arrhythmias may be observed in post-CABG patients [1, 2, 3]. Importantly, changes in cardiac autonomic regulation are noted in these
patients both before and after surgery and may be assessed non-invasively using
heart rate variability (HRV) derived from a continuous electrocardiography (ECG)
recordings. HRV characterizes oscillations/variations in instantaneous
sinus heart rate and has been calculated using various mathematical algorithms
since its clinical value was first appreciated in 1963 [4, 5]. At that time, Hon
and Lee [6] recognized that fetal distress was preceded by changes in inter-beat
intervals recorded on fetal monitoring. Later it was discovered that 24-hr HRV
from a Holter recording is a powerful predictor of death following acute
myocardial infarction (MI) [7]. This stunning finding brought the potential for
Holter-based HRV to add to risk stratification to cardiology. Although the
original cut point of the standard deviation of normal-to-normal intervals (SDNN)
There are several approaches to the measurement of HRV, virtually all derived from the time series of normal to normal (NN) inter-beat intervals [8, 9, 10, 11]. Time domain methods describe statistical properties of the NN intervals and assess the amount of HRV seen during recordings that may range from 2-min to 24-hr or even more in duration [12]. Time domain HRV parameters can be categorized as: variables that came from evaluations of the NN intervals or instantaneous heart rate, and variables derived from the variations between successive NN intervals.
Frequency domain HRV parameters provide data regarding the difference in heart
rate patterns at different underlying frequencies (spectral analysis) and can
potentially be linked to physiologic rhythms [6]. Spectral analysis of HRV is
generally calculated using a Fast Fourier Transform (FFT) which assumes that the
total variance in the heart rate time series can be decomposed into underlying
oscillatory components, much like a single note played by an orchestra can be
decomposed into its underlying sounds. Spectral components derived from
short-term recordings (often 2–5 mins) and long-term recordings (24-hr) differ.
The distinction between long-term and short-term recordings is important and
often lost when a particular finding about HRV is promoted by investigators.
Short-term recordings, usually captured at rest, permit assessment of three main
spectral components of HRV: high frequency (HF) power, low frequency (LF) power,
and very low frequency (VLF) power [7, 13]. Longer-term recordings generally
capture HRV during real life activities, including sleep and permit the
estimation of the ultra-low frequency (ULF) power component (oscillations over an
every-20-minutes to every-24-hr-period and now, with more advanced technology,
even multi day recordings). Also, in order to meet the mathematical assumptions
underlying the FFT, that the signal being analyzed can be reproduced by an
appropriate combination of underlying, regular sine ways (referred to as
stationarity) [13] the VLF, LF and HF components, in a longer recording, are
calculated and averaged over shorter time periods. However, classic statistical
tests can be employed to check the stability of signals of spectral components
[14]. Recordings often include arrhythmic events, ectopic beats, missing data and
noise [14]. In general, these are handled by interpolating (splining) the missing
NN interval data so that they do not add non-physiologic variation in heart rate
and, for research purposes, limits on the percent of missing data for frequency
domain analyses are pre-specified. For example, for a 24-hr recording to be
accepted as providing a reasonable estimate of the subject’s frequency domain
HRV, each 5-min segment is required to have at least 80% NN intervals (i.e.,
In addition to statistical, time domain parameters and FFT-based frequency-domain HRV indices, the time series of NN intervals may also be represented by patterns which can be analyzed for HRV based on the geometric attributes of the resulting pattern. The geometric patterns can be fitted to a shape that is mathematically defined and the parameters of this shape can be used. These geometric shapes are classified into pattern-based categories such as linear, elliptical, triangular, which express various classes of HRV. The advantage of such geometrical methods is that they do not rely on the analytical quality of the series of NN intervals [16]. However, such methods do require a significant number of NN intervals to build a geometric model.
Heart rate turbulence (HRT), is not based on NNs but rather on the NN response to a single premature ventricular contraction (PVC), although it requires the presence of at least 5 qualifying PVCs on a recording for a reliable estimate [17]. This relatively novel index of HRV has the following elements: turbulence onset, the direct parasympathetically-mediated reaction of the heart rate to the decrease of cardiac output related to a premature ventricular contraction and turbulence slope, which reflects the slope of the return to baseline heart rate after a premature ventricular contraction and reflects the functional health of baroreflex (blood pressure response).
Nonlinear methods of HRV measurements supply data regarding the dynamics of
heart rate, not obvious with conventional methods of HRV analysis [16, 18].
Specifically, nonlinear HRV captures the relative organization vs.
randomness of the heart rate patterns at different scales. One is approximate
entropy (ApEn), which is derived from the logarithmic probability that data
points which are like each other will remain close over gradual comparisons.
Greater uniformity is related to smaller values of ApEn and abnormality with
larger ApEn, in other words, it is a measure of randomness or disorder in a
system [19]. Another commonly-used non-linear HRV measure is detrended
fluctuation analysis (DFA). DFA has two components: DFA
In this systematic review, we summarize findings regarding the clinical significance of what is known about HRV before and after CABG.
This literature review was conducted consistent with Preferred Reporting Items for Systematic Reviews and Meta–Analyses (PRISMA) guidelines [21]. The following keywords were used to find relevant papers in PubMed, Cochrane and Embase: “CABG” or “Coronary Artery Bypass Surgery” and “HRV” or “heart rate variability”. Only English language papers found using these keywords and published between the years 1991 and 2022 were checked for importance to the topic. First duplicates were ruled out, and then the initial eligibility evaluation was performed based on titles and abstracts. Afterwards the full texts were checked for eligibility. First, only papers evaluating HRV changes after CABG when comparing with pre-operative HRV status were included in this review. In the second step, papers assessing HRV as a predictor of outcomes after CABG were chosen. Finally, the studies investigating use of HRV to assess effects of post-CABG rehabilitation were included in this analysis. Papers describing potential confounding factors or pre-clinical states and their effect on HRV post-CABG were excluded to preserve focus, but they are summarized briefly in the last paragraphs. The detailed research process is shown in the flow diagram presented in Fig. 1. The latest search was performed on 20 August 2022.
Flow diagram of research strategy. Abbreviations: CABG, coronary artery bypass grafting; HRV, heart rate variability.
Finally, N = 26 articles were identified for this review. These were categorized into five groups based on their research question (Fig. 1). These five groups were: (1) Acute Effects of CABG on HRV; (2) Longer-term Effects of CABG on HRV; (3) Pre-CABG and post-CABG HRV as predictors of clinical course after CABG; (4) Effects of CABG on the usefulness of HRV as a predictor of mortality after surgery; and (5) Effects of interventions on HRV post-CABG. Additionally, we have described pre-operative clinical conditions and their influence on HRV and confounding factors related to the assessment of the effect of CABG on HRV. Table 1 describes conventional time domain HRV indices, most of which were used in the studies included in the current review, while frequency domain HRV parameters, used in analyzed studies are depicted in Table 2. Table 3 shows examples of HRV nonlinear parameters.
Parameter | Definition |
IBI (ms) | Interbeat interval: The interval in ms between successive heartbeats. |
RR intervals (ms) | Times between all successive heartbeats. |
NN intervals (ms) | Times between normal sinus beats, from which artifacts have been removed. |
SDNN (ms) | Standard deviation of the interbeat interval of normal sinus beats. |
SDANN (ms) | Standard deviation of the average normal-to-normal intervals calculated over 5-minute intervals. |
SDRR (ms) | Standard deviation of RR intervals. |
SDNN index (ms) | Mean of the standard deviation of all the normal-to-normal intervals for each 5 min segment of a 24-hr Holter. |
pNN50 (%) | Proportion of NN50 divided by the total number of NN (RR) intervals in %. |
rMSSD (ms) | Root mean square of successive NN interval differences. |
HRV triangular index* | The integral of the density distribution (i.e., the number of all NN intervals) divided by the maximum of the density distribution. |
TINN (ms)* | Basic width of the RR interval histogram. |
*Geometrical measure.
Parameter | Definition |
ULF power (ms |
Ultra-low frequency spectral power ( |
VLF power (ms |
Very–low frequency spectral power (0.0033–0.04 Hz)*. |
LF power (ms |
Low frequency spectral power (0.04–0.15 Hz)*. |
nLF power (nu) | Relative power of the LF band (0.04–0.15 Hz) compared to the combined LF+HF band in normalized units. |
LF power (%) | Relative power of the LF band. |
HF power (ms |
High frequency spectral power (0.15–0.4 Hz)*. |
nHF power (nu) | Relative power of the HF band (0.15–0.4 Hz) in normalized units. |
HF power (%) | Relative power of the HF band. |
LF/HF ratio | Ratio of low frequency power to high frequency power. |
*Generally, ln transformed to normalize distributions and permit parametric statistical comparisons.
Indices | Definition |
SD1 (ms) | Standard deviation perpendicular to the line-of-identity in Poincaré plot*. |
SD2 (ms) | Standard deviation along the line-of-identity in Poincaré plot*. |
SD1/SD2 | Ratio between SD1/SD2. |
SampEn | Sample entropy assessing time series regularity and complexity. |
ApEn | Approximate entropy assessing time series regularity and complexity. |
MSE | Multiscale entropy evaluate the complexity of a time series by quantifying its entropy over a range of temporal scales. |
DFA |
Detrended fluctuation analysis evaluating short-term fluctuations. |
Shannon entropy | Measures the average information provided by a set of events and proves its uncertainty; quantifying the complexity of physiological signals. |
DFA |
Detrended fluctuation analysis evaluating long-term fluctuations. |
D2 | Correlation dimension estimates the minimum number of dynamic variables needed to model the underlying system. |
SymDyn | Symbolic dynamics reflects changes in cardiac autonomic modulations on short time scales in spite of the considerable reduction of information involved. |
Beta slope is evaluated from the power-law analysis of 24-hour HRV; in healthy subjects, the |
* Poincaré plot - a scatter plot where each IBI (IBIn) is plotted against the subsequent IBI (IBIn+1).
Decreased HRV has been shown to be a predictor of death in CAD patients [22]. CABG improves prognosis in this group of patients [23], however little is known about the effect of the procedure on HRV. Although CABG leads to a positive clinical outcome, it is unclear whether CABG surgery simultaneously improves HRV. Published studies of the short-term changes in HRV post-CABG are listed in Table 4 (Ref. [18, 24, 25, 26, 27, 28, 29, 30]). In addition, some of these studies recorded HRV changes beyond 6 weeks post-CABG, these results are discussed in the section following this one.
Study | Patients | HRV parameters | Results |
Soares et al. 2005 [24] | After CABG (N = 13). | Mean RR, rMSSD, SDNN, SDANN, pNN50, TP, LF, HF, LF/HF ratio. | Both time-domain indexes of HRV and TP, and HF power dropped following CABG operation, and recovered 15, 30 or 60 days after CABG. LF and the LF/HF ratio increased early after CABG, then decreased to the pre-CABG levels. |
Control groups (patients with CAD who refused operation [N = 9], and healthy patients [N = 9]). | 15-min recordings of paced breathing at 18 cycles/min. | ||
Ksela et al. 2015 [25] | Patients undergoing off-pump CABG (N = 49). | rMSSD, SDNN, SDANN, pNN50, HRV TI, TP, HF, LF, LF/HF ratio. | All observed linear HRV parameters significantly declined 7 days after the surgical procedure, when compared with HRV before surgery. |
24-hr Holter recordings. | |||
Demirel et al. 2002 [26] | Patients with CAD undergoing CABG (N = 12). | pNN50, Mean NN, SDANN, SD, rMSSD, SDNN, TP, HF, LF, LF/HF ratio. | All HRV parameters significantly decreased 1 week after CABG. |
24-hr Holter recordings. | |||
Thanh et al. 2022 [27] | Patients with stable CAD (N = 119). | SDNN, rMSSD, SDANN, SDNN index (ASDNN), pNN50, HF, VLF, LF, LF/HF ratio. | Almost all HRV parameters were lower 7 days after the surgery. Only, the LF/HF ratio was unchanged. |
24-hr Holter recordings. | |||
Bellwon et al. 1996 [30] | Patients undergoing CABG (N = 34). | TP, HF, VLF, LF, LF/HF ratio, and %HF. | Before surgery TP, HF, VLF, LF, and %HF dropped after standing up. LF/HF ratio and %LF increased after stand up. After the operation changes described above were no longer observed. Only an increase of HF and %HF in standing position was significant. |
Measured during 10-min periods. Patients were breathing at a controlled rate of 0.25 Hz during the ECG recording. | |||
Kalisnik et al. 2006 [29] | Patients electively admitted for off-pump CABG (N = 42). | TP, LF, HF, nLF, nHF. | HRV measures remained depressed 4 weeks after the procedure. |
10-min ECG recordings. | |||
Komatsu et al. 1997 [28] | N = 10 CABG patients. | Mean RR, LF, HF, LF/HF ratio. | Post-operative changes in cardiac autonomic control with incomplete improvement during the first month after CABG. |
30-min recordings with RR intervals measured with an accuracy of 1 ms. | |||
Laitio et al. 2006 [18] | N = 26 CABG patients (N = 17 complete case included in statistical analysis). | SDNN, ULF, VLF, LF, HF, DFA |
HF, LF, ULF, VLF dropped after CABG and were decreased six weeks, six months and twelve months after CABG. DFA |
24-hr Holter recordings. |
Abbreviations: ApEn, approximate entropy assessing time series regularity and
complexity; CABG, coronary artery bypass grafting; CAD, coronary artery disease;
DFA
All studies reported a decrease in traditional HRV parameters immediately after surgery, reaching their lowest value at 3–6 days post-CABG and remaining depressed even 6-weeks follow up after surgery [24, 25, 26, 27, 28, 29]. Thanh et al. [27] showed that decreased HRV before surgery was present in 28.6% of patients in 51.8% after seven days, 19.6% after three months, and 12.7% after six months. However, in contrast the finding of Bellwon et al. [30] showed that HF and percent of power in the HF band (%HF) increased significantly at 6-weeks post-CABG while in a standing position, but results of this study must be interpreted with caution, because of the brevity of the data- recording time (10 minutes) and because it is not clear that the organization of the HRV signal in the HF power was assessed and that makes it impossible to determine if this increase in HRV reflected a more disorganized cardiac autonomic functioning which has been called “erratic sinus rhythm” [31].
Summary of results: HRV decreases acutely after CABG. A study comparing
HRV in patients undergoing uncomplicated CABG surgery with patients undergoing
non-thoracic vascular surgery found that HRV indices were 40–50% lower in the
CABG patients, suggesting that CABG itself reduces HRV [26]. Decreases
in HRV
It has now been established, as described in the first section of this review, that HRV decreases acutely after CABG [24, 25, 26, 27, 28, 29]. However, the effect on HRV beyond 6-weeks post-CABG is still unclear. N = 4 studies, listed in Table 5 (Ref. [18, 24, 26, 27]), suggest a recovery of HRV after the initial drop. Demirel et al. [26] reported, in N = 12 patients, that standard HRV indices, which decreased after surgery, came back to pre-operative levels after CABG and then improved during the 1-year follow-up. At the 3-year follow-up, all HRV indices recovered to normal values. Soares et al. [24], in their study had a CABG group (N = 13) and control groups (patients who refused the operation (N = 9), and healthy subjects (N = 9). HRV was recorded during a 15-min period of paced breathing at 18 cycles/min one day before and 3, 6, 15, 30, 60, and 90 days after surgery. They found that paced-breathing-based HRV reached its lowest value three to six days after CABG and returned to its pre-operative levels at about 2 months post-CABG. Thanh et al. [27] demonstrated that, 6 months after surgery, the reperfusion and majority of HRV parameters reflecting injury to the ANS had improved.
Study | Patients | HRV parameters | Results |
Soares et al. 2005 [24] | After CABG (N = 13). | rMSSD, Mean RR, SDNN, SDANN, pNN50 (%), TP, LF, HF, LF/HF ratio. | Both time-domain indexes of HRV and TP, and HF power recover by 15, 30 or 60 days after CABG. |
Control groups (patients with CAD who refused operation [N = 9], and healthy patients [N = 9]. | 15- min recordings of paced breathing at 18 cycles/min. | LF and the LF/HF ratio increased early after CABG, then decreased to the levels before CABG. | |
Demirel et al. 2002 [26] | Patients with CAD undergoing CABG (N = 12). | Mean NN, SDANN, SD, rMSSD, SDNN, pNN50, TP, LF, HF, LF/HF ratio. | HRV indices decreased after CABG, and majority of them recover three months after surgery (only mean NN, rMSSD, and pNN50 were still decreased). LF/HF ratio increased after three months and continued to increase towards pre-operative values six months after CABG. |
24-hr Holter recordings. | |||
Thanh et al. 2022 [27] | Patients with stable CAD (N = 119). | SDANN, SDNN, SDNN index (ASDNN), rMSSD, pNN50, HF, VLF, LF, LF/HF ratio. | HRV indices recover three months after CABG. The percentage of decreased HRV before CABG was 28.6%, 51.8% after seven days, 19.6% after three months, and 12.7% after six months. AS-DNN and SDNN changed the most. |
24-hr Holter recordings. | |||
Laitio et al. 2006 [18] | N = 26 CABG patients (N = 17 complete case included in statistical analysis). | SDNN, ULF, VLF, LF, HF, DFA |
HF, LF, ULF, VLF power dropped after CABG and were decreased six weeks, six months and twelve months after CABG. DFA |
24-hr Holter recordings. |
Abbreviations: ApEn, approximate entropy assessing time series regularity and
complexity; CABG, coronary artery bypass graft; CAD, coronary artery disease;
DFA
Summary of results: The report of a later recovery of HRV to levels
like those found in healthy subjects, regardless of the pre-operative status of
the subjects and their post-operative clinical condition, suggest that decrease
in HRV parameters
Atrial fibrillation (AF) is the most common complication after CABG and is related to a greater risk of heart failure, in-hospital stroke, longer hospital stay and rehospitalization [24, 36, 37, 38, 39]. Importantly, it has been shown that patients at high risk for AF may be recognized using HRV. However, assessment of relationships between HRV and AF risk have been limited to time or frequency domain parameters [40, 41]. Studies (N = 7) describing HRV as a predictor of complications including AF post-CABG are shown in Table 6 (Ref. [40, 42, 43, 44, 45, 46, 47]).
Study | Patients | HRV parameters | Results |
Hogue et al. 1998 [40] | Patients with post-operative AF (N = 18) and postoperative control patients without AF (N = 18). | Mean NN, SDNN, pNN50, rMSSD, SD1, SD2, SD1/SD2, ApEn. | HRV indices did not differ between patients and controls. |
Holter recorders for two to three days after CABG. | Decreased ApEn and increased heart rate were related to incident AF. | ||
HRV analysis in 3 sequential 20-minute intervals preceding the beginning of post-operative AF. | |||
Tarkiainen et al. 2008 [42] | CABG patients (N = 67) of whom N = 48 patients did not have and N = 19 had an AF after CABG. | Mean NN, DFA |
Preoperative DFA |
ECG recordings as follow: | |||
(1) 10-min in supine position with spontaneous breathing. | |||
(2) 10-min in supine position with controlled breathing. | |||
(3) 2-minute in supine position with normal breathing, then 10-min a passive tilt at 70 degree angle. | |||
Park et al. 2014 [43] | CABG patients (N = 113). | HRT onset and power law slope. | Pre-operative abnormal HRT was related to poor short-term and long-term outcomes after operation. |
Ambulatory ECGs recordings 1 to 3 days before CABG. | |||
Hakala et al. 2002 [44] | Elective CABG patients (N = 92). | SDNN, rMSSD, TP, HF, VLF, LF, LF/HF ratio, nLF, nHF. | No measured HRV parameters differed significantly between AF and sinus rhythm patients. |
Continuous ECG recording as follows: | |||
(1) 10-min in supine position with spontaneous breathing. | |||
(2) 10-min ECG recordings in supine position with controlled breathing. | |||
(3) 2-minute in supine position with normal breathing, then 10-min passive tilt at 70 degree angle. | |||
Chamchad et al. 2011 [45] | Patients undergoing off-pump CABG (N = 50). | LF FFT%, HF FFT%, LF/HF, Mean NN, SDNN, rMSSD, NN50, pNN50, SDNN index, TINN, SD1, SD2, SD1/SD2, pPD2, mPD2, entropy. | Decreased LF/HF ratio pre-operatively was related to occurrence of AF after CABG. |
A 10-min ECG recording. | |||
Laitio et al. 2000 [46] | Elective CABG surgery patients (N = 40) | ApEn, SD1, SD2, SDl/SD2, DFA |
DFA |
Pre-operative ECG 12 to 40-hr before CABG in 20 patients. | |||
Post-operative ECG after CABG recorded for 48-hr. | |||
Wu et al. 2005 [47] | CABG patients (N = 86) | SDNN, LF, HF, VLF, DFA |
DFA |
24-hr ECG Holter recordings collected before CABG and 24-hr during the 1st post-operative day. | Patients with post-operative prolonged intensive care unit stay, taking inotropes, and having AF had decreased pre-operative and post-operative DFA |
Abbreviations: ApEn, approximate entropy assessing time series regularity and
complexity; AF, atrial fibrillation; CABG, coronary artery bypass graft; DFA
Research by Hogue et al. [40] based on post-CABG inpatient ECG
monitoring and analyzing HRV indices during the hour before the onset of AF,
showed that subjects who had AF after surgery had diminished heart rate
complexity and more frequent atrial ectopy before the arrhythmia started. They
also found that lower ApEn and greater heart rate were independently related to
AF. Similarly, another study by Tarkiainen et al. [42] found that those
who developed AF post-CABG had consistently reduced pre-operative
DFA
In contrast, two other studies found that HRV analysis provided little value in predicting AF after CABG. Hakala et al. [44] concluded that the subjects at risk of AF development after surgery could not be recognized with used pre-operative short-term HRV parameters. In their study, assessed HRV indices did not differ between AF and sinus rhythm patients. However, they found that older age and higher body mass index were predictors of AF after surgery. Also, Chamchad et al. [45] in a study that involved 50 patients who underwent off-pump CABG, concluded that nonlinear HRV parameters may provide minimal value in prediction of AF after cardiac operation. In their study only the LF/HF ratio was decreased pre-operatively in subjects with new onset AF after CABG when compared to subjects without AF. Nevertheless, a more sophisticated real-time analysis of HRV from intensive care unit (ICU) ECG signals, as in the Hogue et al. [40] paper cited above may provide a powerful additional tool in identifying at-risk patients and permit timely preventive interventions.
Post-surgical HRV has also been explored as a predictor of length of stay in the
ICU. Laitio et al. [46] assessed post-operative states of patients
throughout their ICU stay. In this study, continuous 24-hr ECG recordings were
obtained in 40 adult patients after CABG. They divided the patients into two
groups, by length of stay in the ICU: Group A included 33 patients with (ICU stay
Wu et al. [47], in their study, randomized 86 CABG patients into a
control and an ischemic preconditioning group (IP). The IP group had induced
2-min ischemia and 3-min reperfusion periods. 24-hr ECG recordings were done both
before and after CABG. They found that both standard and nonlinear HRV
indices, i.e., DFA
Summary of results: From the studies cited above, we can conclude that nonlinear HRV parameters may be better in detection of subtle abnormalities in cardiac autonomic control that traditional HRV indices and may give improved prediction of post-operative MI and post-operative AF in subjects after CABG [33, 34, 42, 46, 47]. Even though the precise mechanism is yet unclear, ischemic preconditioning appears to be effective in improving cardiac performance, decreasing cardiac troponin T levels, decreasing incidence of post-operative arrhythmias and improving post-operative outcome [44, 48, 49]. Although, the clinical use of nonlinear measures of HRV for risk assessment in patients undergoing major cardiac surgery needs to be further confirmed in prospective studies with larger samples, and the use of AI technologies could uncover additional measures, we believe that HRV analysis has shown promise of becoming a clinically useful new prognostic tool for post-operative clinical complications in the assessment of patients scheduled to undergo major cardiac surgeries. Further studies could combine HRV, clinical factors, ECG, peripheral blood biomarkers and/or myocardial properties to improve prediction of adverse clinical outcomes after CABG, as in other cardiovascular diseases [50, 51, 52, 53, 54]. Interestingly, progress in AF detection tools and methods, including wearable and mobile devices may be crucial also for CABG patients [55, 56, 57]. To study the agreement of ECG and photoplethysmographic signals (PPG)-derived HRV, increasingly available on personal devices like smart watches, Chen et al. [58] compared their agreement in patients over 1 year after CABG and demonstrated that pulse rate variability measures, might potentially be able to be useful in assessment the ANS regulation of patients after CABG because of the good agreement between the smart watch and ECG-derived for the majority of standard HRV parameters.
Multiple studies have demonstrated that CABG results in an acute decrease in HRV [26, 27, 59, 60]. Lower HRV is also seen in patients undergoing other cardiac surgery, e.g., valve reparation [61]. The probable reasons for the decrease in HRV after surgery include: a surgical intervention on the heart and adjacent structures, extended anesthesia, and cardioplegia. Importantly, as mentioned above, anxiety about the danger and pain of the surgery is likely also to contribute to the reduction in HRV [29]. Studies (N = 3) describing HRV as a predictor of mortality post-CABG are described in Table 7 (Ref. [62, 63, 64]).
Study | Studied group | HRV parameters | Results |
Milicevic et al. 2004 [62] | N = 175 subjects with decreased HRV after CABG (N = 51) or MI (N = 124). | SDNN. | HRV was lower in CABG subjects than in post-MI group. However, prognosis was better in the operation group than in the post-MI patients. |
24-hr Holter recordings (3 weeks to 3 months after CABG). | |||
Stein et al. 2004 [63] | Participants in Cardiac Arrhythmia Suppression Trial (CAST, N = 735). | SDNN, SDANN, SDNN index, TP, VLF, ULF, LF, nLF, HF, nHF, LF/HF ratio, rMSSD, pNN50, pNN625. | Eliminating post-CABG and diabetic subjects improved the relationship of HRV with risk of death. |
24-hr Holter recordings from the CAST (71 |
|||
Lakusic et al. 2013 [64] | N = 206 CABG patients. | Mean RR, SDNN, SDNNindex, SDANN, rMSSD, pNN50, TP, VLF, HF, LF, LF/HF ratio. | Subjects with decreased HRV (SDNN) after CABG have worse cardiovascular survival rate than subjects with normal HRV. |
24-hr Holter recordings (3.7 |
Abbreviations: CABG, coronary artery bypass graft; HF, high frequency; hr, hour; HRV, heart rate variability; LF, low frequency; MI, myocardial infarction; nHF, normalized high frequency; nLF, normalized low frequency; pNN50, proportion of NN50 divided by the total number of NN (RR) intervals; pNN625, percentage of successive RR intervals that differ by more than 6.25%; rMSSD, root mean square of successive NN interval differences; SDNN, standard deviation of the interbeat interval of normal sinus beats; SDANN, standard deviation of the average normal-to-normal intervals calculated over 5-minute intervals; TP, total power; ULF, ultra-low frequency spectral power; VLF, very–low frequency spectral power.
Kalisnik et al. [29] concluded that changes in HRV parameters after
off-pump CABG are related to adrenergic mobilization which is similar to that
seen in on-pump surgeries. Lakusic et al. [61] demonstrated that in
long-term follow up after CABG surgery HRV parameters were similar both in
patients undergoing CABG off-pump and on-pump. Some studies have demonstrated
that decreased HRV has no prognostic value in CABG patients, unlike in post-MI
patients [62, 63, 65, 66]. Stein et al. [63] concluded that excluding post-CABG
and diabetes mellitus subjects from HRV analysis strengthens the association
between decreased HRV and risk of death. On the other hand, the recent study
performed by Lakusic et al. [64] compared differences in mortality in
patients after CABG with normal vs. decreased post-operative HRV. In
their study, 24-hr Holter ECGs were obtained on all patients. Unlike some prior
reports, results indicated that decreased post-operative SDNN was related to
higher risk of death in CABG patients. However, it should be mentioned that
patients with diminished HRV after CABG had lower left ventricular ejection
fractions, smaller functional capacity and had more bypassed vessels (meaning
that a higher degree of CAD, longer operation, and duration of cardioplegia) than
patients with normal HRV. All of this may be related to decreased HRV per
se. Moreover, one of the limitations of this study is that the HRV was not
measured before and early after operation (the mean time from the CABG to
measuring HRV was 3.7
Summary of results: It has been shown, using both short-term and 24-hr based measures that, in most subjects, HRV declined early after cardiac operation and recovered after a few months of surgery. However, to establish whether diminished post-operative HRV after CABG has prognostic implications, additional studies in a larger, well-characterized sample of subjects are needed. Lakusic et al. [64], as do other investigators, emphasized that patients with diminished HRV observed during long term follow-up after CABG should have access to long-term monitoring, diagnostic procedures, and appropriate treatment such as angiotensin-converting enzyme inhibitors (ACE-I), amiodarone and ivabradine [67, 68, 69].
Patients are exposed to autonomic dysfunction early after their operations, and this makes them more prone to early post-operative complications [26]. Increased parasympathetic tone has been shown to prevent cardiac arrhythmias [70, 71]. Patients with CAD who have decreased parasympathetic nervous activity are at risk of sudden cardiac death, making parasympathetic nervous activity (PNA) a marker with prognostic value [71]. Improvements in measures of parasympathetic functioning have been associated with lower cardiovascular risk [72]. However, sympathetic nervous activity may reflect the severity of heart failure and has prognostic value as well [73]. It has been shown that soon after surgery, sympathetic nervous activity (measured as plasma norepinephrine concentrations) recovers but parasympathetic nervous activity recovery (measured as HF power) lags it [74]. Studies have demonstrated that long-term outpatient cardiac rehabilitation (CR) can modify HRV in a favorable way in patients post-CABG [74, 75, 76, 77, 78]. CR has been shown to have positive effects on subjects with decreased parasympathetic tone before the beginning of rehabilitation post-CABG [78]. Exercise training induces adaptations in the autonomic demand needed during exercise. This adaptation occurs for central as well as peripheral neural pathways [75, 79]. Rehabilitation is aimed at prevention of complications post-CABG, shortening the length of hospital stay and to motivate patients to continue rehabilitation programs in an outpatient setting [80, 81]. Reviewing the scientific evidence today is not possible to sustain that CR as no role after CABG [82]. One study has shown that a short-term CR program, also significantly improves autonomic cardiac regulation at hospital discharge [80]. Amjadian et al. [83] demonstrated that both religious practice (Islamic and Qur’an), accompanied by doing homework, and rehabilitation at home with HRV biofeedback therapies, may be useful in subjects after cardiac surgery. It was also shown that various CR programs impacted cardiac autonomic control and length of hospital stay in subjects after surgery [80]. Some studies have shown no reduction in HRV attenuation in subjects after surgery who did not have any CR. Another study attributed this finding in part to prolonged bed rest, warranting early mobilization after CABG [84]. The intensity of the exercise regimen being administered has also been shown to impacts HRV. In one study [84] patients were randomized to an intensive training regimen post-CABG, characterized by supervised CR for one hour, three times a week and a home bicycle program for another three days a week for three months. Results showed that maximal workload capacity increased significantly and persisted one year later. SDNN and SDANN increased significantly, also persisting one year after the CR. A meta-analysis conducted by Kushwaha et al. [85] confirmed and reinforced the finding that exercise training improved selected HRV indices, e.g., rMSSD, SDNN, HF power, and LF/HF ratio. Studies (N = 8) describing use of HRV to assess effects of post-CABG interventions are shown in Table 8 (Ref. [74, 75, 76, 77, 78, 80, 81, 84]).
Study | Studied group | HRV parameters | Intervention | Results |
Jelinek et al. 2013 [75] | N = 38 patients, (N = 22 after PCI and N = 16 after CABG). | rMSSD, SDNN, LF, HF, LF/HF ratio. | A 6-week cardiac rehabilitation program both in PCI and CABG patients. | Cardiac rehabilitation, especially in CABG patients improved exercise capacity, cardiorespiratory function and cardiac autonomic regulation. |
A 20-min ECG recording. | ||||
Iellamo et al. 2000 [76] | N = 86 patients after CABG randomized into trained (N = 45) control (N = 41) groups. | rMSSD. | Exercise at 85% of maximum heart rate (two daily sessions six times a week for two weeks). | Exercise training increases HRV in CABG patients. |
A 10-min ECG recording. | ||||
Mendes et al. 2010 [81] | N = 47 patients eligible for CABG (N = 24 EG and N = 23 physiotherapy UCG). | rMSSD, HF, SD1, SD2, DFA |
EG had early mobilization with progressive exercises plus usual care. | EG had higher rMSSD, HF, SD1, SD2, DFA |
Only respiratory exercises in UCG. | Increased mean HR, LF, and the LF/HF were found in the UCG. | |||
A 10-min ECG recording in a sitting position. | ||||
Ribeiro et al. 2020 [80] | N = 48 CABG patients CG, EMG, or VRG. | Mean RR, rMSSD, SDNN, LF, HF, LF/HF ratio, SD1, SD2, Shannon’s Entropy. | CG—respiratory and metabolic exercises. EMG—cycle ergometer exercises and ambulation. | CG showed a decline in SDNN, rMSSD, and SD1 after CABG. The EMG and VRG had a better cardiac autonomic regulation. |
A 10-min ECG recording. | VRG—cycle ergometer exercises and ambulation with the addition of two Nintendo Wii games during three post-operative days. | |||
Tygesen et al. 2001 [84] | N = 62 patients (43 MI and 19 CABG) randomized to physical training 2 or 6 times per week for three months. | rMSSD, SDNN, SDANN, pNN50, LF, HF, LF/HF ratio. | Bicycle exercise and 24-hr Holter 1, 4 and 12 months after MI or CABG. | SDANN and SDNN increased more in group training 6 times per week. |
24-hr Holter recordings. | HRV changes were more significant in CABG patients. | |||
Bilińska et al. 2013 [77] | N = 100 patients after 3 months of CABG (N = 50 EG; N = 50 CG). | SDNN, LF, HF, LF/HF. | Six weeks cycling, 60 min three times per week at 70–80% of HRmax. | Increased SDNN and a tendency towards an increase HF power in exercise group. |
A 24-hr Holter recording. | ||||
Ghardashi-Afousi et al. 2018 [78] | N = 42 patients after |
SDANN, rMSSD, LF, HF and LF/HF. | LV-HIIT: 10 intervals of 2 min at 85 to 95% of HRmax and separated by 2 min at 50% HRmax for 3/week for 6 weeks. MICT: 40 min running on a treadmill at 70% of HRmax 3/week for 6 weeks. | Higher R-R interval, SDRR, rMSSD, and HF power, and lower LF power and LF/HF ratio in patients after operation in LV-HIIT group. |
A 24-hr Holter recording. | ||||
Takeyama et al. 2000 [74] | CABG patients (N = 28; N = 13 exercise group and N = 15 controls). | HF power. | 30-min cycling 2/day for 2 weeks in exercise group. Firstly walk 200 m 3/day and then 500 m within 2 weeks in controls. | Increased HF power after three months in exercise group (both at rest and during exercise). |
There are no details about the time of ECG recording (they analyzed 3-min of HRV data). |
Abbreviations: ApEn, approximate entropy assessing time series regularity and
complexity; EG, exercise group; CG, control group; UCG, usual care group; EMG, early mobilization group; VRG, virtual reality group; CABG, coronary artery bypass graft; DFA
Summary of results: Different kinds of CR improve cardiac autonomic control in patients after CABG. However, in the analyzed studies, there are some methodological discrepancies regarding assessment of HRV, therefore future studies are needed to clarify HRV changes reflecting autonomic adaptation after exercise training in CABG patients and identify risk factors that prevent successful adaptation.
CABG improved the quality of life of CAD patients [86]. Nevertheless, about 6–46% of these subjects have been reported to have been exposed to psychological distress and complication related to it [87, 88]. Hallas et al. [89] in their study recruited 22 patients undergoing elective CABG for the first time and used the Hospital anxiety and Depression Scale (HAD), the Global Mood Scale (GMS) and the Dispositional Resilience Scale (DRI). Twelve-hour ECG recordings were obtained one week before surgery and two months after CABG, and HRV was assessed. They found that about 40% of subjects were anxious and depressed before their operation, but only 27% after CABG. Depression was the best predictor of reduced HRV assessed both before and after surgery.
Summary of results: Depression is related to decreased HRV. It is important to also recognize the role of psychological factors in prognosis after CABG operation and in non-emergent cases, tools to manage this distress might be beneficial to outcome.
As stated in sections 3.1 and 3.2, HRV decreases acutely after CABG and later recovers to pre-operative values. However, the process of undergoing CABG presents many confounding factors that might affect HRV beyond the effect of CABG itself. These confounding factors can be split into three general categories: (1) the perioperative medical management of CABG patients, (2) the process of undergoing cardiac surgery, and (3) characteristics of CABG patients that might affect HRV.
The goal of perioperative medical management of CABG is to minimize perioperative and short-term complications. According to the most recent AHA guidelines [90], medical management should include: insulin infusion, beta blockers and amiodarone. Insulin infusion is indicated to keep blood glucose levels under 180 mg/dL and reduce sternal wound infection. Glycemic control has been shown to improve HRV. In a larger cross-sectional investigation, higher fasting glucose was associated with lower time-domain measures of HRV (RR, rMSSD, SDNN) [91], and in a double-blind randomized controlled trial (RCT) with healthy individuals, manipulation of glucose levels outside the 70–90 mg/dL range caused decreases in SDNN, rMSSD and pNN50 [92].
In addition to insulin infusion, perioperative medical management for CABG also includes preoperative beta-blocker therapy since beta-blockers significantly reduce the risk of AF after surgery [93]. There is general agreement, in the literature, that beta-blockers improve both time-domain and frequency-domain measures of HRV in patients [93]. For example, in a large retrospective study of post-MI patients, use of beta-blockers was related to several increased time-domain HRV parameters (average NN, SDNN, SDANN and SDIDX) [65]. In subjects with decompensated heart failure, beta-blockers were associated with better frequency-domain HRV parameters (total power and ULF) [65, 94]. An experimental study with a rat model of chronic heart failure also reported similar findings, with atenolol and pindolol increasing both LF and HF power [95]. This study revealed augmentation of HRV with the use of beta-blockers supporting their use in patients with heart failure [95, 96]. Another element of perioperative medical management is amiodarone, which also reduces the risk of post-operative AF. There are few studies exploring the impact of amiodarone on HRV, but one experimental study found that amiodarone decreases frequency-domain measures (total power, LF power, LF/HF ratio) in a rabbit model [97].
Finally, traditionally, patients were given aspirin after surgery, to prevent graft occlusion and adverse cardiac events after CABG. The latest AHA guidelines recommend the use of dual antiplatelet therapy (DAPT) up to one year after CABG. However, available evidence is limited to small RCTs and the choice between the use of aspirin vs. DAPT remains unclear in CABG [98]. There are two RCTs that characterize the effect of aspirin on HRV, both done on healthy participants: one found that aspirin leads to higher HF and lower LF [99], while another found that aspirin does not have a significant effect on rMSSD [98], although the second may be underpowered. In another RCT, clopidogrel and ticagrelor did not have significant effects on either time- or frequency-domain measures of HRV [100].
Although HRV might be affected by perioperative medical management, it is also affected by the stress of the cardiovascular surgery process itself. For example, anesthetics generally depress both time- and frequency-domain measures of HRV, although different types of anesthetics have different effects. Inhaled anesthetics have been demonstrated to increase HR and decrease HRV (SDNN, LF and HF) [101], while the benzodiazepine remimazolam has been shown to have no effect on LF and HF [102]. Propofol has been shown to decrease HF across two studies, although it is unclear whether it has an effect on LF: one study concludes that it increases LF [102], while another does not find an effect [103]. Another IV anesthetic, thiopental, has similarly been shown to decrease HF and increase LF [104]. The choice of cardioplegia also has an impact on HRV. In an experimental study, warm blood cardioplegia led to significantly higher total power, LF and HF compared to cold crystalloid cardioplegia, although both predictably caused a decrease in HRV [104]. There was also a difference between on-pump versus off-pump CABG: with HRV measured one week and one month. Off-pump CABG led to significantly higher total power, LF and HF compared to on-pump [105], although after a mean of 3.7 months of follow up, there was no difference in HRV by surgery type [106].
There are potentially confounding factors associated with clinical
characteristics in CABG patients that need to be considered when assessing
changes in HRV parameters. In patients with CAD different HRV changes were
observed among subjects with left ventricular ejection fraction (LVEF) over 50%,
between 40–50% and below 40% [107]. Differences included normalized HF and VLF
as well as LF peak during 5:00–6:00 and 18:00–19:00 [107]. The at-risk group
with LVEF
Summary of results: HRV can be affected by perioperative medical management (insulin infusion, beta-blockers and amiodarone), the cardiovascular surgery itself (anesthetics, cardioplegia) and clinical characteristics of the patients.
Clinical states such as: heart failure, type 2 diabetes, and depression, adversely affect post-CABG HRV. HRV decreases acutely after CABG but recovers almost completely to its pre-operative value by 6 months after surgery. Traditional time and frequency domain HRV parameters generally fail to predict complications post-CABG, but more abnormal non-linear measures of HRV may identify subgroups of subjects at increased risk of potential complications, including AF, after operation. However, data available currently does not appear to unequivocally support the hypothesis that early HRV assessment in post-CABG patients predicts long-term mortality. Finally, post-CABG cardiac rehabilitation appears to improve exercise capacity and speed up recovery of HRV.
PSM: Data curation, Formal analysis, Investigation, Methodology, Resources, Validation, Visualization, Writing - original draft, review & editing; OA: Data curation, Formal analysis, Investigation, Methodology, Resources, Validation, Visualization, Writing - original draft, review & editing; MRH: Data curation, Formal analysis, Investigation, Methodology, Resources, Validation, Visualization, Writing - original draft, review & editing; MAkr: Data curation, Formal analysis, Investigation, Methodology, Resources, Validation, Visualization, Writing - original draft, review & editing; PTM: Formal analysis, Investigation, Funding acquisition, Visualization, Writing - review & editing; DMCh: Data curation, Formal analysis, Investigation, Resources, Validation, Visualization, Writing - review & editing; MAlo: Data curation, Formal analysis, Investigation, Resources, Validation, Visualization, Writing - review & editing; PKS: Conceptualization, Methodology, Supervision, Project administration, Validation, Visualization, Writing - original draft, review & editing. All authors contributed to editorial changes in the manuscript. All authors read and approved the final manuscript. All authors have participated sufficiently in the work and agreed to be accountable for all aspects of the work.
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PTM contribution was funded by National Science Centre, Poland (grant number 2021/05/X/NZ5/01511). PTM is supported by the Ministry of Science and Higher Education stipend for outstanding young scientists.
The authors declare no conflicts of interest.
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