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Heart Rate Variability: Measurement and Clinical Utility
Robert E. Kleiger, M.D.,∗Phyllis K. Stein, Ph.D.,∗and J. Thomas Bigger, Jr., M.D.†
From the ∗Washington University School of Medicine, St. Louis, MO and †Columbia University, New York, NY
Electrocardiographic RR intervals fluctuate cyclically, modulated by ventilation, baroreflexes, and
other genetic and environmental factors that are mediated through the autonomic nervous system.
Short term electrocardiographic recordings (5 to 15 minutes), made under controlled conditions, e.g.,
lying supine or standing or tilted upright can elucidate physiologic, pharmacologic, or pathologic
changes in autonomic nervous system function. Long-term, usually 24-hour recordings, can be used to
assess autonomic nervous responses during normal daily activities in health, disease, and in response
to therapeutic interventions, e.g., exercise or drugs. RR interval variability is useful for assessing risk
of cardiovascular death or arrhythmic events, especially when combined with other tests, e.g., left
ventricular ejection fraction or ventricular arrhythmias. A.N.E. 2005;10(1):88–101
autonomic nervous system
Heart rate responds dynamically to physiologic
perturbations mediated by the autonomic nervous
system via efferent vagal and sympathetic nerve
impulses.1,2Even at rest heart rate fluctuates cycli-
cally. High frequency (HF) cyclic fluctuations are
modulated by ventilation, mediated entirely by
changes in vagal outflow.3–7Slower fluctuations
occur due to baroreflexes or due to thermoreg-
ulation.3–7The greatest variation of heart rate
occurs with circadian changes, particularly the dif-
ference between night and day heart rate, mediated
by complex and poorly understood neurohormonal
rhythms.6,8Exercise and emotion also have pro-
found effects on heart rate. Fluctuations in heart
rate reflect autonomic modulation and have prog-
nostic significance in pathological states.9–45
There are two common settings in which heart
rate variability (HRV) is measured. First, HRV
is assessed under controlled laboratory conditions
with short-term measurements before and after
tilt, drugs, controlled ventilation, or other maneu-
vers selected to challenge the autonomic system.
Secondly, HRV can be determined from 24-hour
electrocardiographic (ECG) recordings made while
subjects perform their usual daily activities.
Twenty-four-hour ECG recordings are particularly
useful for risk stratification in a variety of patholog-
ical entities, but can also be useful for quantifying
autonomic dysfunction.5,12,16,46–52
Address for reprints: J. Thomas Bigger, Jr., M.D., Columbia University, 630 West 168th Street, PH 9-103, New York, NY 10032. Fax:
212-305-7141; E-mail: jtb2@columbia.edu
Methods for quantifying HRV are categorized as:
time domain, spectral or frequency domain, geo-
metric, and nonlinear. Baroreflex sensitivity (BRS)
and heart rate turbulence can also be considered
measures of HRV. A short discussion of each will
follow.
TIME DOMAIN MEASURES
OF HEART RATE VARIABILITY
In time domain analysis, the intervals between
adjacent normal R waves (NN intervals) are mea-
sured over the period of recording.53 A variety of
statistical variables can be calculated from the in-
tervals directly and others can be derived from the
differences between intervals (Table 1).53–55
SDNN, the standard deviation of all normal RR
(NN) intervals during a 24-hour period, is the most
commonly used time domain measure of HRV. A
major component of SDNN magnitude (approxi-
mately 30–40%) is attributable to day:night differ-
ence in NN intervals. Accurate calculation of SDNN
requires careful editing to exclude ectopic beats,
artifact, and missed beats. Artificially short or long
intervals occurring as a result of these events can ar-
tificially increase SDNN. Most laboratories require
at least 18 hours of usable data to calculate SDNN
in a 24-hour recording.
88
A.N.E. rJanuary 2005 rVol. 10, No. 1 rKleiger, et al. rMeasurement and Clinical Utility r89
Table 1. Time Domain Measures of HRV Calculated over 24 Hours
SDNN Standard deviation of all normal to normal R-R (NN) intervals
SDANN Standard deviation of 5-minute average NN intervals
ASDNN (index) Mean of the standard deviations of all NN intervals for all 5-minute segments in 24 hours
rMSSD Square root of the mean of the squares of successive NN interval differences
NN50 The number of NN intervals differing by >50 ms from the preceding interval
pNN50 The percentage of intervals >50 ms different from preceding interval
Night-day difference Mean night R-R interval minus mean day R-R interval
SDANN, the standard deviation of the 5-minute
average NN intervals, provides a “smoothed out”
version of SDNN, i.e., measures long-term fluc-
tuations.12 SDANN is less subject to editing er-
ror than SDNN because averaging several hundred
NN intervals minimizes the effects of unedited ar-
tifacts, missed beats, and ectopic complexity. As
such, SDANN is also much less affected by abnor-
mal rhythms and may even permit risk stratifica-
tion in atrial fibrillation.
ASDNN (or SDNN index) is the average of the
5-minute standard deviations of NN intervals.53
It reflects the average of changes in NN inter-
vals that occur within 5-minute periods. ASDNN is
significantly correlated with both SDNN and
SDANN, because low and high HRV tend to be
global phenomena, decreasing or increasing all
measures.
The most common variables calculated as differ-
ences between normal R-R intervals are rMSSD,
NN50, and pNN50.56,57 rMSSD is the square root
of the squares of the successive differences be-
tween NN intervals, essentially the average change
in interval between beats.58 NN50 is the absolute
count of differences between successive intervals
>50 ms,17 and pNN50 is the proportion of dif-
ferences >50 ms.12 In the presence of normal si-
nus rhythm and normal AV-nodal function, each of
these measures quantifies parasympathetic modu-
lation of normal R-R intervals driven by ventilation.
All other time domain measures are variants of
those discussed above and correlate highly with one
or more of the previously discussed measures.
SPECTRAL ANALYSIS OF R-R
INTERVALS
Either fast Fourier transformation or autoregres-
sion techniques can be used to quantify cyclic fluc-
tuations of R-R intervals.59 Traditionally, spectral
analysis has been done in short-term laboratory
studies; often standard 5-minute ECG segments are
analyzed. Two peaks are seen in 5-minute R-R in-
terval power spectra, a HF peak between 0.15 and
0.40 Hz and a low frequency (LF) peak between
0.04 and 0.15 Hz (Fig. 1, upper panel).
High frequency power reflects ventilatory mod-
ulation of R-R intervals (respiratory sinus arrhyth-
mia) with the efferent impulses on the cardiac va-
gus nerves, and is abolished by atropine. When the
frequency of ventilation is changed, the center fre-
quency of the HF peak moves with the ventilatory
rate.60,61 The amplitude of the peak, reflecting the
degree to which R-R intervals are affected by ven-
tilation, is similar over normal ventilatory frequen-
cies60,61
Low frequency power is modulated by barore-
flexes with a combination of sympathetic and
parasympathetic efferent nerve traffic to the sinoa-
trial node.1,3,6,37,63,64 Standing or head up tilt typ-
ically causes a modest increase in LF power
and a substantial decrease in HF power.63 At-
ropine almost abolishes the LF peak, and beta
blockade prevents the increase caused by stand-
ing up. Various manipulations of high and LF
power, e.g., normalization or the LF/HF ra-
tio has been applied in an attempt to bet-
ter estimate sympathetic activity. These manip-
ulations are based on a somewhat simplistic
“ying-yang” model of cardiac autonomic function.
Results have been illuminating under some circum-
stances (e.g., tilt table testing) and readily misinter-
preted under others (numerous papers in which in-
creases in the LF/HF ratio due to reductions in HF
power have been interpreted as increased sympa-
thetic activity).
R-R interval power spectra also have been com-
puted using data from 24-hour ECG recordings and
categorized into total power and four mutually ex-
clusive power bands, ultra low, very low, low,
and HF power (Fig. 1, lower panel).9,10 Total and
ultra-low frequency power are best calculated from
a R-R interval periodogram of the entire 24-hour
recording. Instead of computing the 24-hour power
spectrum, spectral analysis often is performed on
90 rA.N.E. rJanuary 2005 rVol. 10, No. 1 rKleiger, et al. rMeasurement and Clinical Utility
Figure 1. R-R interval power spectra. The upper panel
plots log power versus frequency for a 5-minute peri-
odogram and the lower panel plots log power versus fre-
quency for a 24-hour periodogram. In the lower panel,
frequency is plotted on a log scale and the Y axis is
markedly compressed compared with the upper panel.
Note the exponential increase in power as frequency de-
creases below the low frequency band for both graphs.
The two graphs resemble each other, but with much
greater amplitude in the 24-hour plot (lower panel). The
similarity in the graphs is consistent with fractal behavior
for power below the low frequency band.
5-minute segments from 24-hour recordings. HF
and LF power are calculated for each suitable seg-
ment and then averaged. Either method is suit-
able for estimating the average 24-hour HF and LF
power. Unfortunately, commercial Holter systems
sometimes calculate total power in each 5-minute
segment and report its average value over 24 hours.
Because the 5-minute value does not measure fluc-
tuations in R-R intervals with cycles longer than
5 minutes, such as those due to day:night differ-
ences, the 5-minute value is much smaller than to-
tal 24-hour power. The large difference between
5-minute and 24-hour total power can cause confu-
sion; it is the 24-hour value that is more useful for
prognosis (read below).
Most of the power of HRV in a 24-hour record-
ing resides in the frequencies below HF and LF
power which together account for <10% of the to-
tal power over 24 hour. About 12% of power is
accounted for by fluctuations in R-R intervals that
have a period between 20 seconds and 5 minutes
(0.0033–0.04 Hz).10 This spectral band is called very
low frequency (VLF) power. The exact physiologic
mechanism responsible for VLF is a matter of dis-
pute, but, like most other forms of HRV, VLF power
is abolished by atropine, suggesting that it uses a
parasympathetic efferent limb.64,65 Very low fre-
quency power is also reduced by about 20% by
ACE inhibition, suggesting that, at least in part, it
reflects the activity of the renin-aldosterone sys-
tem.66,67 Others have suggested that VLF power
reflects thermoregulation or vasomotor activity.68
Bernardi et al. showed that physical activity can
exert a large effect on VLF power.69 In addition,
sleep-disordered breathing can cause exaggerated
values for VLF power, seen as clear peaks on plots
of the HRV power spectrum during the night.70
The lowest frequency band in the 24-hour R-R
interval power spectrum is ultra low frequency
(ULF) power, which quantifies fluctuations in R-R
intervals with periods between every 5 minutes
and once per 24 hours (ULF <0.003 Hz). Ultra
low frequency power is strongly associated with
SDANN.11
Although the physiologic basis for ULF and VLF
power are far less clear than HF and LF power,
they have proven to be more powerful risk predic-
tors in cardiovascular diseases.10 It is important to
point out that accurate editing, and attention to the
uniformity of beat onset detection, is crucial for 24-
hour spectral analysis. Including nonNN intervals
in the R-R interval time series will substantially
degrade spectral analysis, even more so than for
time domain analysis. Each of the 24-hour spectral
measures has an equivalent time domain variable,
which is highly correlated with it (Table 2) because
both are influenced by the same physiologic inputs
and because of mathematical relationships.11 For
example, SDNN is the square root of the total vari-
ance in normal R-R intervals, whereas total power
is equivalent to total variance. In practice, the
A.N.E. rJanuary 2005 rVol. 10, No. 1 rKleiger, et al. rMeasurement and Clinical Utility r91
Table 2. Highly Correlated Time and Spectral
Measures of HRV
Time Domain Frequency Domain
SDNN Total power
SDANN ULF power
ASDNN VLF power
PNN50, rMSSD HF power
Highly Correlated Time Domain Measures
SDNN SDANN
RMSSD pNN50
correlations between TP and SDNN, ULF power
and SDANN, VLF power, and SDNN index exceed
0.85 and the correlations between ULF power (ap-
proximately 80% of the total power) and TP, SDNN,
SDANN also exceed 0.8. Use of time domain vari-
ables, e.g., SDNN and SDANN rather than the spec-
tral measures for a particular study is a matter of
preference and capability. Because all frequency
domain and some time domain HRV variables have
skewed distributions, the data are usually log trans-
formed for parametric statistical analyses.
GEOMETRIC MEASURES OF R-R
INTERVALS
Heart rate variability triangular index, a geo-
metric measure of HRV, has been used exten-
sively by investigators at St. George’s Hospital in
London.19,37,54 Bedeviled by difficulties in effi-
ciently dealing with ectopic complexes, missed
beats, and noise in analyzing recordings, they cre-
ated histograms of the intervals by sorting them
into 7.8 ms bins. They then fitted a triangle, using
a least squares technique, to the height of each in-
terval. Two measurements were made, the baseline
width of the triangle in milliseconds and the ratio
of the total number of beats divided by the num-
ber of beats in the modal bin. The latter quantity
is called HRV triangular index or just HRV index,
and is essentially the area of the triangle divided by
the area of the modal bin. The calculation of HRV
index minimizes the influence of outlier R-R inter-
vals, i.e., those much longer or shorter than the
usual, thereby substantially reducing the influence
of missed beats, artifact and ectopic complexes.
With accurate editing, HRV index and SDNN are
strongly correlated and both are powerful risk strat-
ifiers after myocardial infarction.19,37,54
NONLINEAR MEASURES OF R-R
INTERVAL FLUCTUATIONS
Although time and frequency domain measures
of HRV quantify HRV on various time scales, non-
linear HRV measures attempt to quantify the struc-
ture or complexity of the R-R interval time series.
For example, a random series of R-R intervals, a
normal series of R-R intervals and a totally peri-
odic series of R-R intervals might have the exact
same SDNN, but their underlying “organization”
would be completely different. A large number of
nonlinear measures of HRV have been studied, but
only a few have shown clear utility in risk stratifi-
cation (Fig. 2). These include the power law slope,
the short- and long-term fractal-scaling exponent,
and SD12, a measure derived from Poincare plots.
Power Law Slope
In normal sinus rhythm, spectral power, mea-
sured over 24 hours, shows a progressive, expo-
nential increase in amplitude with decreasing fre-
quency.71 (Fig. 1b) This relationship can also be
plotted as the log of power (Y axis) versus the log of
frequency (X axis), which transforms the exponen-
tial curve to a line whose slope can be estimated
(Fig. 2, bottom panel). In a log-log plot, the power
law slope between 10−2and 10−4Hz is linear with
a negative slope, and reflects the degree to which
the structure of the R-R interval time series is self-
similar over a scale of minutes to hours. Decreased
power law slope has been shown to be a marker for
increased risk of mortality after myocardial infarc-
tion.72
Detrended Fractal Scaling Exponent
This measure, also referred to as α1,iscomputed
from detrended fluctuation analysis (DFA) and is
a measure of the degree to which the R-R interval
pattern is random at one extreme, or correlated at
the other on a scale of 3–11 beats (Fig. 2, middle
panel).73 A totally random R-R interval pattern has
a value for α1of 0.5, whereas a totally correlated
pattern of R-R intervals, i.e., one that is totally pe-
riodic, has a value of 1.5. α1is usually repeatedly
measured within a period of 1000 R-R intervals and
then averaged. Normal values are about 1.05. De-
creased values for α1are strong predictors of out-
come after MI.73,74 Another measure, α2(or DFA2)
can be computed in a similar way on a scale of
92 rA.N.E. rJanuary 2005 rVol. 10, No. 1 rKleiger, et al. rMeasurement and Clinical Utility
Figure 2. Nonlinear Measures of R-R Interval fluctu-
ations. The top panel shows a two-dimensional vector
analysis of a Poincar ´
e plot; the middle panel shows cal-
culation of detrended fluctuation analysis (DFA); and the
bottom panel shows calculation of the power law slope.
The Poincar ´
e plots and DFA analyses are derived from
a 1-hour recording at night in a healthy subject. The
power law slope is derived from a 24-hour recording. Ab-
breviations: SD1, short-term beat-to-beat R-R variability
from the Poincar ´
e plot (width); SD2, long-term beat-to-
beat variability from the Poincar ´
e plot (length); α1,the
short-term fractal scaling exponent for 4–11 beats; α2,
the intermediate-term fractal scaling exponent (11–20
beats), β, power law slope (adapted from Ref.73)
12–20 R-R intervals. α2, however, has not proved
to be especially useful in risk stratification.
The Poincar´
e Plot
The Poincar´
e graph plots each R-R interval as
a function of the next R-R interval (Fig. 2, top
panel) and provides an excellent way to visualize
patterns of R-R intervals.73 Usually, the R-R inter-
val time series is plotted for an entire 24 hours, but
plots of shorter periods, e.g., hourly, can reveal de-
tails obscured in a 24-hour plot that involves about
100,000 points. Poincar´
e plots that reveal abnormal
R-R interval patterns have been characterized as
“complex.” In addition, Poincar´
e plots that reflect
extremely low HRV have also been classified as ab-
normal. SD12 is determined by fitting an ellipse to
the Poincar´
e plot. SD1 is the short axis of this el-
lipse and SD2 is the long axis. SD12 is their ratio. As
the plot becomes more complex, the relative mag-
nitude of SD1 compared to SD2 increases and SD12
becomes larger (Fig. 2, top panel). In addition, if the
plot is small and ball-shaped because of relatively
constant R-R intervals, SD12 also will be large. This
measure has not been used much for risk stratifi-
cation, but has proved useful for detecting editing
problems that significantly influence the calcula-
tion of HRV variables.
Heart Rate Turbulence
Heart rate turbulence is a novel analytic method,
which evaluates the perturbation (shortening then
lengthening) in R-R intervals following premature
ventricular complexes (VPC).75 Two parameters
quantify the response to VPC: turbulence onset
(TO) and turbulence slope (TS). Turbulence onset,
a decrease in the first two normal R-R intervals fol-
lowing a VPC compared with the two normal R-R
intervals just before the VPC, presumably reflects
baroreceptor reflex activity induced by a decreased
stroke volume and blood pressure during the com-
pensatory pause. Normally, the two R-R intervals
after a VPC are shorter than the two normal R-R
intervals immediately preceding the VPC. Turbu-
lence slope quantifies the degree of lengthening of
R-R intervals following the shortening of R-R in-
tervals immediately after a VPC, again reflecting
baroreflex activity.75 It is calculated by determin-
ing the maximum slope of any 5-beat sequence
of normal R-R intervals during the 15–20 R-R
intervals after the VPC. Turbulence onset and
A.N.E. rJanuary 2005 rVol. 10, No. 1 rKleiger, et al. rMeasurement and Clinical Utility r93
turbulence slope are calculated from all single VPC
in a 24-hour recording. Schmidt recommends that
at least 5 VPC be present in a Holter recording, in
order to estimate heart rate turbulence.74 Reduced
heart rate turbulence is strongly associated with in-
creased death rates after MI.75–77 Heart rate turbu-
lence will be discussed in detail elsewhere in this
journal.
DIAGNOSTIC USES FOR HEART
RATE VARIABILITY
Analysis of HRV has been used to assess
autonomic function and/or to quantify risk in
a wide variety of both cardiac and noncardiac
disorders. These include, among others, stroke,
multiple sclerosis, end stage renal disease, neonatal
distress, diabetes mellitus, ischemic heart disease,
particularly myocardial infarction, cardiomyopa-
thy, patients awaiting cardiac transplantation,
valvular heart disease, and congestive heart
failure.3,11,12,14,15,17–24,27–29,32,33,35–37,39,43,47,49,50,52
Several authors have reported that HRV analysis
is a more sensitive indicator of autonomic dys-
function in alcoholics and in diabetic subjects than
conventional autonomic tests.78–81 Heart rate vari-
ability analysis has also been used to assess the au-
tonomic effects of drugs, including beta-blockers,
calcium blockers, antiarrhythmics, psychotropic
agents, and cardiac glycosides.65–67,82–91 Drug
effects on HRV can be established with relatively
small numbers of study participants because HRV
measurements are quite stable over the short- and
long-term.92,93
Heart rate variability analysis has had its great-
est cardiologic use in post MI risk stratification
and in assessing risk for arrhythmic events. Wolff
et al. in 1978 first observed that HR variability mea-
sured on admission to the coronary care unit was a
predictor of mortality.94 They calculated the vari-
ance of 30 consecutive R-R intervals taken from
a1minute ECG recording in 176 patients with
acute myocardial infarction. The group of patients
(n =73) with R-R interval variance <32 ms had sig-
nificantly higher hospital mortality than the group
with preserved sinus arrhythmia (n =103). Clin-
ically, patients with low HRV were older, more
likely to have an anterior infarct, and more likely to
have heart failure. It was not clear from this study
whether decreased HRV was an independent pre-
dictor of adverse outcome or if it predicted long-
term risk after myocardial infarction.
The first study that clearly documented the in-
dependent and long-term predictive value of HRV
analysis after myocardial infarction was reported in
1987 by the Multi-Center Post-Infarction Program
(MPIP).28 Eight hundred and eight patients who had
survived acute myocardial infarction had 24-hour
ambulatory electrocardiograms prior to discharge.
Besides Holter variables, which included mean
heart rate, ventricular arrhythmias, and SDNN, pa-
tients were evaluated clinically, had a radionuclide
ejection fraction determined and were evaluated by
a low level exercise test. During a mean follow up
of 31 months, there were 127 deaths (Fig. 3). Forty-
three of these deaths occurred in the group of pa-
tients with SDNN <50 ms (125 patients), approxi-
mately 16% of the total group. Thus, over a third
of these patients died during follow-up and a third
of the deaths occurred in the group with SDNN
<50 ms, establishing a sensitivity and positive pre-
dictive accuracy of about one third (Table 3). The
relative risk of mortality in patients with SDNN
<50 ms versus those with SDNN ≥50 ms was 2.8.
Reduced SDNN was significantly associated with
low ejection fraction, poor exercise performance,
high New York Heart Association functional class,
and short R-R intervals (Table 4), but the correla-
tions were weak (0.15–0.52). Multivariate analysis
clearly demonstrated that SDNN was an indepen-
dent risk factor for mortality. SDNN also was the
Holter variable with the strongest association with
Figure 3. Kaplan-Meier survival curves from the Multi-
Center Post-Infarction Study demonstrating decreased
survival among patients with SDNN <50 ms (from
Ref.28)
94 rA.N.E. rJanuary 2005 rVol. 10, No. 1 rKleiger, et al. rMeasurement and Clinical Utility
Table 3. SDNN Prediction of Mortality in MPIP
SDNN
<50 ms ≥50 ms
Number of patients 125 (15.5%) 683 (84.5%)
Deaths, number (%) 43 (34.4%) 84 (12.3%)
Sensitivity 33.9% (43/127)
Specificity 88.0% (599/681)
Positive predictive 34.4% (43/125)
accuracy
False negative rate 12.3% (84/683)
Relative risk =2.8 (34.4%/12.3%)
all-cause mortality, exceeding that of any ventric-
ular arrhythmia measure. Using combinations of
risk variables such as SDNN and ejection fraction
or SDNN and repetitive VPC subgroups of MI pa-
tients could be determined with either very high
(50%) mortality or very low (<2%) 31 months mor-
tality.28,29
Multi-Center Post-Infarction Program data have
been analyzed using other HRV measures. Bigger
et al. evaluated the predictive value of 24-hour
spectral measures.9–11 Because of the previously
cited physiologic associations of various frequency
bands, it was thought that spectral analysis might
provide mechanistic insight into death and arrhyth-
mias after myocardial infarction. The anticipated
selectivity was not found. All four frequency bands
predicted all-cause and arrhythmic mortality, but
ultra-low frequency power had the strongest associ-
ation with these fatal outcomes. Frequency domain
measures of HRV had similar predictive value for
death of all causes, cardiac death, and arrhythmic
death. The MPIP data also have been analyzed us-
ing heart rate turbulence, which in the MPIP data
Table 4. Correlations of SDNN with Other Variables
in MPIP
rP
Age −0.19 0.0001
Rales in the CCU −0.25 0.0001
Peak BUN −0.15 0.0007
Ejection fraction 0.24 0.0001
Duration of exercise test 0.15 0.0007
Twenty-four-hour average 0.52 0.0001
RR interval
Ln VPC frequency −0.12 0.0004
Ln ventricular paired VPC −0.07 0.04
Ln ventricular runs per hour −0.02 0.57
set is even a stronger risk predictor than conven-
tional time or frequency domain variables.75
Multi-Center Post-Infarction Program was done
in the late 1970s, prior to the institution of much of
what is standard therapy today. Few of the patients
received aspirin or B-blockers and none had reper-
fusion therapy, thrombolytics, angioplasty, or coro-
nary artery bypass graft surgery. Thus, the ques-
tion arose as to whether the MPIP results apply
in the era of reperfusion. Multiple studies since
MPIP have confirmed the power of HRV analysis
in risk stratification post infarction. Some of these
are summarized in Table 5.
Some of the most important of these studies were
performed at St. George’s hospital in London by
Camm, Malik and co-investigators.19,33,34,37,54 Far-
rell reported 68 patients with acute myocardial
infarction who had both baroreceptor sensitivity
and HRV determined before discharge from hos-
pital.95 The latter was measured using HRV trian-
gular index. Both BRS and HRV index were deter-
mined to be good risk stratifiers for survival; BRS
was superior. Subsequently, these investigators ex-
tended their studies to over 400 survivors of my-
ocardial infarction. In all these studies, they uti-
lized HRV index.19,37,54 Approximately 60% of their
patients received thrombolytic therapy or angio-
plasty. Besides HRV index, late potentials, ejection
fraction, clinical variables, and ventricular arrhyth-
mias were measured. In addition, the mechanisms
of death, arrhythmic or nonarrhythmic, and ma-
lignant ventricular rhythms were adjudicated. De-
creased HRV index best predicted both total cardiac
mortality and malignant arrhythmias better than
decreased ejection fraction, abnormal late poten-
tials, or increased frequency of ventricular ectopy
in 24-hour Holter ECG recordings. Furthermore,
combining decreased HRV index with another risk
variable, such as decreased ejection fraction or ab-
normal late potentials, created subgroups of post
MI patients with high risk as well as subgroups
with very low risk of death or malignant ventric-
ular arrhythmias.19
The GISSI study of thrombolytic therapy in acute
myocardial infarction evaluated HRV.52 In GISSI,
all 12,490 patients were treated with streptokinase.
A subset of 567 patients had a valid 24-hour ambu-
latory ECG recording and 52 of them died during
a 1000-day follow-up. Time domain analysis utiliz-
ing SDNN, NN50+, and rMSSD identified high risk
groups comprising 16–18% of the subset with mor-
talities ranging from 20.8 to 24.2% in the high risk
A.N.E. rJanuary 2005 rVol. 10, No. 1 rKleiger, et al. rMeasurement and Clinical Utility r95
Table 5. Representative Post-MPIP Confirmatory Studies of HRV as a Predictor of All-Cause or Cardiac Mortality After MI. Number in Parenthesis
Refers to Reference List. Others Referenced Below Table
Source Number of HRV Measure
(Study Name) Patients (Events) When Obtained Follow-Up HRV Predictors/ Endpoints
Bigger et al. (CAPS)96 N=331 (30 deaths) 24-hour, 1 year after
enrolling in CAPS and
1 week after stopping
meds
3 years ULF, VLF, LF, HF all significant,
univariate predictors of all-cause
mortality. After adjustment for
covariates, VLF was the strongest
predictor
Copie et al.97 N=579, (54 deaths, 42
cardiac, 26 sudden)
24-hour, before
discharge (median 7
days after MI)
>2 years HRV index better predictor than mean
RR interval for sensitivity <40%. For
sensitivity ≥40% mean R-R interval
and HRV index equal. Mean R-R
interval <700 ms predicted cardiac
death (45% sensitivity, 85%
specificity, 20% PPA) and predicted
all-cause, cardiac and sudden death
better than LVEF
Fei et al.98 N=700 (45 cardiac
deaths, 24 sudden)
24-hour, 5-minute
period, 5–8 days
before discharge
1 year SDNN for 5-minutes had lower PPA
than HRV index, but could preselect
those who require 24-hour Holter
ECG for risk stratification
Huikuri et al. (DIAMOND-MI)74 N=446 with LVEF ≤
0.35, 114 deaths, 75
arrhythmic, 28
nonarrhythmic
24-hour, predischarge,
traditional and
nonlinear HRV
685 ±360 days α1<0.75 RR 3.0, 95% CI 2.5–4.2 for all
cause mortality, independent
predictor after adjustment. Predicted
by arrhythmic and nonarrhythmic
death
La Rovere et al. (ATRAMI)99 N=1284 (44 cardiac
deaths, 5 nonfatal
sudden)
24-hour, <28 days after
MI
21 ±8 months SDNN <70 ms vs SDNN ≥70 ms
M¨
akikallio et al. (TRACE)100 N=159 with LVEF ≤35,
72 deaths
24-hour, traditional and
nonlinear
4 years α1<0.85 best univariate predictor of
mortality (RR 3.17, 95% CI
1.96–5.15), PPA 65% and NPA 86%.
Remained significant after
adjustment
Odemuyiwa et al.101 N=433 (first MI), (46
total deaths, 15 sudden
deaths)
24-hour, before
discharge
4 weeks to 5
years
HRV index <20 univariate predictor of
mortality for whole follow-up but
independent predictor of total cardiac
mortality for first 6 months only
Odemuyiwa et al.37 N=385 (44 deaths, 14
sudden)
24-hour, before
discharge
151–1618 days HRV index <39 sensitivity 75%,
specificity 52% compared with LVEF
≤40% which had specificity of 40%
for all-cause mortality. HRV +LVEF
better specificity for sensitivity <60%
Continued
96 rA.N.E. rJanuary 2005 rVol. 10, No. 1 rKleiger, et al. rMeasurement and Clinical Utility
Table 5. Continued.
Source Number of HRV Measure
(Study Name) Patients (Events) When Obtained Follow-Up HRV Predictors/ Endpoints
Quintana et al.102 N=74 (18 deaths 9
nonfatal MI), 24 normal
controls
24-hour, mean 4 days
after MI
36 ±15 months LnVLF <5.99 independent predictor of
all-cause mortality (RR =1.9) or
mortality/nonfatal infarction (RR =2.2)
Tapanainen et al.103 N=697, 49 deaths 24-hour, 2–7 days after
MI
18.4 ±6.5
months
α1<0.65 most powerful predictor of
mortality RR 5.05, 95% CI 2.87–8.89).
After adjustment, α(1) remained
independently associated with mortality
(RR =3.90, 95% CI 2.03–7.49)
Touboul et al. (GREPI)104 N=471 (26 deaths for 1
year FU, 39 for
long-term FU, 9 sudden)
45% had thrombolysis
24-hour HRV, 10 days
after MI
1 year and long
term (median
31.4 months)
Nighttime AVGNN <750 ms (RR =3.2),
daytime SDNN <100 ms (RR =2.6)
Viashnav et al.105 N=226 (19 cardiac
deaths)
24-hour, mean 83 hours
after MI
Mean 8 months Cox regression not performed Decreased
SDNN, SDANN, ASDNN, LF, HF, LF/HF
among nonsurvivors, but rMSSD and
pNN50 not different
Voss et al.106 N=572 (43 all-cause, 14
sudden arrhythmic, 22
sudden, 34 cardiac, 13
nonfatal VT/VF)
24-hour, 5–8 days after
MI, standard, nonlinear
HRV
2 years For best combination of predictors maximum
specificity at 70% sensitivity where PPA for
endpoints was 16–29% compared with
6–17% for HRV alone
Zabel et al.107 N=250 (30 endpoints) 24-hour HRV, stable,
before discharge
Mean 32 months SDNN significantly higher in event-free (no
VT, resuscitated VF, or death)
Zuanetti et al. (GISSI)52 N=567 males treated
with thrombolysis (52
total deaths, 44 cardiac
deaths)
24-hours at discharge
(median 13 days)
1000 days Independent predictors of all-cause
mortality: NN50 +(RR =3.5), SDNN (RR
=3.0), rMSSD (RR =2.8)
ATRAMI =Autonomic Tone and Reflexes after Myocardial Infarction; CAPS =Cardiac Arrhythmia Pilot Study; DIAMOND =Danish Investigations of Arrhythmia and
Mortality on Dofetilide; GISSI =Grupo Italiano per lo Studio della Sopravvivenza nell-Infarto Miocardico; GREPI =Groupe d’Etude du Pronostic de l’Infarctus du
Myocarde; FU =follow-up; HRV =heart rate variability; LVEF =left ventricular ejection fraction; MI =myocardial infarction; PPA =positive predictive accuracy; RR =
relative risk; TRACE =TRAndolapril Cardiac Evaluation; VF =ventricular fibrillation; VT =ventricular tachycardia.
A.N.E. rJanuary 2005 rVol. 10, No. 1 rKleiger, et al. rMeasurement and Clinical Utility r97
group versus 6.0–6.8% in the low risk group de-
fined by HRV analysis. The relative risk of mortal-
ity was approximately 3.0 for the low HRV groups.
This study not only confirmed the ability of HRV
analysis to risk stratify patients in the modern era
of myocardial infarction treatment but also demon-
strated that HRV analysis alone has limited positive
predictive accuracy.52
THERAPEUTIC IMPLICATIONS OF
HEART RATE VARIABILITY
ANALYSIS
The therapeutic implications to be derived from
HRV analysis after myocardial infarction are un-
clear. Animal models of experimental ischemia and
myocardial infarction show a strong association
between decreased HRV and spontaneous ven-
tricular fibrillation, decreased ventricular fibrilla-
tion threshold and mortality. Furthermore, proce-
dures that increase HRV such as β-adrenergic re-
ceptor blockade, exercise conditioning, low dose
atropine, or scopolamine administration reduce
mortality rates, increase VF threshold, and de-
crease spontaneous, exercise induced, or ischemia
induced ventricular fibrillation in animal mod-
els.15,24,26,40,65–67,69,82–84,108–110 In human studies,
β-blockade increase HRV in both healthy per-
sons and patients who have had myocardial in-
farction,82–84 as does scopolamine.109 Type 1C
antiarrhythmic drugs decrease HRV.86,87 Scopo-
lamine in animal models and β-blockers in both
animal models and humans improve survival af-
ter myocardial infarction, whereas 1C antiarrhyth-
mic drugs increase mortality rates; however, how
these effects are related to HRV is not estab-
lished. Thus, diminished HRV is associated with
increased sympathetic and decreased vagal modu-
lation, and these autonomic changes have been as-
sociated with an increase in malignant ventricular
arrhythmias.108,110,111
Perhaps the greatest potential therapeutic use
for HRV analysis in patients after myocardial in-
farction is risk stratification for antiarrhythmic
therapy. The European Myocardial Infarct Amio-
darone Trial (EMIAT) randomized 1486 post MI
patients with ejection fraction ≤0.40, age ≤75 to
amiodarone or placebo therapy.112 The 743 pa-
tients on amiodarone had exactly the same mor-
tality as those on placebo with superimposable
Kaplan-Meier mortality curves, but the mecha-
nisms of death was predominantly arrhythmic in
the placebo group and nonarrhythmic in the amio-
darone treated group. However, in those patients
with a low HRV defined as SDNN <50 ms or HRV
index ≤20 units, there was both a 66% reduction
in arrhythmic death and a 24%, borderline signifi-
cant, decrease in total mortality in the group treated
with amiodarone.113 These results need confirma-
tion but suggest that HRV analysis may be useful in
determining which patients with low ejection frac-
tions after myocardial infarction might most benefit
from ICD implantation.
The Autonomic Tone and Reflexes after Myocar-
dial Infarction (ATRAMI) epidemiological study fol-
lowed 1071 patients after myocardial infarction to
evaluate the predictive value of LVEF, BRS, and
SDNN after myocardial infarction.99 Therapy in
this study was modern, with 63% of the patients
receiving reperfusion therapy. The patients were
low risk because those with heart failure or angina
were excluded. The average follow up was 21 ±
8 months. There were 43 cardiac deaths, 5 patients
had nonfatal cardiac arrest, and 30 patients had
sudden death and/or sustained ventricular tachy-
cardia. LVEF <0.35, SDNN <70 ms, and BRS
<3.0 were all associated with cardiac death, sud-
den death, and nonfatal cardiac arrest. Both BRS
and SDNN predicted mortality during follow-up af-
ter infarction. In patients under age 65, BRS was a
slightly better predictor than SDNN. However, for
those over age 65, SDNN predicted death much bet-
ter than BRS. Baroreflex sensitivity and heart rate
variability had independent predictive value, al-
though they were significantly associated. The sub-
group that had both low BRS and low HRV had an
18% mortality versus <2% for the group with high
values for both variables. It is clear from these data
that depressed HRV remains a statistically power-
ful predictor of death despite modern treatment for
myocardial infarction.114 In ATRAMI, an ejection
fraction <0.35 and decreased BRS or HRV defined,
even in this low risk group of MI patients, a sub-
group with a high, approximately 20%, 2-year mor-
tality.99
LIMITATIONS OF HEART RATE
VARIABILITY AS A RISK
STRATIFIER AFTER MYOCARDIAL
INFARCTION
Although decreased HRV is the most pow-
erful ambulatory ECG predictor of cardiac
mortality and malignant arrhythmias following
98 rA.N.E. rJanuary 2005 rVol. 10, No. 1 rKleiger, et al. rMeasurement and Clinical Utility
myocardial infarction, and in some studies is a
more powerful predictor than ejection fraction,
late potentials, and clinical variables, it has sev-
eral significant limitations. It requires normal si-
nus rhythm and reasonable signal quality. Atrial
fibrillation, sinoatrial dysfunction, and >20% ec-
topic complexes preclude its use. For heart rate tur-
bulence analysis, five or more VPC are needed in
addition to sinus rhythm. To get useful recordings
requires great care in applying electrodes to reduce
artifact and careful editing of the recording to ex-
clude ectopic complexes and artifacts from the cal-
culations. The best predictors require rather long
recording periods in order to include both night-
time and daytime periods. The optimal time after
myocardial infarction to measure HRV is not cer-
tain. There is considerable recovery of HRV in the
3–6 months after myocardial infarction, but recov-
ery values of HRV are, on average, well below
normal age and gender matched healthy individ-
uals.72,115 Although most studies have been per-
formed in the subacute phase of infarction, some
have been performed as late as a year post infarct
and HRV remained significantly associated with
subsequent mortality.96
The best HRV variable to measure is unclear;
conventional time domain, BRS, heart rate turbu-
lence, spectral measures, geometric measures, and
a variety of nonlinear variables reflect different as-
pects of HRV and have all been significantly as-
sociated with outcome without clear, consistent
superiority for any. Moreover, isolated HRV mea-
surements have limited predictive accuracy. As a
univariate predictor, HRV has low sensitivity and
low positive predictive accuracy. Thus, the ther-
apeutic implications of abnormal HRV are uncer-
tain. Yet it has clearly been demonstrated that com-
bining HRV with other risk variables, such as ejec-
tion fraction, BRS, late potentials, exercise testing,
or ventricular arrhythmias can define subgroups
of patients with both very low and very high total
cardiac and arrhythmic mortality after myocardial
infarction. HRV in combination with other vari-
ables, e.g., left ventricular ejection fraction, may be
a very useful clinical tool to better define patients
likely or unlikely to benefit from prophylactic ICD
implantation.
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