From the Division of Cardiology, Department of Medicine, University of
Oulu (H.V.H., T.H.M., K.E.J.A., T.S.), Research and Development Centre of the
Social Insurance Institution (P.P.), and Department of Geriatrics, University
of Turku (I.J.R., L.B.S.), Finland.
Correspondence to Heikki V. Huikuri, MD, Division of Cardiology, Department of Medicine, University of Oulu, Kajaanintie 50, 90220 Oulu, Finland. E-mail HEIKKI.HUIKURI{at}oulu.fi
Methods and Results—A random sample of 347 subjects of
Conclusions—Power-law relationship of 24-hour HR variability is a
more powerful predictor of death than the traditional risk markers in
elderly subjects. Altered long-term behavior of HR implies an increased
risk of vascular causes of death rather than being a marker of any
disease or frailty leading to death.
HR variability measurements from 24-hour ambulatory ECG
recordings provide prognostic information in patients with
heart disease,5 6 but their prognostic role is
not well established in general populations. In patients with a recent
myocardial infarction, analysis of the spectral characteristics
of 24-hour HR variability has been observed to yield prognostic
information beyond that provided by the traditional short-term measures
of HR variability.6 7 The purpose of the
present investigation was to assess the ability of 24-hour HR
variability to predict mortality in a random population of elderly
subjects. We also studied whether analysis of the spectral
characteristics of long-term HR behavior performs any better than the
traditional measures of HR variability or other common risk markers for
predicting various causes of death during long-term follow-up.
A clinical history was obtained by personal interview, and a
comprehensive clinical evaluation was carried out, including
classification of functional class, physical examination, standard ECG,
chest x-ray, blood pressure, and biochemical
analyses.8 9 10 Major diagnoses were
established on the basis of the history, clinical evaluation, ECG and
chest x-ray findings, and laboratory data.8 9 10
Ischemic heart disease was diagnosed by a standard
questionnaire concerning any history of angina pectoris. Myocardial
infarction was defined from the clinical history and from Q-wave
abnormalities on the ECG, according to the Minnesota code. Additional
information for the diagnostic criteria was obtained from
the subject's national health insurance
documents.9 10 Serum total
cholesterol, HDL and LDL cholesterol,
triglycerides, and glucose were measured from overnight
fasting samples by the methods described
earlier.11
Ten-year mortality and causes of deaths were recorded from the
mortality statistics. The mode of death was defined after a review of
the hospital records, autopsy findings, and death certificates.
Nonfatal cardiovascular events (acute myocardial
infarction, unstable angina pectoris, transient ischemic
attacks, and stroke) were retrospectively analyzed from the
hospital records according to the corresponding
diagnostic codes (ICD 8 and 9 classification). The end
points were all-cause mortality, cardiac mortality, cerebrovascular
mortality, cancer mortality, mortality from various other causes, and
nonfatal cardiovascular events.
Ambulatory ECG Recordings
Analysis of HR Variability
After editing of the R-R interval tachograms, the R-R interval spectrum
was computed over the entire recording interval according to a
previously described method.14 A fast Fourier
transform method was used to estimate the power spectrum densities of
HR variability. Frequency domain measures of R-R interval variability
were computed by integrating the point power spectrum over the
frequency intervals. The power spectra were quantified by measuring the
areas in the following frequency bands (1) <0.0033 Hz, (ULF power),
(2) 0.0033 to <0.04 Hz (VLF power), (3) 0.04 to 0.15 Hz (LF power),
and (4) 0.15 to <0.40 Hz (HF power). The SDNN was used as a
time-domain measure of HR variability. The power-law relationship of
R-R interval variability was calculated from the frequency range of
10-4 to 10-2 by a
previously described method.7 The point power
spectrum was logarithmically smoothed in the frequency domain, and the
power was integrated into bins spaced 0.0167 log(Hz) apart. A robust
line fitting algorithm of log(power) on log(frequency) was then applied
to the power spectrum between 10-4 and
10-2, and the slope of this line was calculated
(see examples in Fig 1
Statistical Analysis
Cox proportional hazards regression analyses were used to
assess the association between different risk predictors and mortality
by use of SPSS for Windows version 6.1. To find the best cutoff points
for various measures of HR variability, the dichotomization cutoff
points that maximized the hazards ratio obtained from the Cox
regression model were sought, with all-cause mortality as the end
point. All the proportional hazards regression analyses were
stratified with sex and age as covariates. In addition, all the
variables that had a univariate association with
all-cause mortality (P<.05) were included in the model to
estimate the independent power of the various variables in
predicting the mortality. Kaplan-Meier estimates of the distribution of
times from the baseline to death were computed; log-rank
analysis was performed to compare the survival curves between
the groups.
Comparison of the HR variability measures between the survivors and
those who had died pointed to the slope of the power-law regression
line of HR variability, SDNN, and the VLF and LF spectral components as
having a univariate association with all-cause mortality
(Table 1
Among all analyzed clinical, laboratory, and Holter
variables, the slope of the power-law regression line was the best
univariate predictor of all-cause mortality (odds ratio,
7.9; 95% confidence interval, 3.7 to 17.0; P<.0001; Figs 1
Multivariate Predictors of Mortality
Predictors of Cardiac, Cerebrovascular, Cancer, and Other Causes of
Death and Nonfatal Cardiovascular Events
Correlations Between HR Variability and Other Risk Factors
Twenty-Four–Hour HR Variability as a Predictor of Various Causes
of Death
The unique length of the present follow-up with a larger number of
deaths than in previous studies5 6 7 15 allowed
the evaluation of different causes of mortality. The slope of the
power-law behavior of HR variability was specifically related to
vascular causes of death, ie, cardiac and cerebrovascular death. HR
variability has been previously shown to predict all-cause and cardiac
mortality in patient populations with documented heart
disease,5 6 7 but there has been no information on
the prognostic role of HR variability as a predictor of cerebrovascular
death. Present findings suggest that altered long-term HR behavior
is not specifically related to cardiac death but reflects an increased
risk for any acute vascular events leading to death. HR variability did
not predict death from cancer or of various other causes showing that
altered HR dynamics do not reflect the presence of an advanced
malignant disease or frailty leading to mortality.
Speculated Mechanisms of Altered Long-term Behavior of Heart Rate
as a Risk Factor for Mortality
Abnormalities in autonomic modulation of HR have been observed in
various cardiovascular and cerebrovascular
disorders,17 18 19 20 and it is possible that altered
cardiovascular neural regulation expressed by a steep
slope of long-term HR dynamics may be a sign of an underlying
subclinical vascular disease predisposing to mortality. Another
potential explanation for the prognostic role of altered HR behavior is
that it may reflect an impairment in the intrinsic
physiological regulatory and adaptive systems, with
aging leading to death during acute perturbations such as myocardial or
cerebral ischemic events. This concept is supported by the
observation that a steep slope did not predict the occurrence of
nonfatal cardiovascular events. Experimental data also
show that cardiovascular autonomic regulation plays an
important role in occurrence of life-threatening arrhythmias
during acute cardiac or cerebral
ischemia.21 22
From a mathematical point of view, it is noteworthy that the value of a
slope of -1.5 turned out to be the optimum discriminator of mortality.
In mathematics, the same boundary (ie, slope of 1.5) is used to
separate "1/f noise" (slope
Conclusions
Received November 13, 1997;
revision received December 16, 1997;
accepted January 23, 1998.
© 1998 American Heart Association, Inc.
Clinical Investigation and Reports
Power-Law Relationship of Heart Rate Variability as a Predictor of Mortality in the Elderly
Abstract
Top
Abstract
Introduction
Methods
Results
Discussion
References
Background—The prognostic role of
heart rate (HR) variability analyzed from 24-hour ECG
recordings in the general population is not well known. We
studied whether analysis of 24-hour HR behavior is able to
predict mortality in a random population of elderly subjects. 
65 years
of age (mean, 73±6 years) underwent a comprehensive clinical
evaluation, laboratory tests, and 24-hour ECG recordings and
were subsequently followed up for 10 years. Various spectral and
nonspectral measures of HR variability were analyzed from the
baseline 24-hour ECG recordings. Risk factors for all-cause,
cardiac, cerebrovascular, cancer, and other causes of death were
assessed. By the end of 10-year follow-up, 184 subjects (53%) had died
and 163 (47%) were still alive. Seventy-four subjects (21%) had died
of cardiac disease, 37 of cancer (11%), 25 of cerebrovascular disease
(7%), and 48 (14%) of various other causes. Among all
analyzed variables, a steep slope of the power-law
regression line of HR variability (<-1.50) was the best
univariate predictor of all-cause mortality (odds ratio,
7.9; 95% confidence interval [CI], 3.7 to 17.0;
P<.0001). After adjusting for age and sex and including
all univariate predictors of mortality in the proportional
hazards analysis, ie, measures of HR variability, history of
heart disease, functional class, smoking, medication, and blood
cholesterol and glucose concentrations, all-cause mortality
was predicted only by the slope of HR variability (adjusted relative
risk, 1.74; 95% CI, 1.42 to 2.13; P<.0001) and a
history of congestive heart failure (adjusted relative risk, 1.70;
P=.0002). The slope of HR variability predicted both
cardiac (adjusted relative risk, 2.05; P=.0002) and
cerebrovascular death (adjusted relative risk, 2.84;
P=.0001) but not cancer or other causes of death.
Key Words: population • death, sudden • intervals • aging
Introduction
Top
Abstract
Introduction
Methods
Results
Discussion
References
Because the
prognostic significance of conventional risk factors applicable to
younger ages tends to disappear in old age,1 2 3 4
it is important to find new prognostic and diagnostic
markers to define the risk of death and to develop therapeutic
strategies to prevent premature death among elderly subjects.
Methods
Top
Abstract
Introduction
Methods
Results
Discussion
References
Population
In connection with a large survey of the health status of the
elderly in the city of Turku, Finland, a random sample of 480 persons
65 years of age living in the community was obtained from the
register of the Social Insurance
Institution.8 9 10 No exclusion criteria other
than living in an institution were used. The participation rate of
these subjects was 72%; ie, the final series consisted of 347
subjects. Details concerning enrollment, measurement of baseline
variables, and follow-up have been described
previously.8 9 10 The final analysis of
ambulatory ECG recordings included 325 subjects; 22 subjects
had to be excluded because of atrial fibrillation (n=14), evidence of
sick sinus syndrome (n=2), or technical artifacts (n=6) during the
24-hour ambulatory ECG.
Twenty-four–hour continuous ambulatory ECG recordings
were performed with a portable two-channel tape recorder (Oxford
Medilog).8 9 The recordings were
analyzed with the replay and analysis units described
in detail previously.8 9 The subjects were
encouraged to continue with their normal everyday activities during the
recordings. Ventricular arrhythmias were
classified as (1) 10 or <10 ventricular premature beats
per hour and (2) episode(s) of ventricular
tachycardia ( 3 consecutive beats).
The ECG data were sampled digitally and transferred from the
Oxford Medilog scanner to a microcomputer for analysis of HR
variability. All R-R interval time series were first edited
automatically, after which careful manual editing was performed by
visual inspection of the R-R intervals. Each R-R interval time series
was passed through a filter that eliminates premature beats and
artifacts and deletes the filling gaps with previously described
methods.12 13 Only recordings with
qualified beats for at least a 20-hour period and with >85% of
qualified sinus beats were included in the analysis of HR
variability (n=305). The average duration of the recordings was
23 hours. 
). This frequency
band was chosen on the basis of previous observations regarding the
linear relationship between log(power) and log(frequency) in this
frequency band.7 14
View larger version (24K):
[in a new window]
Figure 1. Individual values of the slope of power-law
regression line computed over frequencies between 10-2 and
10-4 for subjects who were alive and for those who died
during the 10-year follow-up.
The data collected at the baseline examination were used as the
explanatory variables in univariate comparisons between
the subjects who had remained alive throughout the follow-up and those
who had died. The frequency-domain measures of HR variability were
transformed to natural logarithms because their distributions were
skewed. Univariate comparisons of baseline characteristics
between the subjects who had died and those who were alive at the end
of the follow-up were performed with the 
2
test for categorical variables and with the two-sample t
test for continuous variables. Odds ratios and 95% confidence
intervals were also calculated for each univariate
predictor of all-cause mortality. A value of P<.05 was
considered to indicate statistical significance.
Results
Top
Abstract
Introduction
Methods
Results
Discussion
References
Univariate Predictors of Mortality
By the end of 10-year follow-up 184 subjects (53%) had died and
167 (47%) were still alive. Seventy-four subjects (21%) had died of
cardiac disease, 37 of cancer (11%), 25 of cerebrovascular disease
(7%), and 48 (14%) of various other causes. Baseline characteristics
of subjects who had died during the follow-up and those who were still
alive are shown in Table 1.
Univariate comparison showed age, sex, history of
congestive heart failure, angina pectoris, prior myocardial infarction
or cerebrovascular disease, functional class, use of cardiac
medication, and smoking history to be associated with all-cause
mortality. The subjects who subsequently died also had elevated
baseline blood glucose and lower cholesterol concentrations
relative to those who remained alive during the follow-up.
View this table:
[in a new window]
Table 1. Baseline Characteristics of the Subjects Who Had
Died by the End of the 10-Year Follow-up and Those Who Had Remained
Alive 
). Because the SDNN and the VLF and LF frequency components of
HR variability had close univariate correlations with each
other (r>.7 for all) but the slope of the power-law
regression line had only a weak correlation with SDNN
(r=.16, P<.01), VLF (r=.37,
P<.001), or LF (r=.34, P<.001), a
stepwise proportional hazards method was used to reveal the independent
prognostic power of each measure. This showed both the slope of the
regression line and SDNN to possess independent predictive power with
respect to all-cause mortality (P<.001 for both), whereas
the VLF and LF power spectral components did not enter the model as
independent predictors. The best cutoff points for predicting mortality
were <-1.50 for the slope of the power-law regression line (94
subjects, 31%) and <120 ms for SDNN (100 subjects, 33%), and these
were used as dichotomized cutoff points in the
multivariate analyses. and 2). Overall mortality was also high
(36 of 42 subjects, 85%) among the subgroup of subjects in whom the
heart rate variability could not be analyzed, mostly because of
atrial fibrillation or frequent ventricular premature beats
during the recording.
View larger version (18K):
[in a new window]
Figure 2. Kaplan-Meier survival curves for the subjects with
the slope of the power-law regression line of HR variability <-1.50
or -1.50. Estimated cumulative (Cum) survival rate over a 10-year
period was 67% in those with a slope -1.50 and 20% in those with a
slope <-1.50.
Table 2 shows the significances and
relative risks attached to various clinical, laboratory, and ambulatory
ECG variables adjusted for age and sex in the Cox proportional
hazards analysis in predictions of all-cause mortality. A
history of previous myocardial infarction, angina pectoris, congestive
heart failure and cerebrovascular disease, smoking, functional class,
elevated blood glucose, SDNN <120 ms, and slope of the power-law
regression <-1.50 were associated with all-cause mortality after
adjustment for age and sex. When all the risk variables were
included in the analysis, a steep slope of the power-law
regression line (adjusted relative risk, 1.74; P<.0001) and
a history of congestive heart failure (adjusted relative risk, 1.70;
P=.0002) were the only independent predictors (Table 2).
View this table:
[in a new window]
Table 2. Significant Predictors of All-Cause Mortality in
Proportional Hazards Regression Analysis
Table 3 shows the significances and
relative risks attached to various variables in predictions of
cardiac and cerebrovascular deaths. After adjustment for age and sex,
cardiac death was predicted by the same variables as all-cause
mortality except for smoking history and by the presence of 10
ventricular premature beats per hour on the 24-hour ECG
recording. After adjustment for all risk variables, cardiac
death was independently associated only with a steep slope of the
regression line of HR variability (adjusted relative risk, 2.05;
P=.0002) and a history of congestive heart failure (adjusted
relative risk, 1.56; P=.03). The slope of the regression
line of HR variability was also an independent predictor of
cerebrovascular death (age- and sex-adjusted relative risk, 1.85;
P=.008), which was also predicted by a history of
cerebrovascular disease. None of the measures of HR variability had a
univariate association with cancer death or various other
nonvascular causes of death. When adjusted for age and sex, cancer
death was predicted by current smoking (relative risk, 1.86; 95%
confidence interval, 1.06 to 3.26; P=.03) and low
cholesterol (<5.0 mmol/L) (relative risk, 1.70; 95%
confidence interval, 1.02 to 2.88; P=.04), and various other
causes of death were predicted only by functional disability (class 3
to 4) (relative risk 1.67, 95% confidence interval, 1.14 to 2.47,
P=.007). Nonfatal cardiovascular events
(n=50, consisting of 22 acute myocardial infarctions, 13 unstable
angina pectoris, and 15 nonfatal strokes or transient ischemic
attacks) were not predicted by measures of HR variability, (eg, slope
-1.42±0.20 versus -1.40±0.19 in those with and without events).
These events had a univariate association only with the
baseline blood glucose (6.6±4.2 mmol/L in subjects with events
versus 5.4±1.9 mmol/L in subjects without events,
P<.05).
View this table:
[in a new window]
Table 3. Significant Predictors of Cardiac and
Cerebrovascular Mortality in Proportional Hazards Regression
Analysis
The slope of the power-law regression line of HR variability
showed weak correlations with age (r=-.16,
P<.01) and blood fasting glucose concentration (-0.14,
P<.05) but no significant correlations with the other risk
factors. SDNN was related to functional class (125±36 ms in class 3 to
4 versus 140±37 in class 1 to 2, P<.01), a history of
prior myocardial infarction (136±37 ms in patients without a prior
infarction versus 120±24 ms in those with a prior infarction,
P<001), and age (r=-0.15,
P<001).
Discussion
Top
Abstract
Introduction
Methods
Results
Discussion
References
The results show that the power-law relationship of long-term HR
variability is a more powerful predictor of mortality than conventional
risk markers in elderly subjects. Concurrent with previous findings,
common risk factors such as cholesterol, hypertension, and
smoking were not strong predictors of death, confirming that the
prognostic markers applicable to younger subjects do not perform as
well among the elderly.1 2 3 4
A previous study of a Framingham cohort also showed that the
traditional short-term measures of HR variability are able to predict
all-cause mortality in elderly subjects.15 In the
present population, with a longer follow-up, traditional spectral
and nonspectral measures did not emerge as independent predictors of
survival because they were more closely related to other risk factors
than the slope of the power-law relationship of HR variability, which
remained a powerful predictor of survival after adjustment for other
variables.
The slope of the power-law relationship of HR variability computed
over the ULF and VLF oscillations differs from the
traditional measures of HR variability because it does not reflect the
magnitude of HR variability but the distribution of spectral
characteristics of R-R interval
oscillations.7 14 The
physiological background for the spectral
distribution is not exactly known, but the observation of significantly
steeper slope in denervated hearts suggests that it is mainly
influenced by the autonomic input to the heart.7
The slope was found to be steeper in these elderly subjects than that
previously observed in younger, healthy
subjects,7 14 and it was weakly related to age
despite the rather narrow age distribution, suggesting that aging
itself results in progressive changes in the long-term spectral
characteristics of HR variability. No changes in ULF power but a linear
decline in VLF power has been observed with
aging,16 which probably explains the steeper
slope of the power-law regression line in the elderly. 
1) and "1/f2
noise" (slope 2). Both 1/f and 1/f2 noise
are well-characterized physical phenomena; eg, 1/f distributions have
been demonstrated in various physical systems. The 1/f noise is less
correlated than 1/f2 noise, which reflects a very
high degree of long-range temporal correlation. The present data
show that the worst prognosis is seen in subjects with the highest
degree of long-range temporal correlation in their HR dynamics.
The results show that 24-hour HR variability gives prognostic
information beyond that obtained by traditional risk markers in a
population of elderly subjects. Analysis of long-range
correlation properties of HR data performed better than the traditional
measures of HR variability in predicting the mortality, suggesting that
dynamic analysis of HR behavior may give important
complementary prognostic information in addition to conventional risk
markers.
Selected Abbreviations and Acronyms
HF
=
high frequency
HR
=
heart rate
LF
=
low frequency
SDNN
=
standard deviation of all R-R intervals
ULF
=
ultra–low frequency
VLF
=
very low frequency
Acknowledgments
This work was supported by grants from the Finnish Foundation
for Cardiovascular Research and the Foundation of Signe
och Ane Gyllenberg, Helsinki, Finland. We are indebted to Tuula Tuikka,
BA, for secretarial help and to Pirkko Huikuri, RN, for
analysis of HR variability from 24-hour ECG
recordings.
References
Top
Abstract
Introduction
Methods
Results
Discussion
References
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|
 |
|
 H. V. Huikuri, V. Jokinen, M. Syvanne, M. S. Nieminen, K. E. J. Airaksinen, M. J. Ikaheimo, J. M. Koistinen, H. Kauma, A. Y. Kesaniemi, S. Majahalme, et al. Heart Rate Variability and Progression of Coronary Atherosclerosis Arterioscler. Thromb. Vasc. Biol., August 1, 1999; 19(8): 1979 - 1985. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
S. M. Pikkujamsa, T. H. Makikallio, L. B. Sourander, I. J. Raiha, P. Puukka, J. Skytta, C.-K. Peng, A. L. Goldberger, and H. V. Huikuri Cardiac Interbeat Interval Dynamics From Childhood to Senescence : Comparison of Conventional and New Measures Based on Fractals and Chaos Theory Circulation, July 27, 1999; 100(4): 393 - 399. [Abstract] [Full Text] [PDF]
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|
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|
 S. Sakata, J. Hayano, S. Mukai, A. Okada, and T. Fujinami Aging and spectral characteristics of the nonharmonic component of 24-h heart rate variability Am J Physiol Regulatory Integrative Comp Physiol, June 1, 1999; 276(6): R1724 - R1731. [Abstract] [Full Text] [PDF]
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