From the Department of Biostatistics (M.P., A.P.H.) and the Division of
Cardiology (C.E.F., L.A.C.), University of Washington, Seattle.
Correspondence to Alfred P. Hallstrom, PhD, Department of Biostatistics, University of Washington, 1107 NE 45th, Room 505, Seattle, WA 98105-4689. E-mail aph{at}u.washington.edu
Methods and Results—We explored temporal variation in 6603
out-of-hospital cardiac arrests attended by the Seattle Fire
Department. The data exhibit diurnal variation, with a low incidence at
night and two peaks of approximately the same size (at 8 to 11
AM and 4 to 7 PM). The evening peak is
attributed primarily to the patients found in ventricular
fibrillation, whereas arrests that show other rhythms exhibit mainly a
morning peak. Cardiac arrests associated with survival have more
pronounced diurnal variation than episodes in which survival did not
occur. This difference persists after adjustment for rhythm. For 597
patients who had at least two separate cardiac arrests, we found no
overall association between the times of day of the recurrent arrests.
For women, however, the times of day of the first and second arrests
were closer to each other than one would expect if the times were
entirely unrelated.
Conclusions—Cardiac arrests do not occur randomly during the day,
but rather follow certain periodic patterns. These patterns are
probably associated with patterns of daily activities. The hypothesis
that cardiac arrests are triggered by a person's activity rather than
by some underlying characteristics of his or her disease is supported
by the lack of association between the times of the first and second
arrests in the patients with recurrent arrests.
Circadian variation may differ according to certain
characteristics of the patients. Non–Q-wave
infarctions6 23 and recurrent
infarctions11 are reported to exhibit less
distinctive or no circadian variation. Drugs may suppress diurnal
variation, specifically ß-blockers,4 6 7 12 13
antiarrhythmic drugs,13 and
aspirin.9 These findings have potentially
significant implications for understanding of mechanisms leading to
acute cardiovascular disorders and consequently for
prevention and therapy.24 25 26 27
Studies describing circadian variation of sudden cardiac deaths
use a variety of data sources: death certificates and witness
interviews,16 17 18 populations defined by the
availability of information,21 22 and information
provided by EMS.18 20 28 Reviewing death
certificates would capture all cardiac deaths; however, the diagnosis
and information on death certificates are often imprecise. Furthermore,
these studies would miss all survivors of cardiac arrest. EMS data
usually provide precise time of day. However, their data may not
include all events, because some of the patients are not attended by
EMS. Specific populations, such as residents of nursing homes or
patients with a particular diagnosis who may be followed by some
registry, do not offer widely generalizable results.
This study of temporal variations of cardiac arrests uses a database of
cardiac arrests attended by the SFD, which responds to all emergency
medical calls in the city of Seattle. Using this database of 6603 cases
of out-of-hospital cardiac arrest permitted the study of circadian
variation for several patient subgroups. Particularly unique is the
opportunity to study the time of the recurrent cardiac arrest in
relation to the time of the initial cardiac arrest in 597 patients who
were resuscitated from a previous arrest.
Time of Cardiac Arrest
For unwitnessed arrests and untreated patients, there is no good
surrogate for the time of collapse. Most such patients (99%) are found
in asystole, and there was no means to reliably estimate how long the
patient had been dead. These deaths generally occurred during the
night. In some cases, an interval during which the arrest occurred
might have been estimated, but this is not routinely recorded.
For the analysis of circadian patterns in subgroups, we chose
to restrict analysis to the 3690 witnessed or treated episodes
in which we were reasonably confident of the time of arrest. These
represented
For the analysis involving repeated arrests, the EMS reports,
particularly the narrative, were reviewed, and the time of collapse was
estimated by the center of the interval if one was available and
otherwise, by a random time between 10 PM and 6
AM for those who died at night.17
Circadian Variation
We compared circadian variation in subgroups according to sex, age (16
to 40 years, 41 to 65 years, and
We assumed that missing data were missing at random, and patients with
missing covariate values were omitted from the analyses in
which these covariates were needed. Because certain categories were
rare (specifically the race categories of native Americans and Asians),
some analyses were performed excluding these subgroups.
Repeated Arrests
To investigate whether the time of the initial arrest was similar to
that of the second arrest, we used absolute differences between the two
times of day. We compared the mean, median, and overall distribution
(Kolmogorov-Smirnov test) of these differences to the expected mean,
median, and distribution of the differences under the hypothesis that
there was no association between the times. The expected distribution
was obtained by sampling random pairs from the times of the first and
the second arrests. The set of pairs was resampled 500 times.
We also tested the null hypothesis in specific subgroups; men, women, 3
age categories (16 to 40 years, 41 to 65 years, and
In addition, (1) patients having both cardiac arrests at approximately
the same time of day (differences up to 2 and 4 hours) were compared
with other patients as to their characteristics (sex, age, months
between arrests, medical history, and medications as prescribed at the
time of the second arrest) and (2) a regression model for the absolute
difference between the times of arrests was built to investigate
independent influence of these characteristics.
The pattern of dispatch times of the unwitnessed and untreated cases
exhibits a strong morning peak, between 8 AM and noon,
probably because many people who died at night were discovered in the
morning hours (Figure 2
The distribution of subgroups among the 3690 witnessed or
treated events is shown in Table 1
Age
Rhythm
Baseline covariates are highly correlated (Table 2
Survivor Status
Repeated Arrests
Of the 272 episodes for which the time of onset of second arrest could
not be closely approximated by dispatch time, an interval of <6 hours
is available for 110 episodes, and 59 arrests are known to have
occurred at night. Figure 7
When the mean and the median absolute differences of the arrest times
were compared with those expected under the hypothesis of no
association, no evidence of a significant association between the times
of the arrests was found (P=0.12 and P=0.19 for
the mean and median, respectively). The Kolmogorov-Smirnov test
comparing the distribution of the absolute differences with the
expected distribution also showed no evidence of association
(P=0.64). Subgroups based on sex, age at first arrest, and
length of the period between arrests were separately examined. Women
formed the only subgroup having the times of the first and second
arrests significantly closer together (P=0.029,
P=0.054, and P=0.020 for comparison of the mean,
median, and overall distribution, respectively) than would occur by
chance.
Grouping patients according to whether the times of their two episodes
differed by <4 or >4 hours, we found that only sex
(P=0.0006) and ß-blocker use (P=0.0206)
differed significantly between the groups: women constituted 22.8% and
8.6% of those with and without arrests within a 4-hour window,
respectively; ß-blockers were taken by 12.7% and 27.3%,
respectively. Of those having arrests within a 2-hour window, 42.6%
took antiarrhythmic drugs (other than amiodarone) compared with
57.4% in those with arrests outside a 2-hour window
(P=0.0450).
We built a regression model with the absolute difference between the
arrest times as a response and used sex, ß-blocker use, and
antiarrhythmic use (other than amiodarone) as covariates.
Because of missing values in ß-blocker and antiarrhythmic usage, only
207 patients could be included in the model. In the
multivariate model, only sex (P=0.0238) and
ß-blockers (P=0.0488) were significant: the absolute
difference between the two arrest times is estimated by the model to be
1.75 hours shorter for women than for men, and prescription of
ß-blockers is estimated to increase the difference between the times
of arrest by 1.14 hours.
The EMS is not called in an unknown, but probably small, proportion of
cardiac arrests. Evaluation of the recurrent arrests (Figure 7
Most of the studies reported to date have not had enough cases or
available information to detect differences between the circadian
variation of subgroups with reasonable power. Some studies noted that
no difference was observed between the circadian variation of men and
women16 17 20 and different age
groups.16 17 22 A study using emergency data from
Berlin20 showed that the characteristic bimodal
pattern of the distribution of cardiac arrests was attributed mainly to
the patients who had VF on arrival of EMS. Other rhythms were also
found less frequently at night, but the overall circadian variation was
less pronounced. In the same study, younger patients (<65 years old)
had a low incidence of cardiac arrests at night and a high incidence
during the day, with moderate peaks in the morning and in the evening.
Elderly patients (>65 years old) showed a very prominent morning peak
and almost no evening peak.
In accord with the Berlin study,20 we found that
the bimodal pattern is mainly an attribute of VF and that patients with
asystole or PEA had a less pronounced evening peak. We also observed no
difference in circadian variation between sexes, races, or days of the
week. Our analysis suggests that differences between the age
categories are probably explicable by different rhythm distributions
and thus are more likely due to sociodemographic factors than to
biological differences (eg, older persons are more likely to be living
alone and thus less likely to have their cardiac arrest directly
witnessed with a corresponding prompt call to 911, resulting in a
higher likelihood of being found in asystole compared with VF).
We found a significant difference between circadian variation of
arrests according to whether the victims survived. We suspect that this
may reflect a relation between survival and activity of the patient
before collapse and is not due to any circadian variation in EMS (which
might occur, eg, from out-of-service times). Interestingly, a
difference between survivor and nonsurvivor circadian variation
persists even after rhythm on EMS arrival is adjusted for and is
present if we restrict to VF episodes only.
The concept of circadian variation in arrest times due to biological or
behavioral circadian patterns might be tested in cases in which a
patient serves as his or her own control. However, to the best of our
knowledge, no prior study has analyzed recurrent arrests in
patients who were resuscitated from a first arrest. Because survivors
of cardiac arrests in Seattle are regularly followed up and information
about subsequent arrests is collected, we were able to identify a
relatively large population of patients with recurrent cardiac arrests.
We tested the hypothesis that a given patient is likely to experience
recurrent cardiac arrests at approximately the same time of the day
against the hypothesis that the times of cardiac arrests of a patient
are entirely random, ie, unrelated. Overall, we found no evidence that
the patients had subsequent cardiac arrests at a time of day similar to
their first arrests. In women, however, the second arrest occurred
closer to the time of the first arrest than might be expected by chance
if the times were truly unrelated. This finding did not seem to be
entirely explicable by the difference between prescribed medications
but must be considered hypothesis-generating only because it may well
be a chance result of multiple comparisons. The fact that ß-blockers
suppress circadian variation of myocardial infarction was already noted
in several studies.4 6 7 12 If ß-blockers also
suppress circadian variation of cardiac arrests, we would expect that
the interval between the times of day of the two arrests would widen,
because the expected difference between the arrest times if no
circadian variation is present is larger than in the presence of
circadian variation. Hence, our finding supports the hypothesis of
suppression of circadian variation of cardiac arrests by
ß-blockers.
We do not believe that the overall lack of association between the time
of day of initial and recurrent arrests is due to inadequate sample
size or lack of comparability of the data between different time
periods. The EMS system and associated data capture and management
system in Seattle has been remarkably stable since 1970. Although a
sample size of 325 is not large, it should be adequate for detection of
modest effects; however, we did not find even a trend toward an
association.
Tools for analyzing circadian variation data are imperfect. Although
histograms and harmonic polynomials are useful, one has to be cautious
when interpreting the graphs of circadian variation. Impressions from
histograms often depend on the width and the starting point of the
bars. Harmonic polynomials, although often used to describe circadian
variation, may sometimes exhibit too much of their harmonic nature.
Log-linear models and
Conclusions
Overall, we found no association between the time of the first and the
time of the second arrest for patients with recurrent events. In the
subset of women, however, we found some indication of such association.
The general lack of association between the times of recurrent arrests
may indicate that there is little relationship between the
characteristics of the patient and the time of his or her arrest.
Rather, it may be the patient's activity and/or the environmental
influences that trigger the arrest.
Received December 1, 1997;
revision received February 25, 1998;
accepted March 1, 1998.
© 1998 American Heart Association, Inc.
Clinical Investigation and Reports
Circadian Variations in the Occurrence of Cardiac Arrests
Initial and Repeat Episodes
Abstract
Top
Abstract
Introduction
Methods
Results
Discussion
References
Background—Patterns of temporal
variation of cardiac arrests may be important for understanding
mechanisms leading to the onset of acute cardiovascular
disorders. Previous studies reported diurnal variation of the onset of
cardiac arrests, with high incidence in the morning and in the evening,
lack of daily variation during the week, and some seasonal variation.
The association between the time of day and recurrent cardiac arrests
has not been previously examined.
Key Words: circadian rhythm • heart arrest • cardiovascular diseases
Introduction
Top
Abstract
Introduction
Methods
Results
Discussion
References
Many studies in the
past decade have demonstrated diurnal variation in the onset of acute
cardiovascular disorders, such as myocardial
ischemia,1 2 3 acute myocardial
infarction,4 5 6 7 8 9 10 11 12 cardiac
arrhythmias,13 14 15 and sudden cardiac
death.16 17 18 19 20 21 22 The results consistently
show an increased incidence of acute cardiac events in the morning
hours (6 AM to noon) and a low incidence at night. Some
data suggest another late afternoon or evening peak of incidence.
Methods
Top
Abstract
Introduction
Methods
Results
Discussion
References
Subjects
The database of cardiac arrests was started in 1970 and has
continued to the present without interruption. Cases are identified
by personal review of all run reports submitted by SFD paramedics, and
survivors have been followed up through hospitalization and at least
annually thereafter.29 Among a case series of
6603 out-of-hospital cardiac arrests attended by SFD from March 1985
through February 1993, there were 183 cardiac arrests in children (
15
years old); 166 arrests in patients whose age was unknown; 13 arrests
with unknown time of EMS dispatch; 993 arrests with presumed noncardiac
etiology (eg, trauma, drug overdose, respiratory arrest); and 1558
arrests for which time of collapse could not be estimated because these
were untreated events, ie, pronounced dead on EMS arrival, and either
were unwitnessed or witness status was unknown. The remaining 3690
episodes represent out-of-hospital adult cardiac arrests of
cardiac etiology that were either witnessed (1750) or treated with ALS.
The structure of the data is shown in Table 1
. We were interested in the circadian
variation of the 5248 adult cardiac arrests of cardiac
etiology.
View this table:
[in a new window]
Table 1. Patient Characteristics
For witnessed or treated cases, the time of EMS dispatch was
taken to be the time of cardiac arrest. In these cases, the delay in
requesting aid is highly unlikely to have exceeded 20 minutes. 
70% of the episodes attributed to cardiac
causes (Table 1
). Because others have examined overall circadian
patterns, our primary interest was in how the patterns might differ by
subgroup.
To explore the circadian pattern, we used three techniques: the
histogram (dividing the day into 24 categories of 1-hour length with
centers at whole hours), a kernel estimate (a nonparametric
technique to fit a smooth curve30), and harmonic
polynomials (fitted by the maximum-likelihood method). The
2 test was used to test for the presence of
circadian variation against the hypothesis of uniform distribution of
arrests. 
66 years old), days of the week
(working days versus weekend), rhythm on arrival of emergency personnel
(VF, asystole, PEA), race (white, black, Asian, native American), and
survivor status (resuscitated and discharged alive from hospital, died
in hospital, died with no hospital admission) by the
2 test (dividing the day into six 4-hour
intervals), the Kolmogorov-Smirnov test,31 and a
likelihood ratio test32 associated with the
fitted harmonic polynomials. Log-linear models33
were used to explore the influence of each covariate after adjustment
for the effect of other covariates. For the log-linear models, the day
was divided into six 4-hours intervals.
We identified 597 patients who experienced 2 separate
out-of-hospital cardiac arrests between March 1970 (when the database
was started) and March 1993. For the patients who had more than one
recurrent arrest, we used only the data about the first
recurrence. 66 years old),
and categories according to the length of period between the arrests (0
to 5 months, 5 to 18 months, 18 to 54 months, and 54 months) based
approximately on the quartiles.
Results
Top
Abstract
Introduction
Methods
Results
Discussion
References
Circadian Variation
Figure 1 shows the histogram, the
kernel estimate, and the maximum-likelihood harmonic polynomial
estimate of the distribution of the arrests that were witnessed or that
were treated with ALS (3690). All 3 methods result in a similar
pattern: a low incidence at night, a sharp increase between 8
AM and noon with peaking at 10 AM, a
relatively high incidence during the day, and another peak between 5
and 8 PM. Both morning and evening peaks are of a similar
magnitude. The hypothesis of uniform distribution of the cardiac
arrests over all 24 hours was rejected
(2=252.5, df=24,
P<0.0001).
View larger version (30K):
[in a new window]
Figure 1. Distribution of time of 3690 episodes of
out-of-hospital cardiac arrests that were witnessed or ALS-treated. ML
indicates maximum-likelihood. ).
View larger version (22K):
[in a new window]
Figure 2. Distribution of time of dispatch for 1558
unwitnessed, untreated cardiac-etiology episodes of cardiac
arrests. . No difference in circadian
variation of cardiac arrests was found between men and women (all
P>0.36 for the 2,
maximum-likelihood, and Kolmogorov-Smirnov tests); between whites,
blacks, and Asians (all P>0.12); and between working days
and weekends (all P>0.11). Results for age were equivocal
(for the 2 and the maximum-likelihood,
P>0.23; but for the Kolmogorov-Smirnov test,
P=0.0258) and suggested that elderly patients (66 years
old) have different circadian variation than middle-aged
patients.41–65 Circadian variation differed
significantly according to heart rhythm on EMS arrival
(P<0.0001) and survivor status (P<0.0001).
Figure 3 shows the harmonic
polynomials fitted to the distribution of arrests in three age groups.
The scale of the fitted polynomials was chosen so that the area under
the curve is 1. Young patients had a relatively higher incidence at
night than older patients and also exhibited a higher incidence in the
morning than in the evening. Patients in the age category 41 to 65
years had a higher evening peak. Elderly patients maintained a rather
high incidence during the daytime hours, with less pronounced
peaks.
View larger version (15K):
[in a new window]
Figure 3. Distribution of time of witnessed or treated
episodes according to age of patient.
The distribution of VF events follows the general pattern except
that the evening peak is substantially higher than the morning peak
(Figure 4). Asystole episodes exhibit
primarily a morning peak beginning earlier than for other rhythms.
Cases with PEA follow a bimodal density with a prominent morning
peak.
View larger version (15K):
[in a new window]
Figure 4. Distribution of time of witnessed or treated
episodes according to rhythm on EMS arrival. ): rhythm with age (middle-aged patients
are more likely to be found in VF, whereas the elderly are more often
found in asystole, P<0.0001), rhythm with race (whites have
a higher probability of being found in VF than blacks and Asians,
P=0.007), and age with race (blacks tend to have cardiac
arrests at a younger age, P<0.0001). Among these
characteristics, only rhythm was significantly related to the circadian
distribution of arrests after adjustment for the other factors
(P<0.0001 from the log-linear model).
View this table:
[in a new window]
Table 2. Distribution of Rhythm, Age, and Race
Although the distributions of arrests in the groups determined by
survival status appear similar (Figure 5), they are significantly different. If
patient characteristics, including rhythm, are adjusted for, the
circadian pattern of arrests still differs according to survivor status
(P<0.0001 from the log-linear model). If analysis
is restricted to cases with VF on EMS arrival (Figure 6), patients who died before they could
be admitted to the hospital had less pronounced circadian variation,
with a relatively high incidence at night and no morning peak
(P<0.0001).
View larger version (16K):
[in a new window]
Figure 5. Distribution of time of witnessed or treated
episodes according to survivor status of patient.
View larger version (16K):
[in a new window]
Figure 6. Distribution of time of VF episodes according to
survivor status of patient.
Between March 1970 and March 1993, 597 cases of recurrent events
were observed in the survivors of a prior cardiac arrest. The time of
EMS dispatch can be considered to be within 1 to 20 minutes of the time
of arrest for all resuscitated patients; hence, time of the onset of
the first arrest can be estimated by the time of EMS dispatch. The time
of the second out-of-hospital arrest could be estimated closely for 325
cases: 318 were either witnessed or treated with ALS (251 by SFD and 67
by other medical personnel); the remaining 7 cases were not attended by
medical personnel, but time of collapse was obtained from witnesses.
Table 3 displays the distribution of
covariates for these 325 repeat cardiac arrests.
View this table:
[in a new window]
Table 3. Repeated Arrests Data Structure (top) shows
the distribution of the first and second cardiac arrests in the sample
of 325 patients whose time of second arrest is accurately known.
Compared with the times of first arrest, the distribution of the second
arrest has a higher incidence during the night, with two small peaks of
similar size in the morning and in the evening. The middle panel shows
the distribution for the 435 patients whose time of second arrest was
known either precisely or imprecisely (within 6 hours). For the latter,
the center of the interval was assigned as the time of arrest. The
bottom panel corresponds to 494 patients whose second arrest time was
known either precisely or imprecisely or who died at night. For those
who died at night, a random time between 10 PM and 6
AM was assigned as the time of the second arrest. Circadian
variation of the second arrest when imprecise time arrests were added
was still present (P<0.0001) but was less pronounced
than the circadian variation of the precisely timed second arrests.
View larger version (35K):
[in a new window]
Figure 7. Distribution of time of first and second arrests
for recurrent arrest patients with known precise time of second arrest
(325), known precise or imprecise time of second arrest
(435), known precise or imprecise time or known to have died at night
(494).
Discussion
Top
Abstract
Introduction
Methods
Results
Discussion
References
Most studies of circadian variation of sudden cardiac death have
found a low incidence of events at night and a substantial incidence in
the morning (6 AM to noon).16 17 18 19 20
Some studies reported a secondary peak in the late afternoon or evening
hours, usually around 5 to 7
PM,16 18 20 or sometimes
later.22 Our data support these earlier findings
but suggest the evening peak to be of approximately the same magnitude
as the morning peak of incidence. This discrepancy may be caused partly
by the exclusion of unwitnessed and untreated events from our
analysis. We felt that exclusion of these patients could not be
avoided because, if the patient is pronounced dead on arrival of EMS,
the time of onset cannot be known precisely and may substantially
precede the time of call. Excluded arrests with unknown time of onset
(unwitnessed and untreated) were similar to the witnessed or treated
arrests as to age, race, and day of the week. However, a higher
proportion of the excluded arrests occurred in women (Table 1).
Circadian variation of unwitnessed but treated arrests was very similar
to circadian variation of witnessed arrests, confirming our belief that
the influence of uncertainty of the precise time of onset in
unwitnessed but treated events is minimal. )
suggests how the incidence at night may be underestimated if unattended
arrests are omitted. However, recurrent arrest patients are a specific
subgroup that may differ from the general population of cardiac arrest
patients. Interestingly, the first arrest in the recurrent arrest
patients does not exhibit an evening peak, whereas in general,
recurrences do exhibit an evening peak of about the same size
as the morning peak. 2 tests may depend on
particular partitioning of the time scale. Nevertheless, we believe
that our results are reliable, because they were essentially confirmed
by several analyses. One must be cautious, however, about the
interpretation of subgroup analyses in repeated-arrest data,
because some covariates (especially medication use) had a substantial
number of missing values, and therefore some results are based on a
limited number of observations.
Using the Seattle database of out-of-hospital cardiac
arrests, we confirmed the diurnal variation of the onset of cardiac
arrests reported in other studies. The pattern of diurnal variation
differs depending on the rhythm on arrival of the EMS. The circadian
variation of arrests in those who survived differs from those who did
not survive, even after adjustment for differences in rhythm. This
finding is not explicable by varying availability of emergency care at
different times of day.
Selected Abbreviations and Acronyms
ALS
=
advanced life support
EMS
=
Emergency Medical Services
PEA
=
pulseless electrical activity
SFD
=
Seattle Fire Department
VF
=
ventricular fibrillation
Acknowledgments
This work was supported in part by the Medic One Foundation,
grant R01-HS-08197-03 from the Agency for Health Care Policy and
Research, and contract N01-HC-65042 from the National Heart, Lung, and
Blood Institute.
References
Top
Abstract
Introduction
Methods
Results
Discussion
References
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