Determinants of heart rate variability

JACC Vol. 28, No. 6 November 15. 1996:1539-46

1539

Determinants of Heart Rate Variability HISAKO TSUJI, MD, FERDINAND JANE C. EVANS, MPH,+* MARTIN DANIEL LEVY, MD, FACCtSllll Osaka, Japan; Burlington,

Framingham

J. VENDITTI, G. LARSON,

JR., MD, FACC,* EMILY S. MANDERS. ScD,tS CHARLES L. FELDMAN, ScD,#

BS.?

and Buster, Massachusetts; and Bethesdu. Maryland

O&diver. ibis study sought to etine clinical determinants of heart rate variability and to report normative referencevalues for eight heart rate variability measures. Backg~&. Although the clinical implications of heart rate variability bave been described,clinical determinants and normative values of heart rate variability measures bare not been studied systematicallyin a large community-basedpopulation. hfcthafs. llse lirst 2 II of ambulatory eMrocardiographic mcordiqs obtained in Framingham Heart Study subjectsattending a routine examination were repmcessedfor beart rate variability. Recordingswith !ransient or persistent nonsinus rhythm, premah~ beats >I@% of total beats,
values, derived from a subsetof healthy subjects,were adjusted for age and heart rate. Rcsu&r.There were 2,722 eliible subjects with a mean age (*SD) of 55 f 14 years.Tbm separatemultiple linear regression anulyses revealed that higher heart rate, older age, betaadrenergic Mocking agent use, history of myocardial infarction or congestive heart failure, diuretic use, diastolic blood pressure 290 mm Rg, diabetes mellitus, consumption of three or more cups of co& per day and smoking were associatedwith lower values of one or more heart mte variability measures,whereas longer processedtims start time in tbe nrornin& frequent supraventricular and ventticular premature beats, female Render and systolic blood pressure Z160 mm Hg were associatedwith higher values.Age and heart rate were the major determinants of all three selected heart rate variability measures (partial R’ vahws 0.125 to 0.389). Normative reference values for all eight heart rate variability measuresare presented. CcAu&n.r. Age and heart rate must be taken into account when assessingheart rate variability.

Reduced beat to beat variability in RR intervals, commonly referred to as heart rate variability, is observed in patients with congestive heart failure (I), coronary heart disease (2,3) and diabetic neuropathy (4-6). Various physiologic factors also affect heart rate variability, including aging (7), postural changes (8) and time of day (9). Although prognostic (10-15) and other clinical implications (16-21) of altered heart rate variability have been described, the clinical determinants of heart rate variability have not been studied systematically.Few

previous studies have reported normative reference values for heart rate variability measures (22-24). The objectives of the present study were to assessclinical determinants cf heart rate variability and to provide normative values for several heart rate variability measures as a function of age and heart rate. This investigation was performed in a large population-based sample in which referral bias was inherently minimal.

(J Am Cdl G&id

1996;.!8:1539-46)

Methods From the Kansai Medical University, Osaka. Japan: ‘Lahey Clinic Medical Center, BurlinDon, Massachusetts: tFramingham Heart Study. Framingham, Massachusetts; $Divisions of Epidemiology and Preventive Medicine, Boston University Schcml of Medicine, Boston, Massachusetts; BBrigham and Women’s Hospital, Boston, Massachusetts; 1lNatmnal Hean, Lung and Blond Institute. National Institutes of Health, Bethesda, Maryland, and lDivision of Cardiology and Clinical Epidemiology, Beth Israel Hospital, Boston. Massachusetts. ‘Ihi : sfudy was supported by grants frum the Eleanor Naylor Dana Foundation. New York, New York and CardiudataMortara Inc.. Mihvaukee, Pr~imnsin. Manuscript received September 78. 1995; revised manuscript received May 30. 19%. accepted July 17, 1996. Address for a~rresnnndexx: Dr. Daniel Levy, Framingham Heart Study. 5 Thurber hreet, Framingbam, Massachus&s 01701. 01996 by the American Colkge of Cardiology Fublisked by Ekuvkr Scknce Inc.

Study sample. In 1948, 5.209 residents of Framingham. Massachusetts,who were between the ages of 28 and 62 years, were sekcttd to undergo biennial examinations in a prospective epidemiologic study. In 1971, 5.124 additional subjects (offspring of the o:iginal cohort and spousesof these offspring) entered a’similar prospective study. Selection criteria and study design have been previously reported (25.26). From 1983 to 1987; ambulatory electrocardiograph‘ic (ECG) recording were obtained in surviving study subjects attending a routine. scheduled examination. 0735-1097/96w.oll PII so735-1097(%)m341-7

TSUJI ET AL. DEfERM1NAKt-S

154!J

Abbreviations ECG LF;‘HF PNN.ill r. I

I -MSSD SDNS

JAW OF HEART

RATE

VARIABILITY

avd Acronyms

= elcctrcr;irdiographiu = low frcqucnr):high frqutncy pwcr r& 7 pcrcsntiigc of~diffcrcnccb twwecn 3djacsnf normal RR inwrulr :-XI mh = quart r&t cri the mean of the .qunrrd dikwscsr bct\rccn zdi.1 :mt normal RR intr‘rvals = standard dw.&m nf nnnnal RR intcruls

L-

messing of ambulatory recordings. The first 2 h of ambulatory recordings were processedfor heart rate variability using a Cardiodata/Mortara Mk5 Holter analysissystem. ‘i-h tape speed w;is 1 mm/s, and one channel was used to record a 32-Hz crystal-controlled timing track. For heart rate variability analysis. tapes were played back at 120 times real time on a Cardiodata/Mortara Mk5 Holter analysis system (Mortara Instrument Co.) that sampled each KG channel at 180 samplesb. The playback incorporated a phase-locked loop using the recorded timing track to compensate for errors in recorder speed contra!. Peat to beat RR interval data were obtained from the “beat stream file.” A linearly interpolated beat was substituted for intervals of ectopic beats or artifact less than or equal to two RR intervals (27). The fast Fourier transform was calculated on 100-s blocks of RR interval data. A continuous curve was formed by linear interpolation betwecn RR intervals; this was subjected to a Hamming window and resampled at 1.28 times/s. If there was a run of arrhythmia or artifact >l beat long, the 100-sblock was terminated, the partial block was discarded, and a new block was started at the end of the unusable period. Power density spectrum was estimated by taking the sum of the squaresof the magnitude of the fast Fourier transform performed on all usable 100-sblocks (271. The resulting 100-s power spectra were corrected for attenuation resulting from sampling and the Hamming window and were averaged. Akhough the resulting frequency range of the power spectra was 0.01 to 0.64 Hz, we restricted our analysesto frequencies ~0.40 Hz to maintain comparability of the high frequency component of our data with prev:ous reports (11,12). The 100-s method was intended to avoid any assumptions about sinus node activity in consecutive arrhythmias in which sinus node activity was unknown. When longer time blocks are used, larger amounts of data are discarded becauseof consecutivepremature beats or artifact. In contrast, when shorter time blocks are used, the minimal frequency for which the power spectrum can be measured is >O.Ol Hz. We computed the 2-h power spectral density and calculated five frequency domain measures: 1) very low frequency power (O.Oi to 0.04 Hz); 2) low frequency power (0.04 to 0.15 Hz); 3) high frequency power (0.15 to 0.40 Hz); 4) total power (0.01 to 0.40 Hz); +d 5) the ratio of low frequency to sigh frequency power (LF/HF). In addition, we computed three time domain measures: 1) standard deviation of normal RR intervals (SDNN); 2) the percentage of differences between adjacent normal RR intervals >50 ms (pNN50); and 3) the square root

Nwcmher

Vol. 1X. No. h 15. 1’)9h:15394h

of the mean of th? squared differences betwetin adjacent normal ,RR intervals (r-MSSD). Time domain variables’were calculated from the lUO-s segments used to calculate’ the frequency domain variables. Because SDNN theoretically increasesas a function of recording time. the lrrm 2-h SDNN is used hereafter. We selected high and low frequency pov+cr as frequency domain measures for further analysesbecause rhe physiologic meaning of high frequemy power has been delineated (28). and low frequency power was prrdictivs of prognosis in the elderly population of the Framingham Heart Study (13). Also. both measures are compatible with those of previous reports and are not dependent on the 100-s block method (29). In addition to these two measures, 2-h SDNN was selected because it is conceptually a simpler measure of heart rate variability. Normal QRS complexes and arrhytl.mias were diagnosed under constant visual monitoring. Supraventricular premature beats were differentiated from sinus arrhythmia on the basisof P wave configuration and cyclic changes in RR intervals. For a tape to be eligible for this study. we required that it have ~60 min of analyzable data (11.12), at least 50% time processedand premature beats < 10% of total beats (30). Clinical covariates. Risk factors possibly predisposing to coronary heart disease were recorded at each examination (25). Variables that might be associatedwith autonomic function were also considered, including coffee and al:ohol consumption, current cigarette smoking and use of cardioactive medications. Average reported alcohol consumption was converted to ounces of alcohol/week assuming that a 12-0~bottle or can oE beer had 0.44 oz of alcohol, a glassoi wine 0.40 oz and a measure of spirits 0.57 oz (31). Systolic and diastolic blood pressures were obtained twice in the seated position by the examining physician using a mercury column sphygmomanomeicr positioned near eye level. The average of the two readings was used for each blood pressure variable. Diuberes was defined on the basis of a nonfasting blood glucose level 2200 mg/dl (11.10 mmollliter) or the use of insulin or an oral hypoglycemic agent. Heart rate was calculated as the mean value obtained on the same ambulatory monitoring recording that was processedfor heart rate variability. The number of ventricular premature beats/ hour was analyzed as a continuous variable, but supraventricular premature beats were recoded into three groups as fellows: <120/h, 120 to 240/h and >24O/h. The Framingham definitions of myocardial infarction and congestive heart failure have been described elsewhere (32). At each follow-up examination, interim cardiovascular events were essed with the help. of medical, history, physical examination and the 1Zlead ECG., In addition, medical records were obtained for participants who did not attend an examination, and these were evaluated for evidence of interim heart disease.All suipected interim events were reviewed by a committee of three physicians who evaluated pertinent medical records, hospital records and pathology reports.

JAW Vol. 2x. No. h Ncwmhcr IS. IWh:ISW-Jh

DEl‘ERMINANlS

Table 1. Pratwn Correlations.1mong Measuresof Heart Rate \‘ariahi!ity’

OF HEART

RAIlI

T?XlJl Et- Al.. VARlAHIl.IT1

1541

-

In In VLF+

In LF

In HF

In TPt

LF, HF R&l

Mean

7.37

h.U

5.3 I

SD

0.7.l

0.00

(I.‘:3 0.7ll IuiJ

I.12 l1.51 II.:?

4.41 0.31

0.M

7.3'Io.xl I l.(ti NYC

ll.I!*

11.55

- II.32

ON 0.7.5 il.SY

In VLFt in LF In HF In TPt

In LFlHFruin In l-h SDNN

Il.11

In 2-h SDNN

Il.dU

(l.U7

In pSNW I .50

3. !fl

IX N’ll (I.73

I I.41 Il.hs

O.Vl 11.7' -0.3 11.70

In pNNS0

Statistical methods. Measures of heart rate variability were natural log transformed because their distributions were highly skewed. Associations among transformed variableswere assessedwith Pearson’scorrelation coefficients. In preliminary analyses,mean values of heart rate variability measures were plotted against age (grouped in l@year intervals) and heart rate (grouped in IO-heatlmin intervals) to verify that the relations were linear with constant variance. The principal analysesconsisted of three forward stepwise multiple linear regression procedures (33) to select variables reiated tn heart rate variability (i.e., In 2-h SDNN. In low frequency power, In high frequency power) from among 16 candidate variables: age, gender. heart rate, starting time of moritoring (AM or PM), total processed time (SW vs. BY0 min), systolic(<16Ovs. 2160 mm Hg) and diastolic (240), history of myocardial infarction or congestive heart failure (no vs. yes), presence of diabetes mellitus (no vs. yes), use of beta-adrenergic blocking agents (no vs. yes), use of diuretic drugs (no vs. yes), smoking (no vs. yes), coffee consumption (0 vs. 1 to 2 vs. r3 cups/day) and alcohol intake (0 vs. 1 to 2 vs. r3 ozhveek). These variables were chosen because of their potential physiologic effects on measures nf heart rate variability or because of a possit;le technical influence on the measurement. The Statistical AnalysisSystem procedure REG (34) was used, and each categoric variable was encoded by dummy indicators that were entered or removed as a group. Whereas it is common to define statistical significance with a Type I error rate of alpha 0.05, we adopted a more stringent alpha 0.001 for testing multiple variables across three outcomes; furthermore, we dtfined an “important” variable as onk that contributed a.‘partial R’ statistic ZO.005. Normative values of heart rate variability were generated from a subset of subjects who were free of coronary heart disease, cerebrovascular disease, congestive heart failure and

In r-MSSD

0.71 11.43 (I.iS -- NJI MY 11.93

diabetes mellitus and who were not receiving cardioactive medications. We further restricted this reference sample to subjects 20 to 79 years old with heart rates between 40 and 99 beatslmin. Linear regression was used with the logtransformed data to account for age and heart rate. Estimates were made of the 5th. 25th and 50th percentile values by applying normal theory to the natural log-transformed data before transforming back to the origin;;1 units.

Results Of the 10,333 original and offspring Framingham Heart Study subjects, 5.698 attended the index examination. There were 2,278 (40%) subjects in whom ambulatory recordings were not obtained. Ambulatory ECGs were obtained from June lYS3 to September 1967. Subjects who attended corresponding examinations, but who did not have ambulatory ECGs, spanned a similar period (April 1983 to June 1987). Some basic statistics on subjects who had (vs. those who did not have) ambulatory ECG recordings are as follows: .SZ (vs. 55%) women. mean age 56 (vs. 5X) years. age range 21 to 93 (vs. 18 to 94) years, mean systolic/diastolic blood pressure 129/78 (vs. l30/78) mm Hg. Thus. the two groups were comparable; ,there was no evidence of selection bias. Among 3.420 subjects with tapes analyzed, 698 were excluded from analysis.mostly due to excessiveartifact (n = 500). Compared with excluded subjects (n = 698), those’who were analyzed were .younger (age 55 vs. 62 years. analyzed vs. excluded) but had the same gender distribution (55% vs. 53% female) and had similar blood pressure (129/78 vs. 133/?8 mm Hg) and mean heart rate (73 vs.‘74 beatslmin). The mean age of the 2,722 eligible subjectswas 55 years (range 21 to 93), and 1.236 (45%) were men. The mean heart rate ‘(2 SD) was 73 + 1I beats/min. A history of myocardial infarction was present in 107 subjects (4%) and congestive heart failure in I7 (1%); diabetes was present in 127 subjects (5%).

..

N

JACC Vol. 3. Nn. fi Ntwcmtw I?. l‘J%:lW-4f,

DETERMlNAZlTS

Table 3. Regression Variahilitv Mcasurcs*i

CocUicients

and Partial

R’ Values

CO&

Age 110 yr) HR (.Itl bcatvmin)

il.1 I .-II. I7

Beta-bluctcr

--O.I?

uw

Pruccsud time :+tI Diuretic use History

riin

Variah!ss

R&ted

TSSUJI ET AL. VARIAHILIT\’

RATE

tn Heart

15-n

Rate --

In 2-h SDNS Viiriahle

of Clinica!

OF HEART

NIK

of MI nr CHF

--

Partial I?’ il21S (l.“h -ll.01’)

--.-

CL-d

Partial R‘

.-1l.3h -- Il.33

03SY

-11.33

Il.22

n.13 11SlN

--0.3~ -4.14

t1.152 (ISlo

-

!I.35

cwff

Partial R-’

SS

ES

$

§

--(I.3 -U.4!

o.lYw

$ 0

4

SVPBS 1 ?I&-~40,ll SVPBS >?4Oh

t

Female gender

t

Listed m&l (nu. of terms R-‘) Final mudcl (no. of terms. R’)

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$ (I.!75

I4

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+

In HF

11.1113 B

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In LF

$:I

U.!S

t t

nm 0.007

0

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‘0nly lkse clinical vari;lhla rxmtnhutinfi pafli4 R’ XJ.WS to one or more heti rate vkhility mcaurcz are included here; wknevcr the partial tJ.’ excccdcd O.(N)5, tbc nxresponding p value was <0.OtlO1. I’Otbcr clinical variabks ;~EvL.iatrY with beart rdte variability measures to B lesser eticnt were U f&h assnciatiun is the default): fur bt 2-h SDNN, thcrc were VPIkh

(a minu\ sign indicates invcrsc auxiation

rhcrc*

dirryt

(p < 0.001). nn!ming start time (p x tNltl1) and -dirtiulk (p < I).lLl1). - diastdlc hluud ppgsurc ~90 mm Hg (p < 0.05): for In LF puwer. tbcrc wcx VP&h (p i (l.Oll1). -diaktc+ blaxi pressure ~90 mm Hg (p < 0.05) and morning start timr (p i O.(6): ;tnd fur In HF pmvcr. there wcrc VPbh

(p .: (I.(~!1 ).

HU mm Hg (p < 0.0111). systolic hluud prc%,ure 2 lb0 mm Hg (p < O.OS) and -coffee omsumplbm (p .z 0.05). Only akobol amsumption wiLv,not associated. 81 p < O.M. uitb my of the thnx beart rate variability measures. tVariable was marginally significant with O.OOl c- p < 0.05, but partial R’ was
blood prewre

but partial Rz was ~0.005. 11PartkalR’ for SVPBs &monstrdted amtributium fmm all three categkcs: >2Ql1. NS = variable was not signiticant (p z 0.M) in the stepvis model. - nr + = tign of rrpskn tbe final model: other abbrcviatkns as in Tables I and ?.

effect of a IO-year age difference equaled the effect of a 7- to 1 I-beatlmin heart rate difference.

Normative vahcs of heart rate variability measures. A hubset of 1,918 subjects (mean [? SD] age 50 + 13 years. range 20 to 79 years: 865 [45%] men; heart rate 40 to 99 beats/min) were studied to assess heart rate variability in a healthy reference sample. These subjects were without clinically apparent heart disease and were not receiving cardioactive

Fii 1. Fitted regression lines of 2-h SDNN as a function of age and heart rate for entire study sample. Data are shown for five specific ages (35,45.55,6.5 and 75 years) at mean heart rates between 50 and Ifi)

beatslmin.

~:lZOh. 1% to ?4l!h. caxfficicnt (cxr8) in

medications. Percentile values for all hcdlt rdtr: vanability measures were estimated by combining rcgrcssion techniques and normal statistics theory: these are shown in Table 4 for three age groups and three heart rate categories. Note that as age or heart rate increases. heart rate variability decreasesthe bottom 5th percentile limit is smaller at age 70 than at age 30. Agreement between these estimated pcrcentilcs and empiric data was excellent.

Fire 2. Fitted regression lines of low frequenty (LF) power as a function of age and heart rate for entire study sample. Data are shown for five spLrific ages (3s. 45. 55, 65 and 75 years) at mean heart rates ktween SO and 100 katslmin.

1544

TSlUl ET AL DETERMINAWIX

OF HEART

RATE

JACC Vol. 3. No. 6 Ncwembcr 15, IW6:lWL46

VAKIABILITY

Discussion To our knowledge, this is the first report to quaniitatc the .contributibns of several’determinants of heart rate variability in a large. community-hascd sample. Mean heart rate and age were the major determinants. together accounting for 37% to 5 1 possible that left ventricular hypertrophy (42) or subclinical coronary artery disease confound these associations, The presence of supraventricular and ventricular premature beats a&ted heart rate variability measures.To minimize the effect of premature complexes, we excluded subjects with premature beats >lO% of total beats (30). Nevertheless,

--.----

loo0 l-------

0 ’ 50

60

70 Heat rue

80

90

100

(llMls/mir.;

CizurP3. Fitted rrgrcssion lines of high frrqucncy (HF) purr 6sa function d age and heart rate for entire studysar,;plc.Data arc shown for five speciticages(X,45,55.65 and 75 years)at mean heart rates hctwcrn SO and 100 heats’min.

irequent supraventricular premature bez t!, substantially affected heart rate variability mtiasures. T x: impact of single supraventricular premature beats was miriinized by use of an interpolated correction methd. Con-c&e premature beatc did not affect heart rate variability: wtcn such arrhythmias occurred, the corresponding 100-sblocks mere terminated and discarded. It is possible that the nonunilbrmity of compensatory pauses after ventricular prematmir beats resulted in imprecise interpolation of sinus node activity. Heart raie variability has been reportea (5) to be reduced in patients with diabetic neuropathy, but not in the presence of uncomplicated diabetes. In our study, diabetes was associated with reduced low frequency power, but not with high frequency power or SDNN The lack of association of diabetes with high frequency power or SDNN may have been due to the small number of subjects with diabetes (n = 127), few of whom had diabetic neuropathy. Minor differences in one or more heart rate variability measures were observed for gender, time of day of recording and presence of frequent ventricular premature beats. Comparison with previous studies. A recent study by Bigger et al. (23) reported reference values for a variety of heart rate variability measures in 202 men and 72 women (40 to 69 years old, mean age 57; mean heart rate 73 heats/min). Mean values in that report were similar to those observed in our sample with a mean age of 55 years and a mean heart rate of 73 beatslmin, except for total power and SDNN. The discrepancy in total power is explained by a different sampling method, whereas differences in SDNN are explained by the shorter duration of recordings in our sample. Smaller differences observed in high frequency power, LF/HF ratio and pNNS0 may he due to, differences in sample size,and age range. Limitations of the study. This study was based ,on intermediate-duration recordings, which yield different values for SDNN than shorter or longer recordings. The recordings

JAW

Vol. 3, No. h IS. IWxIW-46

Dt7lERMtS;ANlS

Nwrmhcr

Table

4. Percentile

Values

of Heart

Rate

Variahilit

Measures

Predicted

for Specified

Ages

OF ttt%Rt

and Heart

Rates

TWJI ET AL. VARIARILITY

RATE

in “Hcalth~”

1545

Subjeas’

Percentile

HRV Mrasurc VLFt

LF

HR ibcatsmin)

Slit

TPf

LFIHF

2-h SDNN

PNNM

K-MSSD

S(hh

5th

424u

h.XS

x.:x1

:.r11s

Z.shl

s.nhn

I,)ulc MN

?.7;lh I Ixh I.4M 1.33 XH 7.2 351 171 9,932 4.hl I 2.141 2.25 ‘.4? 2.71 I4n w M 32 H 2 Sb 41 ?I

3,s71 I.554 3,537 1.67 1.Iw)7 1,143 556 270

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I.705 910 4xs 563 274 133 7.4u3 3,474 Ill13 3.n.3 3.32 3 hS

I .4x$ 734 424

HF

5Oth

373 IF1 t;H b.621 3.074 1.42: I.42 I .Sh 1.71 113 80 56 13 3 1 40 24 1s

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4% 71s 3x3 N4 1X4 89 43 3.7h.l

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‘Values were generated kom a subset of subjects who were free of clinically apparent coronary heart discax, ccrehrovascular disease. con@ve hear! failurr and diabetes. and who were no1 reaiving urdioxtive medicationr Linear regression. adjusting fnr age and heart rate. was used with nrlural Iq-Irandomed data Dr heart rate variability (HRV) measures. Estimates 65th. 2SSthand .((hh percentile values were made by applying normal rhcoryro Ihc residuals of Ihr natural log-tramformed data hefore transforming hack tn the original units. tBased on a I(Y)-s sampling method. Otl-cr abbrcvialion, ah :n Tahlr I.

were obtained while subjects underwent an extensive clinic evaluation and are not representative of basal rest conditions. Frequency domain measures were obtained using a 100-s sampling method, which results in a lowest frequency response of 0.01 Hz. Last, the formulas for predicting normative reference values were ob!ained in subjects 20 to 79 years old with a heart rate of 40 to 99 beats/min and should not be extrapolated to values of age or heart rate outside these ranges. Comdusioas. There are many clinical variables associated with reduced or increased heart rate variability. The impact of age and heart rate must be taken into account when evaluating heart rate variability.

References I. Saul JP, kai Y, Berger RD, Lilly IS, Colucei WS. Cohen i. Assessment of autonomic regulation in chrunie mngestive bearl failure by hean rate spectral analysis. Am J Cardiol 198&61:12!I2~9. 2. Airaksinen KE. lkaheimo MJ. Linduofo MK Niemtla hf. Takkunen JT. Impaired vagal heart rate control in mmnary artery disease. Br Heart J 198758592 -7. 3. Hayano J, !&k&ii Y, Yamada M, ei al. Deereased magnitudb of bearr tale rpctral components in mrrmay vtery dkase: its relation to angiograpbie severity. Cimdation l~,R1:1217-24. 4. Ewing DJ, Bmsey DQ, Bellavere F. Clarke BF. Cxdiac auhxmxnic nrump-

arhy in diabeks: mmpxison of measures of R-R inlervdl varialion. Diahetdogia 1Ql;?l:lN-24. S. Stmardi L Ricordi L, Lazari P, et al. Impaired circadian modulation of sympalhovagal acriviry in diabetes: a poGbk explanation for altered kmporaf onset of cardiovascular discase. Circulakm 19%?;R6:144.~52. 6. Weise F. Hcydcnrcich A non-invasive approach to cardiac autonomle neumpalhy in patients with diabetes mcllitus. Clin Physiol 1990;103137-45. 7. Shannon DC, Cariey DW, Benson H. A& of modulrtion of hecut rate. Am J Physid 1987;253(Heart Circ Pbysiol22):HR74-7. 8. Pomeranz 8. Maeaulay RIB. Caudill MA. cl al. Assrswncn~ of aulonkc function in humans by bearl rate qxelral analysis. Am J Pbysiol 198% 24&Heti Cii Pbysiol 17):HlSl-3. 9. Malpas SC. Purdic GL Circadian variation of heart rate variahiliv. Card& vase Rn 199&24:210-3. 10. Kleiger RE. tiiiier JP, Bigger JT Jr. Moss AJ. fnr tbe Mulkemer PosrInk&m Rcscareh Gmup. Decreased beart ralc variability and its association winth increaxd mortality tier acute myocardial infurrion. Am J Car:loi 1987;5~2S6-b2. Il. Bigger JT Jr, Rein JL Steinman RC. RdstitzLy LM. Kkiger RE Rottman JN. kequcncy &main measures of bear, period variability and mortality after myncardial infarckm. Cirmlation 19%?;R5:l64-71. 12. Bigger JT Jr. Reiss JL Rdnitzky LM, Steinman RC. Frcqueney domain ~mtasum of !xart period variabili~, lo assess risk late after myoeardial infarction. J Am Cdl Cardiil 1993;:1:729-36. 13. Tsuji H, Venditti FI Jr. MandenES. et al. Reduced hear! rate variahilily and mortality risk in an elderly cohort: rhe Fmmingham Heart study. Cimdakm 1994;!JO.i37R-R3. 14. Hmsbesky WJM. Fader DJ. Btrcska JS. Sommer M. Hayes J. Cope FD. Diminisbmcnl of respimkny sinus arrbytbmia foresbrdam doxorubiiininduced urdiomyopa~hy. CirculaGon IWl;M:h97-707.

I.%

Tst’JI FT AL. DETERMINAWS

JACC OF HEART

RATE

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