Eurly Humun Development. 27 (1991) 187-205 Elsevier Scientific Publishers Ireland Ltd.

187

EHD 01202

Influences on heart rate variability in spontaneously breathing preterm infants Conny M.A. van Ravenswaaij-Artsa, Jeroen C.W. Hopmana, Louis A.A. KollCe”, Joop P.L. van Amen”, Gerard B.A. Stoelinga” and Herman P. van Geijnb *Department

of Pediatrics, Vniversily Hospital Ngmegen and hDepartment of Obstetrics and Gynecology, Free University Hospital Amsterdam (The Netherlands)

(Received

6 May 1991; revision

received

31 October

1991; accepted

6 November

1991)

Summary

To investigate the influence of maturational and physiological factors on heart rate variability in spontaneously breathing very preterm infants (n = 29) a multiparametric study was performed during the first 3 days of life in infants born at a gestational age below 33 weeks. Four times a day, RR-intervals, respiration curve and rate, transcutaneously measured blood gases and observed body movements were recorded while the infants were asleep. All data were stored simultaneously in a micro-computer. Non-invasively measured blood pressure and patency of the ductus arteriosus were documented as well. Four sets of short- (STV) and long term variability (LTV) indices were calculated. Both STV and LTV appeared to be significantly influenced by conceptional and postnatal age in the appropriate for gestational age infants. LTV was influenced by the behavioural state and body movements. During state coincidence 2 (‘active sleep’) LTV was influenced by respiratory rate and the variations in transcutaneous PO,. An effect of blood pressure or ductus patency could not be demonstrated. heart rate variability; long term variability; short term variability; respiratory sinus arrhythmia; preterm infant; neonatal monitoring

Correspondence to: C.M.A. van Ravenswaaij-Arts, Department Nijmegen, P.O. Box 9101, 6500 HB Nijmegen, The Netherlands. Abbreviations: AGA, appropriate for gestational age; HRV, variability; RR, RR-interval length; SGA, small for gestational transcutaneous Pco,; tcPo,, transcutaneous PO,.

0378-3782/91/$3.50 0 1991 Elsevier Published and Printed in Ireland

Scientific

Publishers

of Pediatrics,

University

Hospital

heart rate variability; LTV, long term age; STV, short term variability; tcPco,,

Ireland

Ltd.

188

Introduction The spontaneous cardiac rhythm exhibits oscillations, which are mainly ascribed to respiration (respiratory sinus arrhythmia), baroreceptor reflex activity and thermoregulation [17,35]. A distinction is made between fast oscillations caused by respiration and mediated by the parasympathetic system, and slower oscillations mediated by both the parasympathetic and sympathetic system and mainly caused by changes in vascular resistance [3]. The fast and slow oscillations can be mathematically assessed by a variety of algorithms and presented as a set of indices. It is customary to distinguish short and long term heart rate variability (HRV) indices [22,29,38,44]. HRV is assumed to reflect the condition both in the fetus [22,25] and the neonate. Diseases such as respiratory distress syndrome and intracranial haemorrhage have an effect on neonatal HRV [ 10,20,23]. Many factors, maturational (birthweight, age) and physiological (e.g. respiration, behavioural state) influence HRV. In contrast to the fetal situation most of the physiological parameters are easily accessible in the human newborn. Nearly all studies that have addressed the maturational [ 10,20,36,40] and physiological [ 1,17,32,36,40] influences on HRV have focused on term or near term infants. The very preterm infant is susceptible to various complications especially due to insufficient lung maturation. Respiratory distress syndrome is associated with a marked decrease in neonatal HRV [ 10,231. The effect of respiratory distress syndrome on neonatal HRV may, however, partially be due to secondary circulatory and respiratory changes that usually result from this syndrome. In general, it is not known whether the influence of disease on HRV can partially or completely be explained by associated changes in physiological factors (e.g. increased respiratory rate in respiratory distress syndrome). To distinguish clinical, maturational and physiological factors a large-scale multiparametric study was started. The influence of maturational (birthweight, conceptional age and age after birth), physiological (respiration, blood pressure, transcutaneous blood gases and behavioural state) and clinical (respiratory distress syndrome, patent ductus arteriosus and periventricular haemorrhage) factors was investigated in very preterm infants. The present paper reports on the relative contribution of maturational and physiological factors on HRV in a subgroup of spontaneously breathing preterm infants. Based on studies in less preterm infants it was expected that conceptional age and postnatal adaptation would have a predominant impact on HRV. We wondered to which extent non-invasively measured physiological and postnatal

parameters age.

were related

with HRV after correction

for conceptional

Subjects and Methods Subjects Infants born at a gestational age criteria were presence of congenital and use of cardiovascular or sedative have been admitted to the neonatal

below 33 weeks entered the study. Exclusion malformations, need for resuscitation at birth agents by mother or infant. The infants should intensive care unit within 12 h after birth.

189

Sixty-nine infants were originally included in the study. Twenty-nine of them were breathing spontaneously: 20 during the entire observation period of 3 days and 9 during 1 or 2 days. They were the subjects for the present study. The total number of infants measured on days 1, 2 and 3, respectively, was 21, 26 and 28. Patient characteristics are tabulated in Table I. Infants were considered small-forgestational-age if birthweight was below the tenth percentile of normal birthweights in the Dutch population [24]. Ten infants needed extra oxygen support. The Fro2 was at the most 0.40. Infants with apnea were treated with aminophylline (loading dose 5 mg/kg i.v., followed by 1 mg/kg i.v. three times a day). Although most infants received antibiotics prophylactically, none of them developed clinical symptoms of sepsis. Data acquisition Heart rate, respiration, blood pressure, transcutaneous PO, and Pcoz and motility were assessed at regular intervals (0930, 1330, 1700 and 2 130 h) until 72 h after birth. This period was chosen since most adaptive processes develop during the first 3 days after birth. Recordings were obtained while the infants were asleep and at least 1 h after the last intervention (e.g. feeding, endotracheal suctioning). R-waves in the ECG were detected by the QRS-detector of a Hewlett-Packardmonitor (78801B). The subsequent flash-pulses were timed with a resolution of 0.1 ms by a PDP-1 l/23 micro-computer. Respiration curve and rate (HP 78801B, sampling rate 40 and 5 Hz, respectively), transcutaneous POT and PCO? (Novametrix tco2mette 809A or Sensormedics Transend, sampling rate 2 Hz) and movements observed by the investigator were stored on-line in the micro-computer.

TABLE Patient

I characteristics. Range

Number of infants (n) Conceptional age (weeks) Birthweight (g) Small for gestational age (n) Conceptional age (weeks) Birthweight (g) Mode of delivery (n) Caesarean section Vaginally Age (h) At first measurement At last measurement Patent ductus arteriosus Detectable in (n) Spontaneous closure at (h) Aminophylline Administered in (n) 3rd day serum level (mg/ml) “Median.

29 30” 1400” 4 31” 933”

27-32 850-2280 30-31 850-1000

12 I7 5” 69” 20 II” I8 5.1”

l-11 62-72

O-63

2.9-7.7

190

Distinction was made between small movements (hand or foot), isolated limb movements, startles and more complex movements (general, rotating movements and stretches). The Novametrix was calibrated to zero and in room air. The Sensormedics was automatically calibrated in two gas mixtures. The sensor was attached to the skin of the right upper thorax at least 30 min before the recording was started. In 17 of the 29 infants both tcP@ and tcPCt&, while in others only tcP02 was measured. Measurements were continued until three periods of 3 min in stable condition were marked by the observer. If apnea or bradycardia (i.e. a respiratory pause exceeding 15 s or bradycardia below 100 bpm) occurred the registration was restarted. Subsequently blood pressure was measured non-invasively (Dinamap 1486 SX) from the right arm or right calf. Cuff size was chosen to equal 0.40 of the circumference of the extremity as recommended [4]. At least eight blood pressure measurements, 1 min apart, were obtained. Thereafter, the infant was screened for patent ductus arteriosus by echo-Doppler investigation. Patent ductus arteriosus was considered to be present if both continuous forward flow and diastolic backward flow in the main pulmonary artery could be demonstrated [12]. The total number of measurement series was 293. Data processing Following an artefact-rejection procedure, for each 3-min period four long term variability (LTV) and four short term variability @TV) indices were calculated according to Corometrics [23] (LTV-l (bpm) and STV-1 (ms)), Yeh [44] (LTV-2 and STV-2), Huey [22] (LTV-3 (ms) and STV-3 (ms)) and Van Geijn [38] (LTV-4 (ms) and STV4) (see Appendix). Also the mean RR interval (mean RR (ms)) and mean and standard deviation (S.D.) of respiratory frequency (per min) and transcutaneous blood gases (mean and S.D. tcPOz; mean and S.D. tcPc@ (mmHg)) were calculated for each 3-min period. From the last five blood pressure measurements the medians of systolic, diastolic and mean arterial blood pressure (as measured by the device) were used for further analysis. The extent of movements was scored as no movements (0), only movements of the hand or feet or startles (l), only arm or leg movements (2) or also complex head or trunk movements (3). Sleep state (closed eyes) was scored as state coincidence 1 (Cl, ‘quiet sleep’) if respiration was regular during the entire 3-min period and if no or only small movements or startles occurred (movements score 0 or 1). Coincidence 2 (C2, ‘active sleep’) was defined as irregular breathing during 3 min with or without body movements. Three-minute periods with a transition from Cl to C2 or reverse, or non-classifiable periods were scored as no-coincidence (NC). This simplified scoring system is based on the vectors of behavioural state described by Prechtl and O’Brien [31]. The name ‘state coincidence’ was introduced by Nijhuis et al. [28]. Periods with evident periodic breathing (i.e. short periods of apnea lasting 5-10 s alternated by regular breathing periods [7]) were marked. Statistics The registrations of the 20 infants who were breathing spontaneously during the entire study period were used in most analyses. The data of the 9 other infants were

191

only used when analyses were performed for each postnatal day separately. Each measurement series consisted of three periods. The median values of the simultaneously measured parameters (HRV, mean RR, mean and SD. of respiratory rate, tcP@ and tcPC0J were used to investigate the influence of birthweight, conceptional age, postnatal age and patent ductus arteriosus. This data reduction was not performed in the study on the interrelations of the simultaneously measured parameters (e.g. respiratory frequency and HRV). The effect of conceptional age and birthweight on the patient-averages of STV, LTV, mean RR, blood pressure, mean and SD. of respiratory rate, tcPo, and tcPco, was investigated by calculating Spearman correlation coefficients. Spearman correlation coefficients were also calculated between HRV, postnatal age, and the other parameters for each infant separately, followed by a t-test on the correlation coefficients found. To analyze the effect of sleep state the differences in the recorded parameters were calculated whenever Cl and C2 occurred during the same registration. A t-test was performed on the patient-averages of these differences. A paired t-test was performed on the parameters obtained before and after closure of the ductus arteriosus and before and after the aminophylline loading dose. Kruskal-Wallis or Wilcoxon test were used to investigate the influence of gender, mode of delivery and time of measurement. Wilcoxon test was also used to compare SGA infants with conceptional age-matched AGA infants. Finally, a stepwise regression procedure [34] was used to explore which of the parameters contributed at the most to STV and LTV. All analyses were repeated for state coincidence 1 and 2 and for each postnatal day separately. The results of these extra analyses are only mentioned when additional information was obtained. Results were considered significant at the 0.05 level. Ethical approvement The study was approved by the Ethical Committee Nijmegen and informed parental consent was obtained

of the University for each infant.

Hospital

Results Subjects The most important patient characteristics are shown in Table I. A difference for HRV between the infants born by caesarean section and the others was not found at the first day of life. Also gender, time of measurement and aminophylline appeared not to influence HRV. Birthweight, conceptional age and age after birth Birthweight and conceptional age appeared to correlate significantly with mean RR, STV-1 and all LTV-indices. These correlations were more pronounced with conceptional age than with birthweight. The correlations were most obvious after the first day of life. In Table II the correlation coefficients with conceptional age and birthweight for days 2 and 3 are tabulated. All HRV indices were positively correlated with age after birth in the appropriatefor-gestational age (AGA) infants (Table II). In contrast with the AGA infants, none

192

TABLE

II

Correlation coefficients of birthweight, conceptional and postnatal age with heart rate variability, respiratory rate and mean arterial pressure. Correlation coeffkients of STV- and LTV-indices according to Corometrics (-I), Yeh (-2) Huey (-3) and Van Geijn (-4) and of RR-interval. respiratory frequency (RF) and blood pressure (BP) with birthweight (BW) and conceptional age (CA) and with age. BW” STV-1 STV-2 STV-3 STV4 Mean RR Mean RF

0.65** 0.41 -0. I 1 -0.02 0.66** -0.09

CA” 0.68*** 0.42** 0.0 -0.02 0.57** -0.22

Ageb

BWb

CA”

Ageb

0.45*** 0.46*** 0.43*** 0.40*** -0.18 -0.34**

LTV-I LTV-2 LTV-3 LTV-4 Mean BP

0.52* 0.64** 0.71*** 0.57** 0.08

0.68*** 0.72*** 0.81*** 0.64** 0.13

0.50*** 0.43*** 0.56*** 0.3s*** 0.55***

‘Spearman correlation coefficients of mean values per infant on day 2 and 3. n = 22 AGA infants. bMean of Speannan correlation coefficients of each infant, P-values according to r-test, n = 16 completely measured AGA infants. *P < 0.05, **p < 0.01. ***p < 0.001.

of the four small-for-gestational-age (SGA) infants had a positive correlation of HRV with postnatal age. This resulted in significantly lower STV-1, LTV-2 and LTV-4 on day 2, and lower STV-1 and STV-2 and all LTV-indices on day 3 in the SGA infants compared to conceptional age-matched AGA infants (P c 0.05). Only the AGA infants are represented in Tables II and III. In Fig. 1 the 4 SGA infants are compared with 10 AGA infants with approximately the same conceptional age (30-31 weeks). Mean RR was lower on days 1 and 2 in the SGA infants. No significant differences in respiration, blood gases and blood pressure were found between the two groups. Behaviour

Behavioural states were distributed as follows: Cl, C2 and NC occurred during respectively 29%, 59% and 12% of all 3-min periods. During C2 a significant increase in all LTV-indices and respiratory frequency variability was found which persisted when only 3-min periods with small movements were included in the analysis (Table III). An increase in STV4 and tcPo* variability, and a decrease in RR interval and mean respiratory rate disappeared after exclusion of all periods with movements, except isolated hand or foot movements. In Fig. 2 the relative differences between Cl and C2 periods are visualized. Although HRV tended to be increased during periods marked for evident periodic breathing (56 out of 698 periods), reperformance of the analyses after exclusion of these periods did not reveal different results. Patent ductus arteriosus

In 20 of the 29 infants a non-symptomatic left-to-right shunt through a ductus arteriosus could be detected. Comparison of registrations before and after spon-

III

Cl c2 Cl c2

STV-I

LTV-4

LTV-3

LTV-2

Cl c2 Cl c2

Cl c2 Cl c2

LTV-I

STV4

Cl c2 Cl c2

STV-3

STV-2

Cl c2

n n

(0.7) (1.1) (0.9) (1.0)

53 (7) 94 (IO) 8.0 (1 .O) 15.1 (1.6)

6.8 11.1 52.1 56.8

16.5 (1.6) 22.7 (2.4) 2.72 (0.28) 4.34 (0.8) 179 (19) 236 (41) 28.7 (5.0) 33.3 (5.3)

8 9

Day 1

Conceptional

weeks

53 (9) 99 (II) 8.3 (1.1) 15.1 (1.6)

6.6 (0.6) 1 I .O (0.8) 51.9 (1.1) 56.5 (1.3)

21.2 (1.9) 25.3 (2.1) 4.08 (0.90) 4.45 (0.38) 257 (23) 266 (24) 38.9 (5.2) 38.8 (3.9)

11 13

Day 2

age 27-30

8.6 (0.9) 13.8 (1.6) 53.1 (1.3) 58.2 (2.3) 83 (15) 120 (18) 10.1 (1.4) 15.9 (2.4)

25.8 (3.0) 26.8 (2.3) 4.75 (0.72) 5.70 (0.65) 329 (41) 331 (28) 52.5 (7.7) 51.8 (6.0)

14 15

Day 3

weeks

11.4 (1.2) 14.7 (1.5) 57.2 (1.8) 60.9 (2.3) 102 (12) 151 (20) 13.2 (1.6) 20.4 (2.2)

8.1 11.6 52.7 56.9

69 (8) 106 (20) 10.3 (1.1) 15.6 (2.3)

(0.8) (1.1) (0.9) (1.7)

230 (18) 288 (22) 40.2 (4.0) 43.9 (3.4)

28.9 (4.2) 30.1 (3.8) 4.71 (0.86) 4.87 (0.73) 260 (33) 274 (25) 40.1 (4.8) 45.0 (3.6)

12 13

Day 2

age 31-32

20.8 (2.4) 24.7 (2.1) 3.01 (0.29) 6.09 (1.02)

12 12

Day 1

Conceptional

11.4 (1.1) 16.7 (1.4) 56.1 (1.1) 62.4 (2.1) 121 (18) 184 (21) 13.2 (1.3) 20.2 (1.9)

30.7 (3.5) 36.7 (4.5) 4.43 (0.45) 5.76 (0.66) 310 (32) 356 (36) 51.0 (5.0) 55.4 (4.3)

13 13

Day 3

The influence of conceptional age, postnatal age and sleep state (Cl and C2) on HRV in AGA infants. Mean (standard error) of STV and LTV according to Corometrics (STV-I (ms) and LTV-l (bpm)), Yeh (STV-2 and LTV-2 (X IO-‘)), Huey @TV-3 (ms) and LTV-3 (ms)) and Van Geijn (STV4 and LTV-4 (ms)).

TABLE

194 STV-1 Imsl

LTV-l lbpml 25

45

AGA

SGA

40 35 30 25 20 15 10 5 0 day 1

day 2

day 3

day 1

day 2

day 3

Fig. 1. Influence of postnatal age (days) on LTV and STV according to Corometrics (mean and standard error) in SGA (birthweight < PIO), and AGA infants with conceptional age 30-31 weeks.

% difference 60

***

*a.

*

??

T

40

20

0

-

I ??

I

??? ? ?

-.

-20 RR

I

STV 1

1

2 m

I 3

4

all movements

I 1

2

LTV

/

I

3

4

/

1

I

mRF adRFsdO2

iZZ@small movements

Fig. 2. Influence of sleep state on RR-interval length (RR), STV and LTV according to Corometrics [I], Yeh [2], Huey [3] and Van Geijn [4], respiratory frequency (mRF) and variability (S.D. RF) and tcPo, variability (S.D. 02).The solid bars represent the %I mean difference (and standard error) during coinI without correction for movements (n = 19). The striped bars are cidence 2 compared to coincidence based on analyses after exclusion of registrations with movements of limbs, head or trunk (n = 14). ***p < 0.001. *P < 0.05, **p < 0.01,

195

196

taneous cessation of the ductal shunt did not reveal significant differences in mean RR, STV, LTV, respiratory rate, blood gases or blood pressure. Also if only pairs with identical sleep states were studied no significant differences were found. Respiration, tramcutaneous blood gases and blood pressure The most obvious correlation coefficients (patients average) of HRV with mean RR, mean and S.D. of respiratory frequency, tcPc+ S.D. and mean blood pressure are summarized in Table IV. A significant positive correlation between RR-interval and STV-1, STV-2, LTV-2 and LTV-4 was found. STV4 appeared to be negatively correlated with mean RR. All indices showed a slight negative correlation with mean respiratory rate and a positive correlation with S.D. of respiratory rate. Correlations with mean and S.D. of respiratory rate were more pronounced in LTV- than in STVindices. Small inconsistent correlations were found between HRV and mean tcPoz and tcPc@. However, tcP@-variability was positively correlated with both STV and LTV. Significant correlation coefficients were also found between all HRVindices and blood pressure. Based upon the correlations found, a stepwise regression analysis [34] was performed with the STV- and LTV-indices as dependent variables, for both Cl and C2 periods separately. The SGA infants were not included in this analysis. The results are summarized in Table V. STV is mainly influenced by age after birth and RRTABLE

V

Stepwise regression analysis of factors that might influence heart rate variability in preterm infants. Results of stepwise regression analysis with STV and LTV according to Corometrics (-I), Yeh (-2). Huey (-3) and Van Geijn (-4) as dependent variables and age after birth (h), conceptional age (weeks) (CA), mean RR-interval (ms), mean and S.D. of respiratory rate (per min) (RF), standard deviation of tcPo, (mmHg) and extent of movements as independent variables. The significant partial correlation coefficients (R2) are tabulated and can be interpreted as the reduction in variance of the dependent variable due to addition of the independent variable to the model. The influences found are all based on a positive correlation, except for mean RR in STV-4 and for mean RF. State Cl

Age

State C2

STV- 1

STV-2

STV-3

STV4

STV- 1

STV-2

STV-3

STV-4

0. IO***

0.06***

0.14*** 0.02**

0.07***

0.03**

0.09*** 0.03*** 0.12***

0.01**

0.15***

0.06*** 0.018 0.22*** 0.01*

0.06*** 0.06*** 0.17***

LTV- I

LTV-2

LTV-3

LTV-4

LTV- 1

LTV-2

LTV-3

LTV-4

0.11*** 0.09***

0.04*** 0.021’

0.13*** 0.12*** 0.21***

0.04*** 0.06***

0.06*** 0.12*** 0.06*** O.OS*** 0.01** 0.03*** 0.03***

0.04*** 0.14*** O.Ol*

0.11*** 0.25*** 0.04*** 0.10***

0.01** 0.15***

CA Mean RR Mean RF

Age ‘CA Mean RR Mean RF SD. RF SD. tcPo, Movements

0.3l3***

0.17*** 0.02* 0.07***

0.01; 0.01*

‘P < 0.05, **p < 0.01, ***p

0.06*** c 0.001.

0.04*** 0.05***

0.01*** 0.02***

0.01* 0.02*** 0.02***

0.01** 0.03*** 0.01*

0.05***

197

interval length. LTV is influenced by conceptional age, postnatal age and magnitude of movements. During C2 periods also respiratory rate and tcPoz variability significantly influence LTV. LTV-2 and LTV-4 are strongly influenced by RRinterval length. The stepwise regression analysis was also repeated after exclusion of the 3-min periods with periodic breathing. The same results were obtained. Discussion

Before the clinical applicability of heart rate variability as a monitoring tool in very preterm newborns can be investigated, it is necessary to establish the relationship between HRV and several physiological variables. Hitherto most studies have dealt with only a few parameters in relation to HRV, such as respiratory rate [ 17,321 or transcutaneous blood gases [I]. Also, the influence of behavioural state was not always considered. The aim of this study was to gain insight into physiological influences on HRV previous to the study of the influences of clinical factors on HRV in a comparable study group. Methodological considerations

For practical reasons all infants were measured at the same time of the day. These times were chosen to prevent interference with routine care of the infants. A disadvantage of this approach is that the infants are not studied on exactly the same postnatal ages. On the other hand this time-fixed measurement schedule allowed to study the influence of time of the day on HRV. No evidence for a circadian rhythm in HRV was found. Registrations were only made until the age of 72 h after birth. Therefore no definite conclusions can be drawn with respect to the influence of age on neonatal HRV. The influence of postnatal age found in this study represents the influence of early postnatal adaptation. Eight HRV indices have been applied: four short term and four long term variability indices. They were chosen because of the following reasons: frequent use in neonatal [23] or in fetal [38,44] studies, correction for RR-interval [38] and proven independence of STV and LTV [22]. This selection was partially based on the comparative study of Parer et al. [29]. Reliability of tcP@ and tcPc02 measurements has been confirmed in numerous reports [9,11,21]. We did not obtain supplementary arterial PO? and Pco2 samples for comparison because of ethical reasons. The time delay between arterial PO* and tcPo2 values is in preterm infants only a few seconds [l 11. Oscillometric determination of neonatal blood pressure is an accepted method for non-invasive blood pressure measurement. Extensive studies have shown excellent correlation between direct and oscillometric blood pressure measurement [8,30]. Since most parameters that might influence HRV are interdependent, a stepwise regression analysis was used for final analysis. Maturational influences on HR V

Birthweight apparently was less correlated with HRV than conceptional age (Table II) and its influence disappeared after correction for other parameters (Table V). Since HRV is considered to reflect autonomic nervous system function it is ex-

198

petted to reflect neurological maturity. Both in the fetus [43] and in the newborn infant [20,36] a positive correlation between conceptional age and HRV has been found. We were able to confirm this in preterm infants with a conceptional age between 27 and 32 weeks (Table II). The RR-interval length was also positively correlated with birthweight and conceptional age and thus may have been a confounder. The correlation of especially LTV with conceptional age remained significant when HRV was corrected for RR-interval (stepwise regression, Table V). The correlation of HRV with age after birth has been demonstrated in newborn infants [10,20,40]. After accounting for other parameters that are influenced by age, such as respiratory rate, postnatal age remained an important determinant of HRV (Table V). Since registrations were terminated after the age of 72 h, the influence of postnatal age has to be interpreted as early postnatal adaptation. In contrast to AGA infants, SGA infants did not show a significant increase in HRV with postnatal adaptation. In SGA infants apparently different neurological influences are operative, preventing the adaptive increase in HRV during the early neonatal period. Influence of behavioural state The primary goal of this study was to establish the influence of physiological parameters on HRV. Infants were only measured during sleep, since sleep states represent a rather stable condition compared with active states (states 4 and 5). It is well known that in full term infants HRV is significantly different in active or REM sleep compared with quiet or non-REM sleep [18,36,40]. In the preterm infant behavioural state organization is not completely developed [5], but coincidence of state variables is already present [37]. With a simplified scoring system we were able to categorize 88% of the 3-min periods as state coincidence 1 or 2, based on breathing and movement patterns. When measurements during coincidence 1 and 2 are compared a significant difference in LTV and respiratory rate variability was found (Fig. 2). The difference in breathing rate variability is inherent to the state definition used and was also found by others [ 18,19,36,40]. The increase in LTV during active sleep is a well-known phenomenon [ 18,36,40]. Stepwise regression reveals that during either Cl or C2 periods movements contribute to all LTV-indices (Table V). However, the LTV increase during C2 can only partially be ascribed to movements. When only sleep periods with small movements are studied there remained a significant difference in LTV between Cl and C2. Therefore, another explanation besides movements must be found. In the fetus it has been found that during state coincidence 2F without fetal breathing movements HRV is still increased in comparison to coincidence 1F [41]. Therefore, the increase of LTV during coincidence 2 cannot fully be explained by a change in breathing pattern. It has been proven in healthy term infants and in the fetus that during active sleep sympathetic tone is increased at the expense of parasympathetic tone, resulting in an increase of LTV and a decrease of STV [ 13,391. Although we found an increase in LTV, a decrease in STV could not be detected. This is in accordance with Siassi [36] and might be due to immaturity of the parasympathetic system in the preterm infant. It is well known that the sympathetic system matures earlier in pregnancy than the parasympathetic system [6].

199

Injluence of respiration From stepwise regression it can be concluded that mean respiratory rate and variations in respiratory rate and in tcPo2 significantly influence HRV during C2 periods. In adults respiration causes fast fluctuations in heart rate (respiratory sinus arrhythmia), which are ascribed to several mechanisms including Bainbridge reflex, lung mechanoreceptor activity and a central interaction between the respiratory and cardiovascular center resulting in attenuation of the baroreceptor reflex during inspiration. The effect is a fluctuation in heart rate and blood pressure (Traube-Hering waves) synchronous with respiration [15]. Another fluctuation in heart rate and blood pressure is caused by a delay in the baroreceptor reflex feedback loop. This results in the Mayer-waves of blood pressure and, synchronously, in heart rate oscillations with a frequency of 0.1 Hz in adults [35] and 0.07 Hz in newborns [17]. An important phenomenon with respect to HRV in adults is the entrainment between respiratoryand baroreflex-related oscillations [35]. If respiratory rate approaches 0.1 Hz HRV tends to be augmented. Therefore, in adults STV is maximal at a breathing frequency of 6 per minute (0.1 Hz). Above this frequency there is a decrease of STV with respiratory rate [27]. In newborns respiratory rate is increased, the parasympathetic system is immature, and the sensitive HRV frequency regarding entrainment is around 0.07 Hz, so limited influence from respiratory rate on HRV can be expected [ 171. Nevertheless, a strong negative correlation between respiratory rate and STV in neonates was found by Rother et al. [32]. In contrast, we found a more obvious negative correlation of respiratory rate with LTV than with STV (Table IV). These influences were confirmed by stepwise regression analysis, especially during state coincidence 2 (Table V). Several explanations for the influence of respiration on LTV can be found in the literature. Finley [ 161 recognized a low frequency periodicity in the heart rate pattern of normal newborns during sleep either with or without visible periodic breathing. The periodicity was equal to the frequency of periodic breathing when present. Subsequently Giddens and Kitney [17] and Dykes et al. [ 141 demonstrated that these low frequency heart rate fluctuations were due to fluctuations in respiratory amplitude or tidal volume (breath amplitude sinus arrhythmia). Hathorn [19] has stated that oscillations in tidal volume originate from the delay in the respiratory feed back control system involving the autonomic nervous system mediation of chemoregulation. These oscillations have a frequency of approximately 0.10 Hz during active sleep and 0.12 Hz during quiet sleep. So, the tidal volume oscillations cause low frequency oscillations in heart rate near the sensitive region of 0.07 Hz [ 171. A rise in LTV, e.g. during active sleep in comparison to quiet sleep, may be caused by an increase in oscillation amplitude 1191 or a decrease in oscillation frequency which results in more entrainment. We found an increase in LTV and standard deviation of respiratory frequency during state coincidence 2 compared to coincidence 1. The increase of variations in both respiratory rate and tcPoz is in favor of an increase of tidal volume oscillation amplitude during C2 periods as cause of the LTV increase. Periodic breathing has been considered to represent an extreme form of the spontaneous tidal volume oscillations [42]. In fact, omitting periods with evident periodic breathing in our study (8% of the total number of periods) did not result in a change in the difference in LTV found between Cl and C2 periods.

200

Rother et al. [33] introduced the concept of cardiac aliasing. If the mean respiratory rate exceeds half of the mean heart rate, respiratory sinus arrhythmia occurs at a lower frequency, i.e. equal to the difference between heart rate and respiratory rate. In our population respiratory rate was lower than or approximated half of the heart rate, and thus should have been expressed in STV according to the last hypothesis. Influence of RR-interval length

It is generally assumed that there is a positive correlation between STV and RRinterval length [26,38], probably caused by a decrease in parasympathetic tone related to RR-interval shortening. In our study group of very preterm infants this was only true for STV according to Corometrics (Tables IV and V). The STV according to Van Geijn contains a correction for RR-interval length, determined in a group of full term infants [38] (Appendix). In our population of very preterm infants this correction is introducing an error and results in a negative correlation with RRinterval length. There is no agreement about the influence of RR-interval length on LTV. We found a significant positive correlation between mean RR and the LTV according to Yeh and to Van Geijn. Influence of blood pressure, tcP0, and tcPcoz

Although we found a positive correlation between blood pressure and all HRVindices (Table IV), no obvious influence of blood pressure level on HRV could be found with stepwise regression analysis. The correlation found can be ascribed to age after birth as confounding variable, since both HRV and blood pressure are positively correlated with age (Table II). Only a few studies have been performed regarding the relationship between blood gases and HRV. In neonates with respiratory distress syndrome especially respiratory acidosis causes a decrease of HRV. Ailrimaa et al. [l] described a negative correlation between HRV-indices and tcPC@, probably due to a decrease of pH in the medulla oblongata. However, in a later study they were not able to confirm this with spectral analysis [2]. No clear relationship between mean tcPo* or tcPcoz and HRV was found with stepwise regression in our study. Influence of non-symptomatic patent ductus arteriosus

The influence of non-symptomatic patent ductus arteriosus on HRV was not studied previously. In our patients we could not detect a significant influence of patent ductus arteriosus on HRV. However, the echo-Doppler method used is very sensitive and will also detect very small left-to-right shunts. During the last measurement before spontaneous cessation of the ductal shunt only a minor shunt might have been present in most cases. Conclusions

In conclusion, in spontaneously breathing very preterm AGA infants HRV is predominantly influenced by postnatal adaptation, conceptional age and, dependent on the particular HRV-index used, by heart rate. LTV is also influenced by sleep state and body movements and, during state coincidence 2, by respiratory rate and

201

PO* variability. In SGA infants no significant influence of age on HRV is found and especially LTV is lower on day 2 and 3 compared to AGA infants. If HRV is going to be used as a monitoring tool in neonatal intensive care, nomograms have to be developed regarding conceptional age, postnatal age and behavioural state. HRV is also influenced by respiration, independent of maturational factors. It has to be studied whether the influence of neonatal disease can partially be explained by concomitant changes in physiological factors. Which set of HRV-indices should be used for neonatal monitoring depends on the discriminative power of the indices regarding the severity of neonatal disease. Acknowledgement

The authors wish to thank A. Theeuwes from the Department of Statistical Consultation, Faculty of Medical Sciences, University of Nijmegen, for his cooperation. Appendix Artefact

detection

and correction

An artificial QRS event is defined as either a wrongly detected or a missed pulse. In the former, the correct RR-interval is divided into two or more too short, incorrect intervals. In the latter, a too long, incorrect interval, consisting of the concatenation of the correct intervals, is detected. Depending on the aim of the study the intervals under suspicion can be omitted or have to be corrected. A proven artefact can be repaired based on statistical considerations. Based on the (weighted) mean and S.D. of a number of foregoing RR-intervals (x,+.i through x& the unreliability of the next interval xk can be estimated. In this procedure the number of foregoing intervals (window length L) and the decrease in contribution of the less recent intervals (window shape I+‘[)can be chosen. Repair of an interval is attempted if the unreliability is above a threshold q. Repair of a too short interval consists of omission of one of the QRS events aligning the suspected interval, leading to concatenation of this interval with its following or preceding interval. The alternative that will lead to the largest reduction of the unreliability is considered for acceptance. Repair of a too long interval consists of dividing the interval in two or more equal intervals with a length of approximately the weighted mean. Repair, i.e. concatenation or dividing, is only accepted if the improvement of the reliability of the repaired intervals exceeds a threshold R. Definitions,

practical

Unreliability of xk : xk 2,‘

-

mk

=

sk

Weighted mean:

parameter

values and calculations

used

202

Weighted standard deviation:

Sk

$6

=

i=

wi * xk-i2 -

mk2

I

Window shape: wi = N-’ a

e-ai

= 0.5

Normalisation N=

k i=

constant: Wi

I

L = 20

At the start of the xk series we need an initial estimate for mk. Therefore the series xk is extended with interval values preceding the first measured value defined as x_k = xk. Repair attempt threshold: q = 4, means that xk must be at least four times the weighted standard deviation Sk remote from the weighted mean wk. Repair acceptance threshold R: 1zk’ 1 < R * 1 zk 1 with R = 0.5. If the number of successive or the number of total artefacts exceeds 4 and 10, respectively, the entire registration is rejected. Heart rate variability 1. Corometrics

indices

[23]

STV = average of the beat-to-beat xk

-

RR-interval differences (ms)

xk-l

LTV = difference between P95 and P5 of instantaneous heart rate (bpm) (originally: difference between maximal and minimal heart rate). 2. Yeh [44] STV = Differential index = standard deviation of dk-,

=

xk-I

-xk

xk - I +

*

1ooo

xk

LTV = Interval index = coefficient of variation of RR-intervals, i.e. standard deviation/mean.

203

3. Huey [22] STV = conditional

SUIII

of 1xk - x,+1 1 with: (xk - xk_,) * (xk_, - x&2) < 0

LTV = conditional sum of 1xk - q-1 1 of subseries of three or more beats with: (xk - xk-,) * (xk-, - xk_2) 2 0 4. van Geijn [38] STV = Interval difference index = interquartile

range of

gk * (& - x&l) with:

gk

=

(

,,““,,)”

and ?k =

xk - 1 + xk

2 but if ,,

< 381 ms then gk = 5

LTV = Long term irregularity index = interquartile

range of

jzp

References Alrimaa, T., Kero, P. and Vllimiiki, I. (1985): Simultaneous monitoring gases and heart-rate variability in neonates. Crit. Care Med., 13, 9-11.

of transcutaneous

blood

Aiiirimaa, T., Oja, R., Antila, K. and Vllimaki, I. (1988): Interaction of heart rate and respiration in newborn babies. Pediatr. Res., 24,745-750. Akselrod, S., Gordon, D., Madwed, J.B., Snidman, N.C., Shannon, D.C. and Cohen, R.J. (1985): Hemodynamic regulation: investigation by spectral analysis. Am. J. Physiol., 249, H867-H875. American Heart Association (1988): Recommendations for human blood pressure determination by sphygmomanometers. Circulation, 77, 50lA-514A Arduini, D., Rizzo, G., Giorlandino, C., Valensise, H., Dell’Acqua, S. and Romanini, C. (1986): The development of fetal behavioural states: a longitudinal study. Prenatal Diag., 6, 117-124. Assali, M.S., Brinkman, C.R., Woods, J.R., Dandavino, A. and Nuwayhid, B. (1977): Development of neurohumoral control of fetal, neonatal, and adult cardiovascular functions. Am. J. Obstet. Gynecol., 129, 748-759. Avery, M.E. (1968): The lung and its disorders in the newborn infant. In: Major Problems in Clinical Pediatrics, Vol. I. Saunders Comp, Philadelphia. Baker, M.D., Maisels, M.J. and Marks, K.H. (1984): Indirect BP monitoring in the newborn; evaluation of a new oscillometer and comparison of upper- and lower-limb measurements. Am. J. Dis. Child., 138, 775-778.

204

9

10 II 12

13 14

15 16 17 18 19 20

21 22 23 24 25 26

27 28 29 30 31

32

33

Bucher, H.O., Fanconi, S., Fallenstein, F. and Due, G. (1986): Transcutaneous carbon dioxide tension in newborn infants: reliability and safety of continuous 24-hour measurements at 42°C. Pediatrics, 78, 631-635. Cabal, L.A., Siassi, B., Zanini, B., Hodgman, J.E. and Hon, E.E. (1980): Factors affecting heart rate variability in preterm infants. Pediatrics, 65, 50-56. Cassady, G. (1983): Transcutaneous monitoring in the newborn infant. J. Pediatr., 103, 837-848. Dan&, 0.. Hopman, J.C.W., Stoelinga, G.B.A., Busch H.J. and Peer, P.G.M. (1982): Doppler flow characteristics in the main pulmonary artery and the LA/A0 ratio before and after ductal closure in healthy newborns. Pediatr. Cardiol., 3, 99-104. Di Lieto, A., Catalano, D. and Miranda, L. (1987): The automated quantification of FHR variability beat to beat differences during fetal breathing. J. Perinat. Med., 15 (Suppl. I), 175. Dykes, F.D., Ahmann, P.A., Baldzer, K., Carrigan, T.A.. Kitney, R. and Giddens D.P. (1986): Breath amplitude modulation of heart rate variability in normal 20, 301-308. Feldman, J.L. and Ellenberger, H.H. (1988): Central coordination control in mammals. Annu. Rev. Physiol., 50, 593-606.

full term neonates. of respiratory

Pediatr.

Res.,

and cardiovascular

Finley, J.P. and Nugent, S.T. (1983): Periodicities in respiration and heart rate in new-borns. Can. J. Physiol. Pharmacol., 61, 329-335. Giddens, D.P. and Kitney, R.I. (1985): Neonatal heart rate variability and its relation to respiration. J. Theor. Biol., 113, 759-780. Haddad, G.G., Jeng, H.J., Lai, T.L. and Mellins, R.B. (1987): Determination of sleep state in infants using respiratory variability. Pediatr. Res., 21, 556-562. Hathorn, M.K.S. (1978): Analysis of periodic changes in ventilation in new-born infants. J. Physiol., 285, 85-99. Hornchen, H., Bets, R., Kotlarek, F. and Roebruck, P. (1983): Microprocessor-based long term cardiorespirography: II. Status evaluation in term and premature newborns. J. Perinat. Med., 11, 32-42. Huch, R., Lubbers, D.W. and Huch, A (1974): Reliability of transcutaneous monitoring of arterial PO, in newborn infants. Arch. Dis. Child., 49, 213-218. Huey, J.R., Paul, R.H., Hadjiev, A.AI., Jilek, J. and Hon, E.H. (1979): Fetal heart rate variability: an approach to automated assessment. Am. J. Obstet. Gynecol.. 134, 691-695. Jenkins, J.G., Reid, M.Mc.C. and McClure, B.G. (1980): Study of heart rate variability in sick newborn infants. Acta Paediatr. Stand., 69, 393-396. Kloosterman, G.J. (1970): On intrauterine growth. The significance of prenatal care. Int. J. Gynaecol. Obstet., 8, 895-912. Maulik, D., Saini, V. and Zigrossi, ST. (1983): Clinical significance of short-term variability computed from heart-rate waveforms. J. Perinat. Med., I I, 243-248. Mazza, N.M., Epstein, M.A.F., Haddad, G.G., Law, H.S., Mellins, R.B. and Epstein, R.A (1980): Relation of beat-to-beat variability to heart rate in normal sleeping infants. Pediatr. Res.. 14, 232-235. Mehlsen, J., Pagh, K., Nielsen, J.S., Sestoft, L. and Nielsen, S.L. (1987): Heart rate response to breathing: dependency upon breathing pattern. Clin. Physiol., 7, 115-124. Nijhuis, J.G., Prechtl, H.F.R., Martin, C.B. and Bots, R.S.G.M. (1982): Are there behavioural states in the human fetus? Early Hum. Dev., 6, 177-195. Parer, W.I., Parer, J.T., Holbrook, R.H. and Block, B.S.B. (1985): Validity of mathematical methods of quantitating fetal heart rate variability. Am. J. Obstet. Gynecol., 153, 402-409. Park, M.K. and Menard, SM. (1987): Accuracy of blood pressure measurement by the Dinamap monitor in infants and children. Pediatrics, 79, 907-914. Prechtl, H.F.R. and O’Brien, M.J. (1982): Behavioural states of the full-term newborn: the emergence of a concept. In: Psychobiology of the Human Newborn, pp. 53-73. Editor: P. Stratton. John Wiley and Sons Ltd, New York. Rother, M., Witte, H., Koschel, U., Eiselt, M. and Zwiener, U. (1987): Respiratory sinus arrhythmia in neonates: quantification based on a new model. In: International Working Conference: Progress in Biological Function Analysis by Computer Technologies, pp. 12-03. Berlin. Rother, M., Witte, H., Zwiener, U., Eiselt, M. and Fischer, P. (1989): Cardiac aliasing: a possible cause for the misinterpretation of cardiorespirographic data in neonates. Early Hum. Dev.. 20, I - 12.

205

34 35 36 37

38 39 40

41 42

43 44

SAS Institute Inc. (1985): SAS User’s Guide:‘Statistics, Version 5 Edition. SAS Institute Inc.. Cary, NC. Sayers, B.Mc.A (1973): Analysis of heart rate variability. Ergonomics, 16, 17-32. Siassi, B., Hodgman, J.E., Cabal, L. and Hon, E.H. (1979): Cardiac and respiratory activity in relation to gestation and sleep states in newborn infants. Pediatr. Res., 13, 1163-I 166. Swartjes, J.M., Van Geijn, H.P., Mantel, R., Van Woerden, E.E. and Schoemaker, H.C. (1990): Coincidence of behavioural state parameters in the human fetus at three gestational ages, Early Hum. Dev., 23, 75-83. Van Geijn, H.P., Jongsma, H.W., De Haan, J. and Eskes, T.K.A.B. (1980): Analysis of heart rate and beat-to-beat variability: interval difference index. Am. J. Obstet. Gynecol., 138, 246-252. Van Geijn, H.P., Jongsma, H.W., De Haan, J., Eskes, T.K.A.B. and Prechtl, H.F.R. (1980): Heart rate as an indicator of the behavioral state. Am. J. Obstet. Gynecol., 136, 1061-1066. Van Ravenswaaij-Arts, C.M.A, Hopman, J.C.W., Kollee, L.A.A. and Stoelinga G.B.A. (1990): The influence of physiological parameters on long term heart rate variability in healthy preterm infants. J. Perinat. Med., 18, 131-137. Visser, G.H.A, Carse, E.A, Goodman, J.D.S. and Johnson, P. (1982): A comparison of episodic heart-rate patterns in the fetus and newborn. Br. J. Obstet. Gynaceol., 89, 50-55. Waggener, T.B., Frantz III, I.D., Stark, A.R. and Kronauer, R.E. (1982): Oscillatory breathing patterns leading to apneic spells in infants. J. Appl. Physiol. Respir. Environ. Exercise Physiol., 52, 1288-1295. Wheeler, T. and Murrills, A. Patterns of fetal heart rate during normal pregnancy. Br. J. Obstet. Gynaecol., 85, 18-27. Yeh, S.-Y., Forsythe, A. and Hon, E.H. (1973): Quantification of fetal heart beat-to-beat interval differences. Obstet. Gynecol., 41, 355-363.