Baroreceptor reflex sensitivity in human neonates: the effect of postmenstrual age
1 Neonatal Intensive Care Unit
2 Department of Clinical Physics
3 Department of Medical Technology, Máxima Medical Center, PO Box 7777, 5500 MB Veldhoven, the Netherlands
4 Department of Physics, Eindhoven University of Technology, Den Dolech 2, 5612 AZ Eindhoven, the Netherlands
5 Department of Pediatrics, division of Neonatology, Academic Hospital Maastricht, PO Box 5800, 6202 AZ Maastricht, the Netherlands
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Abstract
We performed a cross-sectional study in human infants to determine if indices of R–R interval variability, systolic blood pressure (SBP) variability, and baroreceptor reflex sensitivity change with postmenstrual age (PMA: gestational age + postnatal age). The electrocardiogram, arterial SBP and respiration were recorded in clinically stable infants (PMA, 28–42 weeks) in the quiet sleep state in the first days after birth. (Cross-)spectral analyses of R–R interval series and SBP series were performed to calculate the power of low-frequency (LF, indicating baroreceptor reflex activity, 0.04–0.15 Hz) and high-frequency (HF, indicating parasympathetic activity, individualized between the p-10 and p-90 values of respiratory frequency) fluctuations, and transfer function phase and gain. The mean R–R interval, and LF and HF spectral powers of R–R interval series increased with PMA. The mean SBP increased with PMA, but not the LF and HF spectral powers of SBP series. In the LF range, cross-spectral analysis showed high coherence values (> 0.5) with a consistent negative phase shift between R–R interval and SBP, indicating a 3 s lag in R–R interval changes in relation to SBP. Baroreceptor reflex sensitivity, calculated from LF transfer gain, increased significantly with PMA, from 5 (preterm) to 15 ms mmHg–1 (term). Baroreceptor reflex sensitivity correlated significantly with the (LF and) HF spectral powers of R–R interval series, but not with the LF and HF spectral powers of SBP series. The principal conclusions are that baroreceptor reflex sensitivity and spectral power in R–R interval series increase in parallel with PMA, suggesting a progressive vagal maturation with PMA.
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Introduction
The baroreceptor reflex (BR) is the most important regulatory mechanism in the short term control of heart rate (HR) and blood pressure (BP). The BR buffers sudden changes in systemic BP by adapting HR and peripheral vascular resistance. The autonomic nervous system plays an important role in this regulation: HR responses are mediated by both parasympathetic and sympathetic efferent nerve activity, whereas vascular resistance is mainly mediated by sympathetic nerves. Because vascular resistance is difficult to measure, autonomic changes related to BR control are usually studied by evaluating R–R interval and BP changes only. BR sensitivity (BRS) is an important parameter to describe BR function and is classically defined as the slope of the (sigmoid-shaped) BP–R–R interval relationship (R–R interval change per unit of BP change, ms mmHg–1) plotted during the pressure rise (pressor response) or decrease (depressor response), induced by the administration of adrenaline or sodium nitroprusside, respectively (Smyth et al. 1969; Korner et al. 1974; Fritsch et al. 1989). The curve midpoint of the reflex refers to the level at which the reflex responds most effectively to changes in arterial pressure. BRS refers to the magnitude of the reflex response per unit of arterial BP deviation from the operating (physiological) point, which is individually set and may differ from the curve midpoint (Stauss, 2002).
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The pharmacological method of altering the control of the cardiovascular system, however, is limited in neonates for medical ethical reasons. Spectral analysis offers the opportunity to decompose spontaneously occurring fluctuations in BP and R–R interval series into a power spectrum, and to relate the character of the fluctuations to physiological processes. Low-frequency (LF) fluctuations at a frequency of approximately 0.1 Hz are ascribed to the BR activity and high-frequency (HF) fluctuations are associated with respiratory activity and vagal modulation (Akselrod et al. 1985). Cross-spectral analysis (notably transfer function parameter LF gain) between BP and R–R interval fluctuations in the LF band (0.04–0.15 Hz) has been shown to be an estimate of the BRS (de Boer et al. 1987; Robbe et al. 1987; Honzíková et al. 1992; Head et al. 2001). Previously, we showed the feasibility of using cross-spectral analysis to estimate BRS from spontaneous BP and R–R interval fluctuations in stable preterm infants (Andriessen et al. 2003). We found a BRS of approximately 4 ms mmHg–1 in a selected population of stable preterm infants (gestational age, 28–32 weeks) in the first week of life.
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Various neonatal studies show a progressive increase in mean R–R interval and R–R interval variability, with a higher amount of HF relative to total variability with advancing gestational age (Clairambault et al. 1992; Eiselt et al. 1993; Chatow et al. 1995; Sahni et al. 2000). Since the parasympathetic system is mainly responsible for the modulation of respiratory associated HF variability of R–R interval, this suggests a progressive maturation of parasympathetic cardiovascular control with age. However, because these studies lack beat-to-beat BP data, no interpretation can be given on BRS. Gournay et al. (2002) studied BR maturation by measuring BRS in preterm (gestational age, 24–37 week) and full term infants. The BRS was lower in preterm infants (approximately 4 ms mmHg–1) and increased with gestational age (approximately 10 ms mmHg–1). In this study, BRS was calculated from very short time periods (lasting a few seconds) of R–R interval response consistent with BR reflex activity (e.g. bradycardia in response to an increase in SBP or tachycardia in response to a decrease in SBP). However, the use of short time periods precluded analysis of R–R interval or BP variability, and to relate spectral power analysis of R–R interval and BP with BRS. Thus there is a lack of data with which to assess the development of the BR function in human neonates, using beat-to-beat data of R–R interval as well as BP for spectral analysis and estimating the BRS.
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The objectives of the study were to determine (1) the effect of postmenstrual age (PMA, gestational age + postnatal age) on the spectral power of the R–R interval and SBP series; (2) the effect of PMA on BRS, investigated by cross-spectral power analysis of spontaneously occurring fluctuations in SBP and R–R interval series (LF transfer function: coherence, gain and phase); and (3) to study the relationship between BRS and spectral power analysis of R–R interval and SBP series.
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Methods
Subjects
The study group consisted of 32 clinically stable infants (gestational age, 32.1 ± 3.7 weeks; birth weight, 1855 ± 808 g). Thirty infants were studied in the immediate postintensive care phase between 24 h and 7 days of postnatal life; two infants were studied at day 9 and 10, respectively. After correction for the postnatal age of measurement, the study group consisted of 16 preterm infants with a PMA between 28 and 32 weeks, 10 preterm infants with a PMA between 32 and 37 weeks and 6 term infants with a PMA between 37 and 42 weeks.
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Table 1 shows the clinical characteristics of the study group. The primary diagnosis for the preterm infants were respiratory distress syndrome (n = 23), apnoea (n = 2) and hyperviscosity syndrome (n = 1). For term babies, the primary diagnosis were exchange transfusion for hyperbilirubinaemia (n = 1), transient tachypnoea of the newborn or pneumonia (n = 3) and idiopathic neonatal convulsions (n = 2). All infants were appropriate-for-gestational age, according to the Dutch growth charts (Kloosterman, 1970). Arterial catheters were neither inserted nor remained longer in place because of the study. All infants were breathing room air spontaneously. None had echo encephalographic evidence of cerebral haemorrhage or parenchymal lesions. In all subjects echocardiography revealed no structural abnormalities. All infants were judged as cardiovascularly stable without need of cardiotonic drugs (dopamine, dobutamine) or volume expanders at the time of the study. There were no signs or symptoms of asphyxia, respiratory distress, sepsis, or patency of the ductus arteriosus at the time of measurements. Babies with congenital or chromosomal abnormalities were excluded. Two babies with idiopathic convulsions had normal electroencephalographic tracings at the time of measurements and were on phenobarbital therapy with plasma levels within the therapeutic range. Of the preterm infants who were on caffeine therapy, serum concentration levels were between 10 and 17 mg l–1. Values of electrolytes, blood gas analysis, and haematocrit were all within normal range at the time of the study.
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Informed consent was obtained from the parents of each infant. The study conformed to the standards set by the Declaration of Helsinki. The local Ethics Committee approved the study.
Data acquisition
A bipolar chest lead of the surface electrocardiogram (sample frequency, 512 Hz) and the transthoracic electric impedance waveforms were recorded by a Hewlett Packard neonatal monitor type Merlin (Waltham, MA, USA). Arterial BP (sample frequency, 256 Hz) was measured invasively through a 4-French catheter in the aortic position (n = 26) or in the right radial artery (n = 6), used for routine monitoring of vital functions and intensive care management. A 0.5 ml h–1 infusion of heparinized physiological saline solution was continuously flushed through the catheter. Recordings were made in the prone position for 1–2 h. Data segments were selected subsequently during periods of regular breathing and spontaneous sleep with closed eyes and without gross body movements (quiet sleep state) (Prechtl, 1974). Data analysis was performed on 192-s-long segments. Because preterm infants have an immature and irregular respiratory drive, these 192-s-long segments were chosen as a compromise between the demands of sufficient duration and signal stability. The data selection during the quiet sleep state resulted in R–R interval artefact free segments.
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Data analysis
With respect to details of the spectral analysis we refer to a previous report (Andriessen et al. 2003). After detection of the R waves from the electrocardiogram, the beat-to-beat R–R interval time series were resampled to obtain equidistant R–R interval time series. SBP was identified from peak detection of the BP signal, resulting in an unevenly time spaced ‘systogram’. The ‘systogram’ was converted into an equidistantly spaced time series using the same resampling method as used for the R–R interval time series. Each 192-s-long segment of equidistant R–R and SBP time series was subdivided into five half-overlapping 64-s segments. A fast Fourier transform was used to compute the auto- and cross-spectral density functions for each of these 64-s segments. A mean power spectrum was derived from five spectral density functions and the spectral power values in the ranges of interest were calculated. The data analysis included procedures to remove the direct current component and to reduce effects of spectral leakage. The data acquisition and analysis software package was developed at the department of Clinical Physics of our hospital in collaboration with the department of Physics of Eindhoven University of Technology, the Netherlands.
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Two frequency bands were defined, as indicated in Fig. 1. The LF band, reflecting BR activity, was defined between 0.04 and 0.15 Hz. Because the HF band primarily contains the reflection of respiratory associated parasympathetic activity, the HF band was individualized for each subject depending on his or her respiratory drive as described earlier (Andriessen et al. 2003). The respiratory rate was estimated by peak detection of the thoracic waveforms resulting in a mean value and a bandwidth between the 10th and 90th centiles of the breath-by-breath frequency. The frequency range between these centiles was used to identify each subject's individual HF band. The upper spectral limit of the HF band was less than half of the mean HR thereby meeting the requirements of the Nyquist critical frequency. The very low frequency band (0–0.04 Hz) was discarded to avoid the possible contribution of slow trend artefacts. The total frequency (TF) band of interest was the range between 0.04 and 1.5 Hz. Spectral power was calculated in each defined frequency band. The values for spectral power are presented in units of ms2 (for R–R interval series) and mmHg2 (for SBP series).
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This figure shows the spectral power curves of R–R interval (A) and SBP (B) series. The spectral powers (R–R interval series, ms2; SBP series, mmHg2) are distributed as a function of frequency (Hz). The low-frequency (LF) band was defined between 0.04 and 0.15 Hz and is marked between vertical lines. The individual high-frequency (HF) band was defined between the 10th and 90th centile of the individual respiratory frequency (0.70 and 0.90 Hz, respectively) and is marked between vertical lines. In the LF band clear peaks in both power spectra are observed at 0.08 Hz, assumed to be attributed to oscillation of the baroreceptor reflex. A HF peak at 0.80 Hz is clearly visible in the SBP power spectrum and corresponds with the mean respiratory rate of the subject. In the R–R interval power spectrum a modest HF peak is visible.
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Transfer function analysis (Fig. 2)
In addition to the spectral density power, the transfer function (coherence, transfer gain and phase) was calculated, as previously described in detail (Andriessen et al. 2003). The coherence function was computed to assess the amount of linear coupling between SBP and
This figure shows the coherence function for the linear relationship between R–R interval and SBP series (A) and corresponding transfer gain (B) and transfer phase (C) as a function of frequency (Hz). The vertical lines indicate the boundaries of the low-frequency (LF) and high-frequency (HF) bands. Note the high coherence values in both LF (0.73) and HF bands (0.79). The frequency value with highest coherence in LF was chosen to compute LF transfer gain and phase (indicated as dots). LF transfer gain and phase were 2.3 ms mmHg–1 and 1.5 s, respectively. The negative phase relationship indicates that SBP fluctuations lead R–R interval changes by 1.5 s and are probably related to baroreceptor reflex activity. Likewise, at HF transfer gain and phase can be calculated: 0.9 ms mmHg–1 and 0 s, respectively. At HF phase is zero, indicating SBP and R–R interval fluctuations are oscillating together and not related to baroreceptor reflex activity.
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R–R interval in the frequency domain. The coherence function, which ranges from 0 (no relationship) to 1 (linear relationship) was used to assess the statistical reliability of the transfer function at each frequency. Only data in which the coherence was above 0.5 were used in the estimation of the transfer gain and phase (de Boer et al. 1987; Robbe et al. 1987). Transfer gain reflects the degree to which the input signal (SBP) amplitude becomes manifest in the output signal (R–R interval) amplitude at a discrete frequency. At LF, transfer gain is an estimate for BRS (de Boer et al. 1987; Robbe et al. 1987; Honzíková et al. 1992; Head et al. 2001). The phase difference (degrees or seconds) indicates the lead or lag of one signal with respect to the other at a discrete frequency. In the case of a closed-loop system, judgement considering causality is limited, because the mathematical techniques do not clarify which signal leads the other. However, consistent with BR activity is R–R interval lengthening in response to increase in BP, or R–R interval shortening in response to a decrease in BP (Drouin et al. 1997). In our study, a negative phase relation indicates SBP fluctuations lead R–R interval changes. As indicated in Fig. 2, transfer gain and phase were assessed in each frequency band at the frequency of highest coherence value.
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Statistical analysis
Data with a normal distribution are expressed as the mean ± S.D. The non-parametric spectral power values of R–R interval and SBP series and transfer function variables (frequency, coherence, gain and phase) are expressed as median and inter-quartile range (IQR). Comparisons of variables between the three PMA groups were statistically evaluated with analysis of variance (one-way ANOVA) and post hoc Scheffé's test for parametric data and the Kruskal-Wallis H test and Mann-Whitney U test for non-parametric data, with Bonferroni's correction for multiple comparisons. The correlation between PMA and indices of R–R and SBP variability was evaluated with Spearman's rho rank correlation test for non-parametric data. Likewise, Spearman's rho correlation was used to evaluate the correlation between indices of R–R and SBP variability and transfer function variables. The effect of possible covariables (sex, antenatal corticosteroids, caffeine and surfactant) on transfer functions gain and phase were studied with multiple regression analysis. Statistical significance was accepted with a P-value less than 0.05. All analyses were performed using the statistical software package SPSS version 11.5 (SPSS Inc., Chicago, IL, USA).
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Results
R–R interval and SBP were both significantly correlated with PMA (R–R interval = 7 x PMA + 203 ms, r = 0.61, P < 0.01; SBP = 0.5 x PMA + 39 mmHg, r = 0.42, P < 0.05).
An example of R–R interval and SBP power spectra of a spontaneously regular-breathing preterm subject measured at a PMA of 31 weeks is shown in Fig. 1. Note in the power spectra the following features: (1) a clear spectral peak is centred on 0.1 Hz in the LF band, both in the R–R interval and SBP power spectra; (2) a clear HF spectral peak is centred on 0.80 Hz in the SBP power spectrum and a modest HF peak is seen in the R–R interval power spectrum; and (3) the LF (0.04–0.15 Hz) and individual HF band (0.70–0.90 Hz) are indicated by vertical dotted lines.
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Table 2 displays the spectral power parameters for the three PMA groups. In general, for R–R interval series, the values of all spectral power parameters (LF-, HF-, total) were significantly higher in the term infants compared with the preterm infants. For SBP series, lower total and HF spectral values were observed in the term infants compared with preterm infants. Table 3 shows the correlation coefficients between PMA and the spectral power values of the R–R interval and SBP series, respectively.
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Figure 2 illustrates the transfer function between SBP and R–R interval signals of the same preterm subject as in Fig. 1. Note that high coherence values are calculated for the LF and HF band. The corresponding transfer gain and phase are indicated by dots in the figure. The LF transfer gain (in this subject 2.3 ms mmHg–1) was used to estimate the BRS. In this example, LF SBP fluctuations lead R–R interval changes by 1.5 s.
For the total population, the median coherence value between SBP and R–R interval in the LF band was 0.65 (IQR, 0.58–0.73). The high coherence values indicate reliable estimates of the LF transfer function parameters phase and gain. LF transfer phase did not correlate with PMA. At LF, SBP fluctuations lead R–R interval changes by a median of 2.6 s (IQR, –3.8 to –2.1). Linear regression analysis showed that LF transfer gain (BRS) significantly correlated with PMA (BRS = 1.1 x PMA – 30 ms mmHg–1, r = 0.80, P < 0.01) as shown in Fig. 3. In a multiple regression analysis, sex, antenatal corticosteroids, caffeine and surfactant did not significantly influence the linear regression between PMA and LF transfer gain. No significant differences were found in transfer function values between data obtained from the aortic or radial artery catheter. HF transfer phase was close to 0 s and the HF transfer gain was significantly lower than LF transfer gain (HF transfer data not shown). Table 2 displays the details of the LF transfer function values for each PMA group.
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Low-frequency (LF) transfer gain, as an estimate for baroreceptor reflex sensitivity, correlated significantly with postmenstrual age (PMA).
Table 3 shows the correlation coefficients between the LF transfer gain and the spectral power values of R–R interval and SBP series. Transfer LF gain correlated significantly with LF-, HF- and total spectral power values of the R–R interval series. By contrast, LF transfer gain did not show a significant correlation with the spectral values of SBP series.
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Discussion
In this paper the effects of PMA (range, 28–42 weeks) on indices of R–R interval variability, BP variability and BRS were studied in 32 cardiovascularly stable infants. Intra-arterial BP and electrocardiogram recordings were used to calculate spectral parameters of R–R interval series and SBP as well as an index for BRS, based on the LF transfer gain between R–R intervals and SBP. The major findings are that parasympathetically mediated fluctuations in R–R intervals and BRS increase with advancing PMA and that the increase in BRS is possibly related to maturation of the vagal system.
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Data about the ontogeny of the BR and functional maturation in the human infant are limited and conflicting (Gootman, 1991; Mazursky et al. 1998). This is partly caused by the limited experimental (pharmacological or mechanical) possibilities to challenge the BR in neonates. The passive head-up tilt test has been applied to neonates, measuring responses in BP, HR and limb blood flow to body tilting. In preterm infants (gestational age, 26–37 weeks), passive head-up tilt resulted in significant vasoconstriction of the lower limb with a slight fall in aortic BP and unchanged HR (Waldman et al. 1979). The inadequate ability to maintain BP and the lack of a tachycardia suggest that preterm infants lack the fully integrated BR response as seen in adults. In term infants, however, a fall in BP was observed in conjunction with a tachycardia and a fall in limb blood flow, suggesting the presence of active reflex vasoconstriction (Picton-Warlow & Mayer, 1970). Others, however, conclude that term as well as preterm babies (gestational age, 33–37 weeks) show a well-developed HR response to passive head-up tilt (Finley et al. 1984). A clear HR response to passive head-up tilt is present in full-term babies after 2 h of birth and progressively increases within the first postnatal day (Chen et al. 1995). By contrast, some other studies do not show consistent evidence of a well-developed BR-mediated HR response in the neonate (Moss et al. 1968; Holden et al. 1985). The disparity in results may be, beside gestational age, explained by methodological differences, age at study, smoothness of tilting procedure, sleep state and the methods of measurement. Thus, the available studies of HR response to passive head-up tilt in human neonates suggest at least qualitatively a BR-mediated HR response is present in early postnatal life.
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Only a few studies in the human infant have estimated the BRS quantitatively from spontaneously occurring fluctuations in BP and R–R interval series (Drouin et al. 1997; Gournay et al. 2002; Andriessen et al. 2003). Notwithstanding different methodology (time domain or frequency domain analysis), the ‘spontaneous’ BRS obtained from these studies show comparable values, ranging from approximately 3–5 ms mmHg–1 (very preterm infant) to 10–15 ms mmHg–1 (term infant). In addition, in the present study a significant correlation was observed between PMA and BRS. Recently, a comparison of various techniques to estimate the BRS from spontaneously occurring fluctuations in BP and R–R interval showed strongly related results between the time sequence and LF transfer gain method (Laude et al. 2004). Two studies, performed in mechanically ventilated and paralysed human neonates undergoing major surgery, have estimated the pressor and depressor response of the BR by administering adrenaline and sodium nitroprusside, respectively (Murat et al. 1988, 1989). In both studies, the pressor BR slope exceeded that of the depressor BR slope. In term neonates, the mean slopes were 11 (range 3–24) and 4 ms mmHg–1 (range 2–12), respectively. These findings are consistent with results obtained from critically ill near-term neonates (gestational age, 35–42 weeks) during and after extra-corporal membrane oxygenation in which BRS, derived from spontaneous fluctuations in BP and R–R interval, was higher during BP rise than during BP fall (Buckner et al. 1993). In normal conscious humans, reflex parasympathetic stimulation and withdrawal primarily control R–R interval responses to changes in BP (Eckberg, 1980). Because cardiac slowing provoked by raising arterial BP is mediated primarily by an increase in parasympathetic discharge, it is possible that parasympathetic stimulation results in a more marked (pressor) BR response than parasympathetic withdrawal (depressor) in the neonate.
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Despite the methodological differences between several studies, we may conclude that in the human infant: (1) a BR-mediated R–R interval response can be demonstrated in the immediate postnatal period, and (2) BRS is limited in very preterm infants but increases with advancing PMA.
The novelty of the present study is that, in contrast to earlier studies, we investigated for the first time R–R interval as well as SBP variability in a neonatal population with a considerable variation in PMA. With advancing PMA, the mean R–R interval increases, which is assumed to be an effect of progressive parasympathetic activity (Gootman, 1991). With advancing PMA we observed higher (LF-, HF- and total) spectral power values of R–R interval series. Like others (Clairambault et al. 1992; Chatow et al. 1995), we observed a higher amount of HF relative to total spectral power with advancing PMA (Table 2). Since the parasympathetic system is mainly responsible for the modulation of respiratory associated HF variability of R–R interval, this suggests that the parasympathetic contribution increases with PMA. Furthermore, we observed that increase in BRS was significantly correlated with an increase in (LF-, HF- and total) variability of R–R interval series, without a significant correlation in variability of SBP series. The close relationship between BRS and R–R interval variability is also demonstrated in a previous study, in which atropine markedly decreased BRS as well as R–R interval variability, but not SBP variability (Andriessen et al. 2004). Therefore, we think that in human infants BR function mainly depends on parasympathetic modulation. However, BRS was also positively correlated with SBP and R–R interval (Table 3). Thus, we cannot exclude the possibility that the increase in BRS is merely the result of a different operating point on the sigmoid-shaped BR curve due to increasing SBP with advancing PMA.
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Thus, parasympathetically mediated fluctuations in R–R intervals and BRS increase progressively with PMA. In addition, previous and present data indicate that the increase in BRS might be an effect of progressive parasympathetic activity with PMA.
Methodological considerations
A limitation of the study group is its heterogeneity in the underlying pathology of the infants. Compared with term infants, preterm infants differ with respect to primary pulmonary diagnosis, surfactant administration, caffeine therapy for apnoea of prematurity and indomethacin therapy for closing the ductus arteriosus before the measurements. Another limitation is the unequal amount of data between the different PMA groups. As a consequence of our restrictive policy to introduce invasive catheters in infants, especially recruiting cardiovascularly stable non-asphyxiated term neonates with the presence of umbilical arterial catheters was difficult. A limitation is that PMA does not exclusively represent intra- or extrauterine development because it reflects intrauterine (function of gestational age) as well as postnatal maturation. In addition, another limitation might be the effect of antenatal administration of steroids on glucocorticoid-dependent maturation of the BR and fetal HR variability. Antenatal glucocorticoids decrease BRS after birth in a preterm delivered sheep model (Segar et al. 1998). In human fetuses antenatal glucocorticoids transently lower short- and long-term fetal HR variability for 1–3 days after administration and normalizes it at day 4 (Derks et al. 1995). The period between antenatal glucocorticoid administration and postnatal measurement was at least 3 days in this study, and hence it is possible that our postnatal results might reflect the effect of antenatal steroids rather than normal development of the preterm infant. Some of the above-mentioned covariables (caffeine, surfactant, and antenatal glucocorticoids) did not show a significant influence on the linear regression between PMA and BRS. Finally, a limitation concerns the method of estimating BRS from spontaneously occurring fluctuations in R–R interval and SBP. With this method, information about BR-mediated HR response is limited to the physiological gain and operating point, and the overall reflex parameters, such as the range of R–R interval response, level of the upper and lower plateaus, pressor and depressor gain, are not evaluated (Parlow et al. 1995).
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Conclusion
The major findings are that parasympathetically mediated fluctuations in R–R intervals and BRS increase progressively with PMA. We suggest that the increase in BRS is an effect of progressive parasympathetic activity with PMA. Very low BRS values in very preterm infants might have clinical importance because a BR system is essential in the short term regulation of BP, and thus in avoiding hypotensive or hypertensive episodes. BP changes due to intensive care management are presumably much more difficult to handle in the preterm infant and may contribute to conditions that predispose to pathological diseases in the preterm infant.
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2 Department of Clinical Physics
3 Department of Medical Technology, Máxima Medical Center, PO Box 7777, 5500 MB Veldhoven, the Netherlands
4 Department of Physics, Eindhoven University of Technology, Den Dolech 2, 5612 AZ Eindhoven, the Netherlands
5 Department of Pediatrics, division of Neonatology, Academic Hospital Maastricht, PO Box 5800, 6202 AZ Maastricht, the Netherlands
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Abstract
We performed a cross-sectional study in human infants to determine if indices of R–R interval variability, systolic blood pressure (SBP) variability, and baroreceptor reflex sensitivity change with postmenstrual age (PMA: gestational age + postnatal age). The electrocardiogram, arterial SBP and respiration were recorded in clinically stable infants (PMA, 28–42 weeks) in the quiet sleep state in the first days after birth. (Cross-)spectral analyses of R–R interval series and SBP series were performed to calculate the power of low-frequency (LF, indicating baroreceptor reflex activity, 0.04–0.15 Hz) and high-frequency (HF, indicating parasympathetic activity, individualized between the p-10 and p-90 values of respiratory frequency) fluctuations, and transfer function phase and gain. The mean R–R interval, and LF and HF spectral powers of R–R interval series increased with PMA. The mean SBP increased with PMA, but not the LF and HF spectral powers of SBP series. In the LF range, cross-spectral analysis showed high coherence values (> 0.5) with a consistent negative phase shift between R–R interval and SBP, indicating a 3 s lag in R–R interval changes in relation to SBP. Baroreceptor reflex sensitivity, calculated from LF transfer gain, increased significantly with PMA, from 5 (preterm) to 15 ms mmHg–1 (term). Baroreceptor reflex sensitivity correlated significantly with the (LF and) HF spectral powers of R–R interval series, but not with the LF and HF spectral powers of SBP series. The principal conclusions are that baroreceptor reflex sensitivity and spectral power in R–R interval series increase in parallel with PMA, suggesting a progressive vagal maturation with PMA.
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Introduction
The baroreceptor reflex (BR) is the most important regulatory mechanism in the short term control of heart rate (HR) and blood pressure (BP). The BR buffers sudden changes in systemic BP by adapting HR and peripheral vascular resistance. The autonomic nervous system plays an important role in this regulation: HR responses are mediated by both parasympathetic and sympathetic efferent nerve activity, whereas vascular resistance is mainly mediated by sympathetic nerves. Because vascular resistance is difficult to measure, autonomic changes related to BR control are usually studied by evaluating R–R interval and BP changes only. BR sensitivity (BRS) is an important parameter to describe BR function and is classically defined as the slope of the (sigmoid-shaped) BP–R–R interval relationship (R–R interval change per unit of BP change, ms mmHg–1) plotted during the pressure rise (pressor response) or decrease (depressor response), induced by the administration of adrenaline or sodium nitroprusside, respectively (Smyth et al. 1969; Korner et al. 1974; Fritsch et al. 1989). The curve midpoint of the reflex refers to the level at which the reflex responds most effectively to changes in arterial pressure. BRS refers to the magnitude of the reflex response per unit of arterial BP deviation from the operating (physiological) point, which is individually set and may differ from the curve midpoint (Stauss, 2002).
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The pharmacological method of altering the control of the cardiovascular system, however, is limited in neonates for medical ethical reasons. Spectral analysis offers the opportunity to decompose spontaneously occurring fluctuations in BP and R–R interval series into a power spectrum, and to relate the character of the fluctuations to physiological processes. Low-frequency (LF) fluctuations at a frequency of approximately 0.1 Hz are ascribed to the BR activity and high-frequency (HF) fluctuations are associated with respiratory activity and vagal modulation (Akselrod et al. 1985). Cross-spectral analysis (notably transfer function parameter LF gain) between BP and R–R interval fluctuations in the LF band (0.04–0.15 Hz) has been shown to be an estimate of the BRS (de Boer et al. 1987; Robbe et al. 1987; Honzíková et al. 1992; Head et al. 2001). Previously, we showed the feasibility of using cross-spectral analysis to estimate BRS from spontaneous BP and R–R interval fluctuations in stable preterm infants (Andriessen et al. 2003). We found a BRS of approximately 4 ms mmHg–1 in a selected population of stable preterm infants (gestational age, 28–32 weeks) in the first week of life.
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Various neonatal studies show a progressive increase in mean R–R interval and R–R interval variability, with a higher amount of HF relative to total variability with advancing gestational age (Clairambault et al. 1992; Eiselt et al. 1993; Chatow et al. 1995; Sahni et al. 2000). Since the parasympathetic system is mainly responsible for the modulation of respiratory associated HF variability of R–R interval, this suggests a progressive maturation of parasympathetic cardiovascular control with age. However, because these studies lack beat-to-beat BP data, no interpretation can be given on BRS. Gournay et al. (2002) studied BR maturation by measuring BRS in preterm (gestational age, 24–37 week) and full term infants. The BRS was lower in preterm infants (approximately 4 ms mmHg–1) and increased with gestational age (approximately 10 ms mmHg–1). In this study, BRS was calculated from very short time periods (lasting a few seconds) of R–R interval response consistent with BR reflex activity (e.g. bradycardia in response to an increase in SBP or tachycardia in response to a decrease in SBP). However, the use of short time periods precluded analysis of R–R interval or BP variability, and to relate spectral power analysis of R–R interval and BP with BRS. Thus there is a lack of data with which to assess the development of the BR function in human neonates, using beat-to-beat data of R–R interval as well as BP for spectral analysis and estimating the BRS.
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The objectives of the study were to determine (1) the effect of postmenstrual age (PMA, gestational age + postnatal age) on the spectral power of the R–R interval and SBP series; (2) the effect of PMA on BRS, investigated by cross-spectral power analysis of spontaneously occurring fluctuations in SBP and R–R interval series (LF transfer function: coherence, gain and phase); and (3) to study the relationship between BRS and spectral power analysis of R–R interval and SBP series.
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Methods
Subjects
The study group consisted of 32 clinically stable infants (gestational age, 32.1 ± 3.7 weeks; birth weight, 1855 ± 808 g). Thirty infants were studied in the immediate postintensive care phase between 24 h and 7 days of postnatal life; two infants were studied at day 9 and 10, respectively. After correction for the postnatal age of measurement, the study group consisted of 16 preterm infants with a PMA between 28 and 32 weeks, 10 preterm infants with a PMA between 32 and 37 weeks and 6 term infants with a PMA between 37 and 42 weeks.
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Table 1 shows the clinical characteristics of the study group. The primary diagnosis for the preterm infants were respiratory distress syndrome (n = 23), apnoea (n = 2) and hyperviscosity syndrome (n = 1). For term babies, the primary diagnosis were exchange transfusion for hyperbilirubinaemia (n = 1), transient tachypnoea of the newborn or pneumonia (n = 3) and idiopathic neonatal convulsions (n = 2). All infants were appropriate-for-gestational age, according to the Dutch growth charts (Kloosterman, 1970). Arterial catheters were neither inserted nor remained longer in place because of the study. All infants were breathing room air spontaneously. None had echo encephalographic evidence of cerebral haemorrhage or parenchymal lesions. In all subjects echocardiography revealed no structural abnormalities. All infants were judged as cardiovascularly stable without need of cardiotonic drugs (dopamine, dobutamine) or volume expanders at the time of the study. There were no signs or symptoms of asphyxia, respiratory distress, sepsis, or patency of the ductus arteriosus at the time of measurements. Babies with congenital or chromosomal abnormalities were excluded. Two babies with idiopathic convulsions had normal electroencephalographic tracings at the time of measurements and were on phenobarbital therapy with plasma levels within the therapeutic range. Of the preterm infants who were on caffeine therapy, serum concentration levels were between 10 and 17 mg l–1. Values of electrolytes, blood gas analysis, and haematocrit were all within normal range at the time of the study.
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Informed consent was obtained from the parents of each infant. The study conformed to the standards set by the Declaration of Helsinki. The local Ethics Committee approved the study.
Data acquisition
A bipolar chest lead of the surface electrocardiogram (sample frequency, 512 Hz) and the transthoracic electric impedance waveforms were recorded by a Hewlett Packard neonatal monitor type Merlin (Waltham, MA, USA). Arterial BP (sample frequency, 256 Hz) was measured invasively through a 4-French catheter in the aortic position (n = 26) or in the right radial artery (n = 6), used for routine monitoring of vital functions and intensive care management. A 0.5 ml h–1 infusion of heparinized physiological saline solution was continuously flushed through the catheter. Recordings were made in the prone position for 1–2 h. Data segments were selected subsequently during periods of regular breathing and spontaneous sleep with closed eyes and without gross body movements (quiet sleep state) (Prechtl, 1974). Data analysis was performed on 192-s-long segments. Because preterm infants have an immature and irregular respiratory drive, these 192-s-long segments were chosen as a compromise between the demands of sufficient duration and signal stability. The data selection during the quiet sleep state resulted in R–R interval artefact free segments.
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Data analysis
With respect to details of the spectral analysis we refer to a previous report (Andriessen et al. 2003). After detection of the R waves from the electrocardiogram, the beat-to-beat R–R interval time series were resampled to obtain equidistant R–R interval time series. SBP was identified from peak detection of the BP signal, resulting in an unevenly time spaced ‘systogram’. The ‘systogram’ was converted into an equidistantly spaced time series using the same resampling method as used for the R–R interval time series. Each 192-s-long segment of equidistant R–R and SBP time series was subdivided into five half-overlapping 64-s segments. A fast Fourier transform was used to compute the auto- and cross-spectral density functions for each of these 64-s segments. A mean power spectrum was derived from five spectral density functions and the spectral power values in the ranges of interest were calculated. The data analysis included procedures to remove the direct current component and to reduce effects of spectral leakage. The data acquisition and analysis software package was developed at the department of Clinical Physics of our hospital in collaboration with the department of Physics of Eindhoven University of Technology, the Netherlands.
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Two frequency bands were defined, as indicated in Fig. 1. The LF band, reflecting BR activity, was defined between 0.04 and 0.15 Hz. Because the HF band primarily contains the reflection of respiratory associated parasympathetic activity, the HF band was individualized for each subject depending on his or her respiratory drive as described earlier (Andriessen et al. 2003). The respiratory rate was estimated by peak detection of the thoracic waveforms resulting in a mean value and a bandwidth between the 10th and 90th centiles of the breath-by-breath frequency. The frequency range between these centiles was used to identify each subject's individual HF band. The upper spectral limit of the HF band was less than half of the mean HR thereby meeting the requirements of the Nyquist critical frequency. The very low frequency band (0–0.04 Hz) was discarded to avoid the possible contribution of slow trend artefacts. The total frequency (TF) band of interest was the range between 0.04 and 1.5 Hz. Spectral power was calculated in each defined frequency band. The values for spectral power are presented in units of ms2 (for R–R interval series) and mmHg2 (for SBP series).
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This figure shows the spectral power curves of R–R interval (A) and SBP (B) series. The spectral powers (R–R interval series, ms2; SBP series, mmHg2) are distributed as a function of frequency (Hz). The low-frequency (LF) band was defined between 0.04 and 0.15 Hz and is marked between vertical lines. The individual high-frequency (HF) band was defined between the 10th and 90th centile of the individual respiratory frequency (0.70 and 0.90 Hz, respectively) and is marked between vertical lines. In the LF band clear peaks in both power spectra are observed at 0.08 Hz, assumed to be attributed to oscillation of the baroreceptor reflex. A HF peak at 0.80 Hz is clearly visible in the SBP power spectrum and corresponds with the mean respiratory rate of the subject. In the R–R interval power spectrum a modest HF peak is visible.
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Transfer function analysis (Fig. 2)
In addition to the spectral density power, the transfer function (coherence, transfer gain and phase) was calculated, as previously described in detail (Andriessen et al. 2003). The coherence function was computed to assess the amount of linear coupling between SBP and
This figure shows the coherence function for the linear relationship between R–R interval and SBP series (A) and corresponding transfer gain (B) and transfer phase (C) as a function of frequency (Hz). The vertical lines indicate the boundaries of the low-frequency (LF) and high-frequency (HF) bands. Note the high coherence values in both LF (0.73) and HF bands (0.79). The frequency value with highest coherence in LF was chosen to compute LF transfer gain and phase (indicated as dots). LF transfer gain and phase were 2.3 ms mmHg–1 and 1.5 s, respectively. The negative phase relationship indicates that SBP fluctuations lead R–R interval changes by 1.5 s and are probably related to baroreceptor reflex activity. Likewise, at HF transfer gain and phase can be calculated: 0.9 ms mmHg–1 and 0 s, respectively. At HF phase is zero, indicating SBP and R–R interval fluctuations are oscillating together and not related to baroreceptor reflex activity.
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R–R interval in the frequency domain. The coherence function, which ranges from 0 (no relationship) to 1 (linear relationship) was used to assess the statistical reliability of the transfer function at each frequency. Only data in which the coherence was above 0.5 were used in the estimation of the transfer gain and phase (de Boer et al. 1987; Robbe et al. 1987). Transfer gain reflects the degree to which the input signal (SBP) amplitude becomes manifest in the output signal (R–R interval) amplitude at a discrete frequency. At LF, transfer gain is an estimate for BRS (de Boer et al. 1987; Robbe et al. 1987; Honzíková et al. 1992; Head et al. 2001). The phase difference (degrees or seconds) indicates the lead or lag of one signal with respect to the other at a discrete frequency. In the case of a closed-loop system, judgement considering causality is limited, because the mathematical techniques do not clarify which signal leads the other. However, consistent with BR activity is R–R interval lengthening in response to increase in BP, or R–R interval shortening in response to a decrease in BP (Drouin et al. 1997). In our study, a negative phase relation indicates SBP fluctuations lead R–R interval changes. As indicated in Fig. 2, transfer gain and phase were assessed in each frequency band at the frequency of highest coherence value.
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Statistical analysis
Data with a normal distribution are expressed as the mean ± S.D. The non-parametric spectral power values of R–R interval and SBP series and transfer function variables (frequency, coherence, gain and phase) are expressed as median and inter-quartile range (IQR). Comparisons of variables between the three PMA groups were statistically evaluated with analysis of variance (one-way ANOVA) and post hoc Scheffé's test for parametric data and the Kruskal-Wallis H test and Mann-Whitney U test for non-parametric data, with Bonferroni's correction for multiple comparisons. The correlation between PMA and indices of R–R and SBP variability was evaluated with Spearman's rho rank correlation test for non-parametric data. Likewise, Spearman's rho correlation was used to evaluate the correlation between indices of R–R and SBP variability and transfer function variables. The effect of possible covariables (sex, antenatal corticosteroids, caffeine and surfactant) on transfer functions gain and phase were studied with multiple regression analysis. Statistical significance was accepted with a P-value less than 0.05. All analyses were performed using the statistical software package SPSS version 11.5 (SPSS Inc., Chicago, IL, USA).
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Results
R–R interval and SBP were both significantly correlated with PMA (R–R interval = 7 x PMA + 203 ms, r = 0.61, P < 0.01; SBP = 0.5 x PMA + 39 mmHg, r = 0.42, P < 0.05).
An example of R–R interval and SBP power spectra of a spontaneously regular-breathing preterm subject measured at a PMA of 31 weeks is shown in Fig. 1. Note in the power spectra the following features: (1) a clear spectral peak is centred on 0.1 Hz in the LF band, both in the R–R interval and SBP power spectra; (2) a clear HF spectral peak is centred on 0.80 Hz in the SBP power spectrum and a modest HF peak is seen in the R–R interval power spectrum; and (3) the LF (0.04–0.15 Hz) and individual HF band (0.70–0.90 Hz) are indicated by vertical dotted lines.
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Table 2 displays the spectral power parameters for the three PMA groups. In general, for R–R interval series, the values of all spectral power parameters (LF-, HF-, total) were significantly higher in the term infants compared with the preterm infants. For SBP series, lower total and HF spectral values were observed in the term infants compared with preterm infants. Table 3 shows the correlation coefficients between PMA and the spectral power values of the R–R interval and SBP series, respectively.
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Figure 2 illustrates the transfer function between SBP and R–R interval signals of the same preterm subject as in Fig. 1. Note that high coherence values are calculated for the LF and HF band. The corresponding transfer gain and phase are indicated by dots in the figure. The LF transfer gain (in this subject 2.3 ms mmHg–1) was used to estimate the BRS. In this example, LF SBP fluctuations lead R–R interval changes by 1.5 s.
For the total population, the median coherence value between SBP and R–R interval in the LF band was 0.65 (IQR, 0.58–0.73). The high coherence values indicate reliable estimates of the LF transfer function parameters phase and gain. LF transfer phase did not correlate with PMA. At LF, SBP fluctuations lead R–R interval changes by a median of 2.6 s (IQR, –3.8 to –2.1). Linear regression analysis showed that LF transfer gain (BRS) significantly correlated with PMA (BRS = 1.1 x PMA – 30 ms mmHg–1, r = 0.80, P < 0.01) as shown in Fig. 3. In a multiple regression analysis, sex, antenatal corticosteroids, caffeine and surfactant did not significantly influence the linear regression between PMA and LF transfer gain. No significant differences were found in transfer function values between data obtained from the aortic or radial artery catheter. HF transfer phase was close to 0 s and the HF transfer gain was significantly lower than LF transfer gain (HF transfer data not shown). Table 2 displays the details of the LF transfer function values for each PMA group.
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Low-frequency (LF) transfer gain, as an estimate for baroreceptor reflex sensitivity, correlated significantly with postmenstrual age (PMA).
Table 3 shows the correlation coefficients between the LF transfer gain and the spectral power values of R–R interval and SBP series. Transfer LF gain correlated significantly with LF-, HF- and total spectral power values of the R–R interval series. By contrast, LF transfer gain did not show a significant correlation with the spectral values of SBP series.
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Discussion
In this paper the effects of PMA (range, 28–42 weeks) on indices of R–R interval variability, BP variability and BRS were studied in 32 cardiovascularly stable infants. Intra-arterial BP and electrocardiogram recordings were used to calculate spectral parameters of R–R interval series and SBP as well as an index for BRS, based on the LF transfer gain between R–R intervals and SBP. The major findings are that parasympathetically mediated fluctuations in R–R intervals and BRS increase with advancing PMA and that the increase in BRS is possibly related to maturation of the vagal system.
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Data about the ontogeny of the BR and functional maturation in the human infant are limited and conflicting (Gootman, 1991; Mazursky et al. 1998). This is partly caused by the limited experimental (pharmacological or mechanical) possibilities to challenge the BR in neonates. The passive head-up tilt test has been applied to neonates, measuring responses in BP, HR and limb blood flow to body tilting. In preterm infants (gestational age, 26–37 weeks), passive head-up tilt resulted in significant vasoconstriction of the lower limb with a slight fall in aortic BP and unchanged HR (Waldman et al. 1979). The inadequate ability to maintain BP and the lack of a tachycardia suggest that preterm infants lack the fully integrated BR response as seen in adults. In term infants, however, a fall in BP was observed in conjunction with a tachycardia and a fall in limb blood flow, suggesting the presence of active reflex vasoconstriction (Picton-Warlow & Mayer, 1970). Others, however, conclude that term as well as preterm babies (gestational age, 33–37 weeks) show a well-developed HR response to passive head-up tilt (Finley et al. 1984). A clear HR response to passive head-up tilt is present in full-term babies after 2 h of birth and progressively increases within the first postnatal day (Chen et al. 1995). By contrast, some other studies do not show consistent evidence of a well-developed BR-mediated HR response in the neonate (Moss et al. 1968; Holden et al. 1985). The disparity in results may be, beside gestational age, explained by methodological differences, age at study, smoothness of tilting procedure, sleep state and the methods of measurement. Thus, the available studies of HR response to passive head-up tilt in human neonates suggest at least qualitatively a BR-mediated HR response is present in early postnatal life.
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Only a few studies in the human infant have estimated the BRS quantitatively from spontaneously occurring fluctuations in BP and R–R interval series (Drouin et al. 1997; Gournay et al. 2002; Andriessen et al. 2003). Notwithstanding different methodology (time domain or frequency domain analysis), the ‘spontaneous’ BRS obtained from these studies show comparable values, ranging from approximately 3–5 ms mmHg–1 (very preterm infant) to 10–15 ms mmHg–1 (term infant). In addition, in the present study a significant correlation was observed between PMA and BRS. Recently, a comparison of various techniques to estimate the BRS from spontaneously occurring fluctuations in BP and R–R interval showed strongly related results between the time sequence and LF transfer gain method (Laude et al. 2004). Two studies, performed in mechanically ventilated and paralysed human neonates undergoing major surgery, have estimated the pressor and depressor response of the BR by administering adrenaline and sodium nitroprusside, respectively (Murat et al. 1988, 1989). In both studies, the pressor BR slope exceeded that of the depressor BR slope. In term neonates, the mean slopes were 11 (range 3–24) and 4 ms mmHg–1 (range 2–12), respectively. These findings are consistent with results obtained from critically ill near-term neonates (gestational age, 35–42 weeks) during and after extra-corporal membrane oxygenation in which BRS, derived from spontaneous fluctuations in BP and R–R interval, was higher during BP rise than during BP fall (Buckner et al. 1993). In normal conscious humans, reflex parasympathetic stimulation and withdrawal primarily control R–R interval responses to changes in BP (Eckberg, 1980). Because cardiac slowing provoked by raising arterial BP is mediated primarily by an increase in parasympathetic discharge, it is possible that parasympathetic stimulation results in a more marked (pressor) BR response than parasympathetic withdrawal (depressor) in the neonate.
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Despite the methodological differences between several studies, we may conclude that in the human infant: (1) a BR-mediated R–R interval response can be demonstrated in the immediate postnatal period, and (2) BRS is limited in very preterm infants but increases with advancing PMA.
The novelty of the present study is that, in contrast to earlier studies, we investigated for the first time R–R interval as well as SBP variability in a neonatal population with a considerable variation in PMA. With advancing PMA, the mean R–R interval increases, which is assumed to be an effect of progressive parasympathetic activity (Gootman, 1991). With advancing PMA we observed higher (LF-, HF- and total) spectral power values of R–R interval series. Like others (Clairambault et al. 1992; Chatow et al. 1995), we observed a higher amount of HF relative to total spectral power with advancing PMA (Table 2). Since the parasympathetic system is mainly responsible for the modulation of respiratory associated HF variability of R–R interval, this suggests that the parasympathetic contribution increases with PMA. Furthermore, we observed that increase in BRS was significantly correlated with an increase in (LF-, HF- and total) variability of R–R interval series, without a significant correlation in variability of SBP series. The close relationship between BRS and R–R interval variability is also demonstrated in a previous study, in which atropine markedly decreased BRS as well as R–R interval variability, but not SBP variability (Andriessen et al. 2004). Therefore, we think that in human infants BR function mainly depends on parasympathetic modulation. However, BRS was also positively correlated with SBP and R–R interval (Table 3). Thus, we cannot exclude the possibility that the increase in BRS is merely the result of a different operating point on the sigmoid-shaped BR curve due to increasing SBP with advancing PMA.
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Thus, parasympathetically mediated fluctuations in R–R intervals and BRS increase progressively with PMA. In addition, previous and present data indicate that the increase in BRS might be an effect of progressive parasympathetic activity with PMA.
Methodological considerations
A limitation of the study group is its heterogeneity in the underlying pathology of the infants. Compared with term infants, preterm infants differ with respect to primary pulmonary diagnosis, surfactant administration, caffeine therapy for apnoea of prematurity and indomethacin therapy for closing the ductus arteriosus before the measurements. Another limitation is the unequal amount of data between the different PMA groups. As a consequence of our restrictive policy to introduce invasive catheters in infants, especially recruiting cardiovascularly stable non-asphyxiated term neonates with the presence of umbilical arterial catheters was difficult. A limitation is that PMA does not exclusively represent intra- or extrauterine development because it reflects intrauterine (function of gestational age) as well as postnatal maturation. In addition, another limitation might be the effect of antenatal administration of steroids on glucocorticoid-dependent maturation of the BR and fetal HR variability. Antenatal glucocorticoids decrease BRS after birth in a preterm delivered sheep model (Segar et al. 1998). In human fetuses antenatal glucocorticoids transently lower short- and long-term fetal HR variability for 1–3 days after administration and normalizes it at day 4 (Derks et al. 1995). The period between antenatal glucocorticoid administration and postnatal measurement was at least 3 days in this study, and hence it is possible that our postnatal results might reflect the effect of antenatal steroids rather than normal development of the preterm infant. Some of the above-mentioned covariables (caffeine, surfactant, and antenatal glucocorticoids) did not show a significant influence on the linear regression between PMA and BRS. Finally, a limitation concerns the method of estimating BRS from spontaneously occurring fluctuations in R–R interval and SBP. With this method, information about BR-mediated HR response is limited to the physiological gain and operating point, and the overall reflex parameters, such as the range of R–R interval response, level of the upper and lower plateaus, pressor and depressor gain, are not evaluated (Parlow et al. 1995).
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Conclusion
The major findings are that parasympathetically mediated fluctuations in R–R intervals and BRS increase progressively with PMA. We suggest that the increase in BRS is an effect of progressive parasympathetic activity with PMA. Very low BRS values in very preterm infants might have clinical importance because a BR system is essential in the short term regulation of BP, and thus in avoiding hypotensive or hypertensive episodes. BP changes due to intensive care management are presumably much more difficult to handle in the preterm infant and may contribute to conditions that predispose to pathological diseases in the preterm infant.
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