J.C.W. Hopman

Heart Rate Variability

Heart rate variability, that is, the amount of heart rate fluctuations around the mean heart rate, can be used as a mirror of the cardiorespiratory control system. It is a valuable tool to investigate the sympathetic and parasympathetic function of the autonomic nervous system. The most important application of heart rate variability analysis is the surveillance of postinfarction and diabetic patients. Heart rate variability gives information about the sympathetic-parasympathetic autonomic balance and thus about the risk for sudden cardiac death in these patients. Heart rate variability measurements are easy to perform, are noninvasive, and have good reproducibility if used under standardized conditions [1, 2]. Standardized conditions are necessary because heart rate variability is influenced by factors such as respiratory rate and posture. Increasing age is associated with lower heart rate variability, which is comparable to its influence on the classical autonomic function tests [3]. In our overview, we provide a succinct description of these physiologic influences on heart rate variability as well as of methods to measure heart rate variability. The influences of cardiovascular and neurologic disorders on heart rate variability are described in greater detail. During a 4-year period, all new papers concerning the clinical applicability of heart rate variability in fetal, neonatal, and adult medicine were collected (with the help of Current Contents, ISI, Philadelphia). For this review we selected papers from this collection and, if necessary, gathered less recent but relevant papers. Physiology of Heart Rate Variability Because of continuous changes in the sympathetic-parasympathetic balance, the sinus rhythm exhibits fluctuations around the mean heart rate. Frequent small adjustments in heart rate are made by cardiovascular control mechanisms (Figure 1). This results in periodic fluctuations in heart rate. The main periodic fluctuations found are respiratory sinus arrhythmia and baroreflex-related and thermoregulation-related heart rate variability [4, 5]. Figure 1. Scheme of the cardiovascular control mechanisms responsible for the main periodic fluctuations in heart rate. Due to inspiratory inhibition of the vagal tone, the heart rate shows fluctuations with a frequency equal to the respiratory rate [6]. The inspiratory inhibition is evoked primarily by central irradiation of impulses from the medullary respiratory to the cardiovascular center. In addition, peripheral reflexes due to hemodynamic changes and thoracic stretch receptors contribute to respiratory sinus arrhythmia [4]. Fluctuations with the same frequency occur in blood pressure and are known as Traube-Hering waves [7]. Respiratory sinus arrhythmia can be abolished by atropine or vagotomy [4, 8] and is parasympathetically mediated. The so-called 10-second rhythm in heart rate originates from self-oscillation in the vasomotor part of the baroreflex loop. These intrinsic oscillations result from the negative feedback in the baroreflex [9] and are accompanied by synchronous fluctuations in blood pressure (Mayer waves) [7]. The frequency of the fluctuations is determined by the time delay of the system. They are augmented when sympathetic tone is increased [10-12] and they decrease with sympathetic or parasympathetic blockade [4, 12]. Peripheral vascular resistance exhibits intrinsic oscillations with a low frequency [13, 14]. These oscillations can be influenced by thermal skin stimulation [15] and are thought to arise from thermoregulatory peripheral blood flow adjustments. The fluctuations in peripheral vascular resistance are accompanied by fluctuations with the same frequency in blood pressure and heart rate [15] and are mediated by the sympathetic nervous system. Heart Rate Variability Measurement Heart rate variability can be assessed in two ways: by calculation of indices [16] based on statistical operations on R-R intervals (time domain analysis) or by spectral (frequency domain) analysis of an array of R-R intervals [4]. Both methods require accurate timing of R waves. The analysis can be performed on short electrocardiogram (ECG) segments (lasting from 0.5 to 5 minutes) or on 24-hour ECG recordings. Time Domain Analysis Two types of heart rate variability indices are distinguished in time domain analysis (Figure 2, top). Beat-to-beat or short-term variability (STV) indices represent fast changes in heart rate. Long-term variability (LTV) indices are slower fluctuations (fewer than 6 per minute). Both types of indices are calculated from the R-R intervals occurring in a chosen time window (usually between 0.5 and 5 minutes). An example of a simple STV index is the standard deviation (SD) of beat-to-beat R-R interval differences within the time window. Examples of LTV indices are the SD of all the R-R intervals, or the difference between the maximum and minimum R-R interval length, within the window. With calculated heart rate variability indices, respiratory sinus arrhythmia contributes to STV, and baroreflex- and thermoregulation-related heart rate variability contribute to LTV. Figure 2. Example of an adult heart rate trace. Top. Bottom. Twenty-four-hour ECG recordings are frequently used by cardiologists to calculate heart rate variability. For instance, the SD of all R-R intervals within the 24-hour recording, or the mean of the SD of R-R intervals within successive 5-minute periods, is calculated [17-19] (Table 1). These 24-hour indices of heart rate variability also encounter ultradian rhythms (that is, with a period length greater than 1 hour) in heart rate. Table 1. Heart Rate Variability as a Marker of Prognosis after Myocardial Infarction* Frequency Domain Analysis Since spectral analysis was introduced as a method to study heart rate variability [5, 20], an increasing number of investigators prefer this method to that of calculation of heart rate variability indices Figure 2, bottom). The main advantage of spectral analysis of signals is the possibility to study their frequency-specific oscillations [7, 21, 22]. Thus not only the amount of variability but also the oscillation frequency (number of heart rate fluctuations per second) can be obtained. Spectral analysis involves decomposing the series of sequential R-R intervals into a sum of sinusoidal functions of different amplitudes and frequencies by the Fourier transform algorithm. The result can be displayed (power spectrum) with the magnitude of variability as a function of frequency [23]. Thus the power spectrum reflects the amplitude of the heart rate fluctuations present at different oscillation frequencies (see Figure 2, bottom). Spectral analysis can be performed on a short-lasting heart rate trace of 0.5 minute to several minutes. The individual R-R intervals are obtained by R-wave detection. The subsequent array of R-R intervals must be free of artifacts (for example, missed or spurious R waves). To perform a Fourier function on a time-limited signal, the signal must be periodic and stationary [7]. The series of time intervals between consecutive R waves can be treated as if these intervals are equally spaced (a function of R-R interval length against R-R interval number). The Fourier transformation will then result in a spectrum with power as a function of frequency expressed in cycles per beat. The expression can be transformed in Hertz by dividing by mean R-R interval length. To obtain a real data sequence of events equally spaced in time, the sequential R-R intervals are considered as a function of time, interpolated, and subsequently sampled. To obtain a stationary signal, a detrending procedure must be performed. This can be done by subtracting a least-square polynomial approximation from the original signal or by high-pass filtering [7]. Respiratory sinus arrhythmia gives a spectral peak around the respiratory frequency, the baroreflex-related heart rate fluctuations are found as a spectral peak around 0.1 Hz in adults [4], and the thermoregulation-related fluctuations are found as a peak below 0.05 Hz (see Figure 2, bottom). Measurement Conditions Heart rate variability can be studied under spontaneous conditions or with provocation; for example, standing or head-up tilt (increase in sympathetic tone) or deep breathing at a rate of 6 breaths per minute (increase in respiration-related heart rate variability). A 24-hour Holter ECG recording is part of the routine investigations following an acute myocardial infarction. In most of the studies concerning postinfarction patients, therefore, heart rate variability has been established using these 24-hour ECG recordings. In other fields of medicine, for example, regarding diabetic autonomic neuropathy, short-lasting ECG records (ranging from 0.5 to 10 minutes) have been used to calculate spectral and nonspectral heart rate variability indices. These short-lasting measurements were nearly always performed under standardized conditions with and without autonomic nervous system stimulation (that is, tilt and deep breathing). Commercially Available Equipment Commercial devices to assess 24-hour heart rate variability are now available. The conventional tape recorders for Holter monitoring may show variations in tape speed that may cause erroneous STV results [24]. Therefore speed control is necessary with the help of a timing track, that is, simultaneously recorded, crystal-generated timing pulses. The only study that we know of that evaluates commercially available heart rate variability equipment is the study of Molgaard and colleagues [24] concerning the Pathfinder II system. This system corrects for tape speed errors and has a high accuracy of QRS detection but contains no correction for artificially long R-R intervals [24]. The effect of artificially long R-R intervals depends on the heart rate variability index used. Maturational and Physiologic Influences on Heart Rate Variability Maturity of the Autonomic Ne

Photoacoustic determination of blood vessel diameter.

A double-ring sensor was applied in photoacoustic tomographic imaging of artificial blood vessels as well as blood vessels in a rabbit ear. The peak-to-peak time (tau(pp)) of the laser (1064 nm) induced pressure transient was used to estimate the axial vessel diameter. Comparison with the actual vessel diameter showed that the diameter could be approximated by 2ctau(pp), with c the speed of sound in blood. Using this relation, the lateral diameter could also precisely be determined. In vivo imaging and monitoring of changes in vessel diameters was feasible. Finally, acoustic time traces were recorded while flushing a vessel in the rabbit ear with saline, which proved that the main contribution to the laser-induced pressure transient is caused by blood inside the vessel and that the vessel wall gives only a minor contribution.

Validation of transpulmonary thermodilution cardiac output measurement in a pediatric animal model

Objective: This study was undertaken to validate the transpulmonary thermodilution cardiac output measurement (COTPTD) in a controlled newborn animal model under various hemodynamic conditions with special emphasis on low cardiac output. Design: Prospective, experimental, pediatric animal study. Setting: Animal laboratory of a university hospital. Subjects: Twelve lambs. Interventions: We studied 12 lambs under various hemodynamic conditions. Cardiac output was measured using the transpulmonary thermodilution technique with central venous injections of ice-cold saline. An ultrasound transit time perivascular flow probe around the main pulmonary artery served as the standard reference measurement (COUFP). During the experiment, animals were resuscitated from hemodynamic shock using fluid boluses. Cardiac output measurements were performed throughout the experiment. Measurements and Main Results: The correlation coefficient between COTPTD and COUFP was .97 (95% confidence interval .94–.98, p < .0001). Bland-Altman analysis showed a mean bias of 0.19 L/min with limits of agreement of −0.04 and 0.43 L/min (12.0% and ±14.7%, respectively). The correlation coefficient between changes in COTPTD and COUFP during volume loading was .95 (95% confidence interval .91–.96, p < .0001). There was a significant correlation between changes in global end-diastolic volume and changes in stroke volume (r = .59) but not between changes in central venous pressure and changes in stroke volume (r = .03). Conclusions: The transpulmonary thermodilution technique is a reliable method of measuring cardiac output in newborn animals. It is also capable of tracking changes in cardiac output.

Spectral analysis of heart rate variability in spontaneously breathing very preterm infants.

Van Ravenswaaij‐Arts C, Hopman J, Kollée L, Sloelinga G, Van Geijn H. Spectral analysis of heart rate variability in spontaneously breathing very preterm infants. Acta Pædiatr 1994;83:473–80. Stockholm. ISSN 0803–5253

The effect of blood transfusion and haemodilution on cerebral oxygenation and haemodynamics in newborn infants investigated by near infrared spectrophotometry

Abstract The objective of this study was to investigate the influence of blood transfusion and haemodilution on cerebral oxygenation and haemodynamics in relation to changes in cerebral blood flow velocity (CBFV) and other relevant physiological variables in newborn infants. Thirteen preterm infants with anaemia (haematocrit < 0.33) and ten infants with polycythaemia (haematocrit > 0.65) were studied during blood transfusion and haemodilution respectively using adult red blood cells and partial plasma exchange transfusion. Changes in cerebral concentrations of oxyhaemoglobin (cO2Hb), deoxyhaemoglobin (cHHb), total haemoglobin (ctHb), (oxidized - reduced) cytochrome aa3 (cCyt.- aa3) were continuously measured using near infrared spectrophotometry throughout the whole procedure. Simultaneously, changes of mean CBFV in the internal carotid artery were continuously measured using pulsed Doppler ultrasound. Heart rate, transcutaneous partial pressure of oxygen and carbon dioxide, and arterial O2 saturation were continuously and simultaneously measured. Blood transfusion resulted in increase of cO2Hb, cHHb, ctHb and red cell transport (product of CBFV and haematocrit), whereas CBFV decreased. The increase of cO2Hb exceeded that of cHHb, reflecting improvement of cerebral O2 supply. Haemodilution resulted in a decrease of cO2Hb, cHHb and ctHb, whereas CBFV increased. Red cell transport was unchanged. The decrease of cO2Hb exceeded that of cHHb, reflecting decreased cerebral O2 supply. cCyt.aa3 decreased after blood transfusion and remained unchanged after haemodilution, but the reliability of these results is uncertain. With the exception of a small, but significant increase in transcutaneous partial pressure of oxygen after blood transfusion, the other variables showed no changes. Each blood withdrawal during exchange transfusion resulted in only a significant increase in heart rate without changes in the other variables measured, suggesting unchanged cerebral perfusion. Conclusion In newborn infants blood transfusion in anaemia results in improvement of cerebral oxygenation, but haemodilution in polycythaemia does not improve cerebral oxygenation despite possible improvement of cerebral perfusion.

Effects of repeated indomethacin administration on cerebral oxygenation and haemodynamics in preterm infants: Combined near infrared spectrophotometry and Doppler ultrasound study

Changes in CBV (ACBV), expressed in ml/100 g, were calculated from the formula ACBV = 4*Ac tHb /O.69*cHb , where crib = arterial haemoglobin concentrat ion in mmol/1, 0.69 = cerebral-arterial haematocri t ratio and 4 = correction factor, since ctHb is calculated from changes in light absorption using extinction coefficients based on the tetrahaeme molecule, while cr ib determination in blood samples is based on the monohaeme molecule. The corrected ACBV values in Table 2 and Fig. 2 are shown below. This correction does not inf luence the (statistical) interpretation, the essential statements in the discussion and the final conclusion of the study. Only paragraph 4 of the discussion must be replaced by the fol lowing comment:

Doppler flow characteristics in the main pulmonary artery and the\(\overline {LA} /Ao\) ratio before and after ductal closure in healthy newborns/Ao ratio before and after ductal closure in healthy newborns

SummaryEcho-Doppler (ED) techniques were used to estimate the time of closure of the ductus arteriosus in 30 normal neonates. We found that after birth there was a left-to-right (L-R) shunt through the ductus, which disappeared within 14 hours in 50% of the neonates investigated. Furthermore, patency of the ductus was not associated with a murmur. After closure of the ductus there was a significant diminution of the echocardiographically determined left atrium/aortic (

Cardiac output measurement using an ultrasound dilution method: a validation study in ventilated piglets.

\overline {LA} /Ao