The baroreflex control of circulation is always operating and modulates blood pressure and heart rate oscillations. Thus, the study of cardiovascular variability in humans is performed in a closed-loop model and the physiology of post-sinoaortic denervation is completely unknown in humans. We dissected for the first time the different components of systolic arterial pressure (SAP) and RR-interval spectra in a patient with ‘baroreflex failure’ (due to mixed cranial nerve neuroma) who represents a human model to investigate the cardiovascular regulation in an open-loop condition. Interactions among cardiovascular variability signals and respiratory influences were described using the multivariate parametric ARXAR model with the following findings: (1) rhythms unrelated to respiration were detected only at frequencies lower than classical low frequency (LF; Slow-LF, around 0.02 Hz) both in SAP an RR spectra, (2) small high-frequency (HF) modulation is present and related with respiration at rest and in tilt (but for SAP only) and (3) the Slow-LF fluctuations detected both in SAP and RR oscillate independently as the multivariate model shows no relationships between SAP and RR, and these oscillations are not phase related. Thus, we showed that in a patient with impaired baroreflex arc integrity the Slow-LF rhythms for RR have a central origin that dictates fluctuations on RR at the same rhythm but unrelated to the oscillation of SAP (which may be related with both peripheral activity and central rhythms). The synchronization in LF band is a hallmark of integrity of baroreflex arc whose impairment unmasks lower frequency rhythms in SAP and RR whose fluctuations oscillate independently.
‘Baroreflex failure’ is a relatively rare condition. Different possible manifestations are described, the most common pattern being hypertensive crisis, lasting minutes to hours, with profound general malaise, often accompanied by tachycardia.1, 2, 3 Another presentation is volatile hypertension with baseline blood pressure normal to elevated between the hypertensive spells. A rare presentation is due to an increased parasympathetic tone and presents with severe bradycardia and hypotension, sometimes leading to a syncope.2, 4 The peculiar physiopathology of this syndrome is due to different degree of involvement of the baroreflex arc and in its most common presentation, the underlying lesion is due to the effect of deafferentation of the glossopharyngeal nerve and vagus, which convey information from sinoaortic baroreceptors.2 The physiology of post-sinoaortic denervation is known only in part in animals and completely unknown in humans.5, 6 We studied the different components of systolic arterial pressure (SAP) and RR-interval spectra in a patient with baroreflex failure who represents a human model to investigate the cardiovascular regulation in an open-loop condition, both at resting and during stimulation by tilting.
Materials and methods
The study of the various components of SAP and RR-interval oscillations was conducted at rest and during tilting in a 67-year-old woman with known baroreflex failure. The clinical aspects of the case have been already described.7 Briefly, initially her chief symptoms were hypertension crisis with mean blood pressure peaks exceeding 230/120 mm Hg with the highest documented peak of 260/140 mm Hg and 90 beats per min. She also complained of several symptoms associated with the hypertensive spells, such as fatigue, malaise, irritability, profuse sweating, tremulousness, dyspnoea and abdominal pain, all of them lasting about 1 h and with a spontaneous recovery. Secondary forms of hypertension were excluded. Standard blood pressure was repeatedly found between the normal range and a 24-h ambulatory blood pressure monitoring (Spacelabs 90207-30 with blood pressures recorded every 15 min; Spacelabs. Medical Inc., Redmond, WA, USA) was performed and showed mean 24 h blood pressure of 125/68 mm Hg. An echocardiogram showed normal parietal thickness and function. The clinical and laboratory examinations excluded the presence of chronic diseases, including obesity, diabetes and heart diseases. The patient reported also swallowing impairment, mild dysphonia, cough and disruption in talking. During a few months after the first visit, the patient experienced numerous episodes of hypertensive crisis and repeatedly was investigated for secondary forms of hypertension, particularly focusing on a suspected diagnosis of pheochromocytoma, without positive findings.
Because the clinical pattern of hypertensive spells and a single hypotensive episode reported by the patient suggested a disorder of the regions related with the vasomotor modulation, she was submitted to a basal and gadolinium magnetic resonance imaging of the brain that revealed an extra-assial mass, localized in the inferior portions of the cisterna of the right ponto-cerebellar angulus. Thus, the patient was submitted to neurosurgery and the diagnosis of mixed cranial nerve neuroma (Schwannoma) involving the glossopharyngeal nerve was determined. On the basis of both the history and the finding of anatomic involvement of the baroreflex arc, we made a presumptive diagnosis of baroreflex failure that was confirmed by the pharmacological study of baroreflex function.8, 9
Acquisition of electrocardiographic, blood pressure and respiratory signals
After the confirmed diagnosis of baroreflex failure, the patient was submitted to a continuous electrocardiographic recording by means of a polygraph (Cardioline WS2000, Remco Italia, Mi, Italy) connected with a microcomputer and blood pressure was obtained noninvasively by a plethysmographic method (Finapres, Ohmeda, Japan), as previously described.10
The respiratory frequency was assessed by a nasal thermistor through a commercially available system (Healthdyne Tech 6260, Healthdyne Technology Inc., Dublin, Ireland). The patient was studied during free breathing, without any controlled respiration.
We recorded 10 min of resting followed by a tilting test.11
Processing of physiological signals
Extraction of variability series
As previously described, QRS detection and RR-interval and respiration signal measurement were automatically performed by the Cardioline WS2000 equipment.10 This algorithm looked for the R wave peak as a reference point. Afterwards each QRS complex was interpolated by a parabolic curve. The R point was chosen to correspond with the maximum of the interpolating parabola to improve the accuracy of detection of the peak R wave.12 The systolic blood pressure value was automatically identified as the maximum of the parabolic curve fitting the pressure tracing. Finally, respiration amplitude was measured in correspondence of R peak on the electrocardiogram. The use of an interactive graphic interface allowed the operator to visually identify and correct premature beats, missed beats and artefacts on RR series. Corrections made on RR intervals determined automatic corrections in the SAP and respiratory series. In this way, a series of successive RR intervals (RR tachogram, t), a series of corresponding successive SAP values (systogram, s) and a series of respiration amplitudes (respirogram, r) were obtained.
Processing of variability series
Interactions among cardiovascular variability signals and respiratory influences were described using the multivariate parametric ARXAR model of Figure 1. The following relationships are considered: (1) t and s affect each other in a closed loop; this describes the mechanical effect of RR on SAP and the feedback ascribed to baroreceptive mechanisms; (2) a regression of s on its own past is considered; it lumps together all feedbacks regulating SAP through mechanisms such as arterial and venous compliances, arterial resistances, contractility and sympatho-sympathetic reflexes; (3) respiration is seen as an exogenous input and can affect separately t (cardiopulmonary reflexes or central drive) and s (modulation of venous return, thoracic pressure effects on afterload and baroreceptors, etc). Finally, additional source of variability (residuals, ut and us) acting on t and s separately are included through all-poles model Mt and Ms.13 In a time-domain description, both the t and s signals are described as the output of prediction error models. The forecast of t is obtained through a regression over past samples of s and r; the forecast of s is based on t, r and s itself as formalized by Baselli et al.14
In details, for s prediction:
where p is the model order; hss(1)…hss(p) the parameters of s autoregression; hst(1) the regression parameter over the previous RR; hsr(0)…hsr(p) the parameters relevant to the past respiration samples and i the temporal index.
For t prediction:
where hts(1)…hts(p) are the parameters of t regression over SAP and htr(0)…htr(p) the parameters relevant to the past respiration samples.
The model (regression) parameters are identified by an iterative algorithm according to classical generalized least-square method used for identification of dynamic adjustment models.15 Optimal model order was determined by Akaike information criterion. The advantage of using this model is twofold: in addition to classical spectral parameters (frequency and powers values in low frequency (LF) and high frequency (HF) bands), the application of a multivariate spectral decomposition method allows to parcel the spectrum of each signal into partial spectra relevant to different inputs (for example the r spectrum can be divided into contribute due to s, r of Mt). Different oscillating mechanisms, even if manifesting at similar frequencies, are separated by model identification and model poles classification. Moreover, after parameter identification, the impulse response of model blocks is known and the model can be used to filter the series thus obtaining, for each signal, the time course of its components.14 An example obtained in a 56-year-old, nonsmoking, lean healthy woman is shown in Figure 2 (SAP variability) and Figure 3 (RR variability). The α value calculated by the model in this normal case is 10.41 ms/mm Hg. Finally, data of time-domain analysis (mean, standard deviation (s.d.) and root-mean-square successive difference (RMSSD)) are dissected for each time-course signal components.
The authors had full access to the data and take responsibility for its integrity. All authors have read and agreed to the paper as written.
As expected in a case of impaired baroreflex control, the ‘closed-loop’ α calculated by the model showed a very low value, both in resting and in tilting conditions (0.51 and 0.02 ms/mm Hg, respectively) in agreement with our working hypothesis.
Figure 4 shows the SAP time-domain variability (left panel) and its corresponding spectra (right panel). We observed a small component in HF, linked with respiration through its effects on the tachogram (Figure 4, panel c). A high coherence is observed for this component (Table 1). In addition, a Slow-LF oscillation (peak in 0.022 Hz; Table 1) related with inputs different from SAP, RR and respiration (Figure 4, panel e) was measured.
When the RR variability was dissected into the separated parts (Figure 5), a similar respiratory-linked HF component was observed (Figure 5, panel c; high coherence in 0.27 Hz, see Table 1). A Slow-LF peak is also observed and its origin is mainly related to the Mt block (ut residuals, panel d), with no contribution from SAP variability (Figure 5, panel c). Accordingly, although this Slow-LF peak is present with oscillations at the same frequency of SAP, no constant phase relationships were detected between these rhythms (very low coherence between SAP and RR; see Table 1, k2=0.45).
After 7 min of tilting, the patient experienced malaise accompanied by hypotension and no substantial change in RR interval (Figure 6); thus, the tilting was interrupted and a slow recovery of symptoms was obtained. During tilting, the SAP spectra were similar to those observed at resting, with the HF component shifted to higher frequency, as expected (Figure 7a). A clear Slow-LF component is still present in the SAP spectrum. Coherence is relatively high for respiration oscillations only (Table 1).
As regards RR, the HF component disappeared as expected during tilting (vagal withdrawal; Figure 8a), whereas the Slow-LF component remained conspicuous but still without contribution by SAP variability (Figure 8b) and still unrelated to SAP (null coherence between SAP and RR, k2=0.21; Table 1).
The main findings of our study, the first observation in humans in an open-loop condition, state that (1) rhythms unrelated to respiration were detected only at frequencies lower than classical LF (Slow-LF, around 0.02 Hz) both in SAP and in RR spectra; (2) small HF modulation is present and related with respiration at rest and in tilt (but for SAP only) and (3) the Slow-LF fluctuations detected both in SAP and in RR oscillate independently as the multivariate model shows no relationships between SAP and RR, and these oscillations are not phase related.
In case of baroreflex failure, the loss of buffering ability is usually secondary to iatrogenic causes such as carotid body tumour resection, neck irradiation, surgical section of glossopharyngeal nerve, carotid bypass surgery and unilateral or bilateral carotid endarterectomy.16, 17, 18, 19, 20, 21, 22 Moreover, rare reports describe this syndrome as due to degenerative neurological disorders or genetic diseases.23, 24, 25, 26, 27 In our patient, the impairment in baroreceptor reflex was due to a posterior fossa tumour, as already described.7
Animal studies reported that arterial baroreceptor denervation induces selective changes in the spectrum of RR and SAP variability, suggesting that the baroreceptor reflex exerts its major buffering effect on the very low-frequency (VLF) fluctuations.28 However, interspecies major differences were reported.28 Our results obtained in a patient with baroreflex failure show in a human model of open-loop condition that the integrity of the baroreflex arc is required to maintain fluctuation in LF band at frequencies higher than 0.03 Hz. Reduction of faster LF oscillations has been observed in several animal studies after sinoaortic denervation.5, 29, 30 Most of these studies evidenced that the spectrum of variability in a condition of post-sinoaortic denervation was concentrated in the VLF.5 In our study, the number of stable cycle guarantees the appropriateness of acquisition length, especially considering that spectral analysis was performed by an autoregressive-based method whose spectral resolution does not depend on the series length.
Moreover, although Slow-LF fluctuations were observed both in SAP and in RR spectra, when the components of spectra were dissected by using the multivariate parametric model, no effect of SAP variability was observed on the tachogram whereas RR oscillations mildly affected SAP fluctuations. Moreover, null coherence was observed between the pressure and RR series in the Slow-LF band. In line with our results, a recent report in mice submitted to acute sinoaortic denervation showed high coherence between SAP and RR variabilities for the HF component whereas in LF/VLF band no coherence was observed.6
In animals, the increased blood pressure variability associated with sinoaortic denervation was reduced by ganglionic block.31 VLF in blood pressure was related to increased plasma epinephrine levels in sympathectomized rats and catecholamine infusion generated VLF fluctuations in blood pressure, suggesting an adrenergic origin of VLF.32 Possibly, the unrestrained central rhythms observed in our case in presence of damage of the baroreflex arc may be linked to the central sympathetic outflow. This interpretation of our data is in agreement with the anatomical finding of destruction of sympathetic inhibitory centers (nucleus tractus solitarii) with sparing of centers that exert a positive modulation on the sympathetic tone described in a patient with baroreflex failure due to central nervous system lesions.27
Concerning the slow SAP waves, it should be emphasized that our model does not possess enough information to disentangle spontaneous vasomotor activity from centrally driven one, leaving open both hypotheses and also a mixed one involving a synchronization of peripheral activity by central rhythms.33 The hypothesis of central rhythms in SAP and RR oscillations has been supported by animals and human studies, and the presence of both supraspinal oscillators and spinal structures has been suggested in modulating the sympathetic outflow.34, 35, 36 Because in our study the prominent component of variability observed in the SAP and RR spectra are independent, we showed that in a patient with impaired baroreflex arc integrity the Slow-LF rhythms for RR have a central origin that dictates fluctuations on RR at the same rhythm but unrelated to the oscillation of SAP.
It has to be acknowledged that, due to the rarity of this disease, we report the results obtained in an individual patient and therefore our results may not apply to other forms of baroreflex failure. Moreover, the patient described here did not show potential confounding diseases acting on baroreflex function such as metabolic or cardiac diseases and, although referring for hypertensive spells, resting blood pressure was within the normal range. As regards the potential effects of age on baroreflex sensitivity, significantly higher values of baroreflex response were reported in subjects older than our patient.37 Therefore, with the above-mentioned limitations, our study sheds a new light on the cardiovascular physiology, showing in a human model of open-loop condition that the rhythms unrelated to respiration in the classical LF band of SAP and RR variabilities depend on the baroreflex control and that synchronization in this band is a hallmark of integrity of baroreflex arc whose impairment unmasks lower frequency rhythms in SAP and RR whose fluctuations oscillate independently.
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The authors declare no conflict of interest.
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Cite this article
Guasti, L., Mainardi, L., Baselli, G. et al. Components of arterial systolic pressure and RR-interval oscillation spectra in a case of baroreflex failure, a human open-loop model of vascular control. J Hum Hypertens 24, 417–426 (2010). https://doi.org/10.1038/jhh.2009.79
- heart rate variability
- blood pressure spectral analysis
- baroreflex control
- baroreflex failure
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