PRO: Monitoring heart rate variability adds a significant incremental value in cardiovascular surgery
Edmundo Pereira de Souza Neto, MD, with Patrice Abry, MD, Jean Frutoso, MD, Patrick Flandrin, MD, Claude Gharib, MD
Hôpital Cardio-Vasculaire et Pneumologique Louis Pradel, Université Claude Bernard Lyon I, & Ecole Normale Supérieure de Lyon, France
Analyzing rhythmic variations of the heart rate can help to better understand the physiopathological mechanisms involved in cardiovascular diseases 1-3. We review that heart rate variability (HRV) can be relevantly described via a frequency band decomposition and discuss various clinical applications of spectral analysis.
Data that oscillate regularly naturally lead to a description in terms of characteristic frequencies and to spectral analysis (Figure I). Human HRV frequency spectrum (that mainly ranges from 0 to 0.4 Hz) is traditionally described via a 4-component frequency band decomposition: (1) Ultra low frequencies (ULF; 0.0001 to 0.003 Hz): this frequency band is not only a research tool but is also used in clinical evaluations, as in hypertension, and has a predictive value of mortality in ischemic cardiopathy 4-6. (2) Very low frequencies (VLF; 0.003 to 0.04 Hz) describe long-term regulation mechanisms of heart rate and blood pressure like thermoregulation, vasomotricity, renin-angiotensin system or other factors 7-9. (3) Low frequencies (LF; 0.04 to 0.15 Hz) are related to the activity of the baroreflex system 10. However, their physiological interpretation remains to be further elucidated 11-14. (4) High frequencies (HF; 0.15 to 0.4 Hz) are related to the parasympathetic activity 15, 16. The exact definition of the HF band should actually be tied to the respiration rate (Figures I and II). However, HF cannot reflect only the parasympathetic modulation, since the sympathetic modulation of the heart rate variations related to breathing can also be present (when the respiratory activity is lower than 9 breathings/minute) 17-19.
To quantify the spectral characteristics of each of these bands, various spectral indices are usually defined. One first defines the (total) power of HRV as the surface under the spectrum for the range 0 to 0.4 Hz, as well as powers for each band by limiting the summation to frequencies defining the bands. From Parseval theorem, the total power corresponds to that of the HRV time fluctuations and can hence be compared to the temporal index SDNN (standard deviation of normal-to-normal intervals) 4 (Table 1). Normalized LF and HF powers are also defined as the ratio of the power in the considered frequency band to the sum of the HF and LF powers: normalised LF = 100*LF/ (HF+LF); normalised HF = 100*HF/ (HF+LF). Normalization indices are used to minimize the impact of a change of the total power in the analysis of the LF and HF balance 4. Also, the LF/HF index is used to quantify the sympathetic and parasympathetic system balance at the cardiac level 20-23.
The spectral analysis of HRV has proved useful in many applications. Upon induction of anesthesia, LF and HF powers are attenuated, while the LF/HF ratio increases at the time of intubation, indicating a shift in the sympathovagal balance to the sympathetic side 24-29. It seems that beyond changes in heart rate detectible on routine monitoring, there also exist more subtle fluctuations of this parameter. This is why spectral analysis is of interest for a quantifiable early evaluation of the cardiovascular system 8, 30. Moreover, these fluctuations in HRV can differ depending on the chosen anaesthetic technique 31-35. Propofol and thiopental decrease the total HRV, but do not deteriorate the sympathovagal balance 35. Etomidate appears to preserve the autonomous nervous system and the baroreflex 36. Ketamine induces a deterioration of the sympathovagal relation: it increases LF and reduces HF powers 37. Midazolam, diazepam and flunitrazepam decrease the LF/HF ratio and reduce short-term variability 34,38. Sufentanil decreases the total activity of the autonomous nervous system; a decrease of all the components of heart rate variability occurs 36. The inhaled anesthetic agents reduce all the components of the heart rate spectrum and hence the activity of the autonomous the nervous system 30-32, 39-43.
A growing body of literature integrates the concept of HRV in monitoring the depth of anaesthesia 44, 45. However, the sensitivity and the specificity of this technique (70 and 60 %, respectively) are lower than those of methods like the bispectral index (97 and 95% respectively), or other methods derived from the electroencephalogram 44. Sympathovagal balance seems to be better preserved after spinal anesthesia than after general anaesthesia 46. Spectral analysis shows the reduction in the sympathetic activity, reduction in the LF power after sympathetic cardiac blocking 47.
In addition to anesthetic agents, cardioactive medications affect HRV. Although alpha-adrenergic stimulation of healthy volunteers does not modify HRV 48, 49, beta-adrenergic stimulation causes a reduction in HRV 48, 49. For patient under anesthesia, the administration of the sympathomimetic does not involve major modifications of the spectral components 50. Beta-blockers increase cardiac variability and vagal activity 51, 52. However, a fall of the HF peak of the heart rate in the prone position is shown after a beta-blocker treatment 48.
Lastly, pain and comorbidities influence HRV. For new born children, during noxious stimulation (capillary puncture with the finger), one observes an increase in sympathetic and a reduction in parasympathetic activities 53. Moreover, these responses are less prominent for the 8 month-old infant than for the 4 month-old one but are more prolonged. This indicates a progressive maturation of the cardiac autonomic response to the nociception 53, 54. After myocardial infarction, the rate of survival is higher for patients with a SDNN > 100ms than for patients with a SDNN < 100 ms 5, 55, 56. For pNN50, patients with a value smaller than 2 % show a lower death rate 56. The study of HRV was also used to understand the incidence of the arrhythmias 57. In cardiac insufficiency secondary to a dilated cardiopathy, HRV analysis could be used as a prognostic factor of survival 58, 59. In cardiac insufficient patients followed over approximately 50 months, SDNN can be used as a forecasting factor, with 80 % of survival in 50 months for patients with a SDNN > 100 ms against only 40 % for those with SDNN < 100 ms 58. In patients with chronic renal insufficiency, one observes a reduction in the LF and HF indices, which suggests a diffuse deterioration of the autonomic nervous system 60, 61. An improvement of these indices was found after 6 months of renal transplantation 62, 63. During septic shock, there is a very early deterioration of HRV with a faster sympathetic activity recovery amongst survivors 50, 64. Patients who die have a progressive reduction in the LF component of the heart rate 65, 66. This reduction multiplies by 13 the risk to die during anesthesia; a LF/HF ratio smaller 1.5 multiplies by 7 the risk of death during anesthesia 65, 66. In addition, studies of hypertensive subjects showed an increase in LF powers compared to those of the normotensive subjects 11, 67-69. Lastly, immediately after heart transplantation, there is a disappearance of the HF and LF peaks in heart rate spectrum, related to the cardiac denervation 70. These peaks reappear with the reinnervation 4, 71. The clinical significance of this reinnervation is not currently well established but both the simplicity and the harmlessness of spectral analyses allow the monitoring of these patients for future studies.
The spectral analyses reported above are all based on three implicit assumptions: HRV is a stationary process, produced by a linear system, and its spectral analysis (i.e., a second-order statistical analysis) fully exhausts its description. Recent studies have called into questions the robustness of such assumptions and proposed alternative analyses, such as chaos, empirical mode decomposition and fractal. Here, we only discuss fractal. Recent studies showed that HRV contains not only harmonic oscillations but also non-harmonic ones which appear in the spectrum not as energy peaks but as spread energy that covers a broad range of frequencies 72-75 and whose general profile correspond to 1/f components 74-76. It has been reported that, during anesthesia, fractal indices undergo a significant decrease compared to those observed under normal circumstances 77. This is explained by a reduction in the neurovegetative reactivity due to anaesthesia and sought in order to reduce the nociception during surgery 77.
Because biological mechanisms driving heart beat regulation involves various frequency bands, it is natural to perform spectral analysis of HRV. Clinicians aim at using it to obtain markers for certain pathologies. Indeed, estimated spectral attributes can serve as basis for the design of practical indicators for the diagnosis, the forecast and the treatment of certain diseases. However, many other natural rhythms, such as the respiration rhythm, may interact with HRV and cause various types of non-stationarities, yielding the need for spectral analyses that specifically accounts for them.
Figure I: Recording of the RR intervals (RRI) in a healthy volunteer, in a prone position, with an uncontrolled breathing (Figure A), and its spectral calculation (Figure B). It is seen that the RRIs combine full and slow oscillations (low frequencies) and faster variations of low amplitude (high frequencies). The recording was carried out from 5 to 6 p.m. using a Finapres ® 2300 system and an oscilloscope Philips ® (personal data). PSD = power spectral density.
Figure II: RR Intervals (RRI) in a healthy adult men during three periods (figure A): prone position (about 10 min) followed by sitting position (about 10 min) and finally standing position (about 10 min). These postural changes provoke instantaneous changes in heart rate mainly resulting from autonomic modifications. The spectral analysis of RRI (figure B) shows two main frequency bands: a low frequency one (LF, from 0.04 to 0.15 Hz) and a high frequency one (HF, from 0.15 to 0.4 Hz). The issue of nonstationarity, which conveys the key information about the evolution of the system, can be better addressed by resorting to a time-frequency technique, as the one used in figure C. The recording was carried out from 5 to 6 p.m. using a Finapres ® 2300 system and an oscilloscope Philips ® (personal data).
Table 1: Heart rate measurements in the temporal and frequential domain.
Frequence domain: TP = total power; ULF = ultra low frequency; VLF = very low frequency; LF = low frequency; HF = high frequency; LTV = long term variability; LWTV = low term variability.
Time domain: In a continuous ECG record, each QRS complex is detected, and the so-called normal-to-normal (NN) intervals, that is, all intervals between adjacent QRS complexes are measured.
SDNN = standard deviation of the NN intervals; SDANN = standard deviation of the averages of NN intervals in all 5-minute segments of the entire recording; SDNN index = mean of the standard deviations of all NN intervals for all 5-minute segments of the entire recording; RMSSD = the square root of the mean of the sum of the squares of differences between adjacent NN intervals; pNN50 = NN50 count* divided by the total number of all NN intervals.
*NN50 count = number of pairs of adjacent NN intervals differing by more than 50 ms in the entire recording; three variants are possible counting all such NN interval pairs or only pairs in which the first or the second interval is longer.
# = correspondence only for heart rate (RR Intervals) variability.
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