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From the *Department of Anesthesiology, Aichi Medical University School of Medicine, Japan; and Department of Anesthesiology, Nagoya University Graduate School of Medicine, Japan.
Address correspondence and reprint requests to Yoshihiro Fujiwara, MD, PhD, Department of Anesthesiology, Aichi Medical University School of Medicine, 21 Karimata Yazako Nagakute Aichi 480-1195, Japan. Address e-mail to yyoshiff{at}aichi-med-u.ac.jp.
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Abstract |
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METHODS: UsEn was estimated by a linearized version of the nonlinear least squares method combined with the maximum entropy spectral analysis method (MemCalc method). Preoperative UsEn of 46 patients (ASA PS 1 or 2, aged 40–60 yr) without a history of hypertension was evaluated using the MemCalc method. Patients were assigned to two groups according to preoperative UsEn (Group LOW; UsEn <45, Group HIGH; UsEn 
45). Anesthesia was induced with propofol, fentanyl and vecuronium bromide and endotracheal intubation was performed. Hemodynamic fluctuations during the induction of anesthesia were recorded and compared between the two groups.
RESULTS: It was found that arterial blood pressure fluctuations during the induction of anesthesia were significantly greater in patients with a low UsEn.
CONCLUSION: UsEn could predict arterial blood pressure fluctuations during the induction of anesthesia.
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Introduction |
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Recently, some investigators found that heart rate variability (HRV) can predict the incidence of hypotension caused by the induction of general (10) or spinal anesthesia (11,12). It may be of great benefit if we could predict the magnitude of the hemodynamic fluctuation related to the induction of anesthesia.
The MemCalc method, which is a combination of the maximum entropy method for a spectral analysis and the nonlinear least squares method for fitting analysis (13) enables us to reliably estimate HRV from a series of RR intervals over 30 s, and provides us with information about the entropy of the RR interval. Although there are numerous entropy formulations, entropy is a concept that addresses system randomness and predictability, with greater entropy often associated with more randomness and less system order (14). When entropy is calculated with the MemCalc method, it is normalized from 0, which represents no randomness of the heart rate (HR), to 100, representing complete randomness, much like Gaussian white noise. Recently, reports investigating autonomic function using this new technique have been introduced into the field of anesthesiology (15,16). Here, we name this entropy of the RR interval "ultra short-term entropy" (UsEn). It can be estimated by a short series of RR intervals.
The objective of this study was to determine whether the preoperative UsEn could predict cardiovascular responses to the induction of general anesthesia.
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METHODS |
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Anesthesia and Hemodynamic Measurement
All patients were allowed to consume clear fluids for up to 2 h before entering the operating theater. Without any premedication, patients entered the operating theater and lay on an operating table. Next, the measurements of oscillometric noninvasive BP, pulse oximetry and electrocardiography were initiated. After 10 min of rest on an operating table, baseline values of systolic BP (SBP) were recorded as SBPbaseline; meanwhile, the estimations of HRV began.
Anesthesia was induced with a target-controlled infusion of 3 µg/mL blood concentration of propofol (Diprifuser TE-371; Terumo, Tokyo, Japan), 1.5 µg/kg of fentanyl and 0.15 mg/kg of vecuronium bromide. Three minutes later endotracheal intubation was performed and the lungs were mechanically ventilated with 50% oxygen in air. Immediately after anesthesia was induced, the target concentration of propofol was decreased to 2 µg/mL for the maintenance of anesthesia. Eight milliliters per kilogram of tidal volume and 12/min of respiratory rate were maintained during positive pressure ventilation. Consecutive noninvasive BP measurement using the STAT mode was started just before the induction of anesthesia and was continued until 5 min after endotracheal intubation. Hypotension was defined as a SBP less than 80 mm Hg for longer than 60 s and was treated with 5 mg ephedrine. Bradycardia was defined a HR <45 for longer than 60 s and was treated with 0.5 mg atropine. As indices of hemodynamic fluctuation during the induction of anesthesia, the specific SBP values and BP differences were defined as follows.
SBPbaseline-postiduction = SBPbaseline – SBPpostinduction SBPpostintubation-postinduction = SBPpostintubation – SBPpostinduction SBPpostintubation-preincision = SBPpostintubation – SBPpreincision SBP = (a) + (b) + (c)
HRV and Entropy Measurement
An electrocardiogram signal was obtained from a conventional anesthesia monitor (Hewlett Packard, Model 66S), digitized at 1000 Hz and transferred to a personal computer (Epson NT2700, Japan). After the RR intervals were determined, on-line analysis of the HRV was made by the MemCalc method (Tarawa, Suwa Trust, Japan). The averaged HR, the power of low (LF; 0.04–0.15 Hz) and high (HF; 0.15–0.4 Hz) frequency components of HRV and the LF/HF ratio before the induction of anesthesia were then calculated. UsEn values were obtained from eight RR intervals.
Statistical Analysis
Data were analyzed using standard software (JMP version5, SAS Institute, Cary, NC).
2 test, the unpaired t-test and the Wilcoxon’s signed rank test were used to compare the LOW group with the HIGH group when appropriate.
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RESULTS |
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The specific SBP values measured during the induction of anesthesia are depicted in Figure 1. There were no significant differences in the specific SBP between the two groups. SBPs, which are the differences in specific SBP caused by the induction of anesthesia, are shown in Table 2. SBPpostintubation-preincision and total SBP were significantly higher in the LOW group. Moreover, significantly more patients in the LOW group needed ephedrine treatment for hypotension.
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DISCUSSION |
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Knüttgen et al. (10) confirmed a significant relationship between HRV preoperatively measured at rest and BP stability during anesthesia induction in diabetics. They reported that BP after endotracheal intubation was significantly higher in patients with normal cardiovascular values, whereas UsEn did not predict SBPpostintubation in our study. The difference in results may be explained by the fact that they focused on patients with diabetes, in whom the prevalence of autonomic dysfunction, ischemic heart disease and congestive heart failure may have been more frequent than in our patients.
Although Knüttgen et al. used a conventional method to estimate HRV (i.e., time domain or frequency domain analysis), we used a newly developed nonlinear method of determining HRV, the MemCalc method. Sawada et al. (13) validated this method by comparing it with autoregressive modeling. Furthermore, this method has a great advantage in clinical situations. During anesthesia and surgery, many factors, such as surgical stimuli and electromagnetic noise from electrocautery, could hinder the accurate estimation of HRV. In contrast to conventional methods such as autoregressive modeling and fast Fourier transform, which require RR intervals longer than 5 min, the MemCalc method enables us to estimate HRV from RR intervals as short as 30 s. In terms of UsEn, only four to eight RR intervals are required.
While frequency-domain analyses of HRV focus on the status of cardiac autonomic activity, nonlinear indices, including UsEn, have been proposed to provide insight into the overall structure of the HR-regulating system. When applied to signals such as RR intervals, the decrease in entropy represents the decreased activity of the HR regulatory systems, namely, more compromised physiology (14). In fact, associations between decreased entropy of the RR interval and pathology have been demonstrated (18).
The induction of and emergence from anesthesia are periods of great hemodynamic variability in hypertensive patients, and periods of greater intraoperative decreases in BP were noted in patients with persistent hypertension (19). Moreover, SBP at rest increases in older patients. This increase is even greater with exercise, and older patients also show a more hypotensive response to general anesthetics. Burgos et al. (20) demonstrated an increased incidence of hypotension after anesthesia induction in patients with diabetes with autonomic neuropathy. Previous reports have also demonstrated reduced HRV in the elderly (21), hypertensive patients (22) and diabetes patients (23). These factors may partly explain the association between decreased HRV and BP fluctuation. However, the findings of this study are independent of these factors, as no differences inpatient age, prevalence of hypertension or diabetes were observed.
Although we carefully tried to match patient characteristics between the two groups, the baseline HR and LF/HF were significantly higher in the LOW group. UsEn may not be completely independent of LF/HF or HR. It is generally agreed that the increase in LF/HF or HR is associated with cardiac sympathetic dominance. Preoperative stress and anxiety have resulted in preoperative hypertension and cardiac sympathetic activation (24), and sudden sympathetic withdrawal caused by the induction of anesthesia may thus exacerbate hemodynamic fluctuation. This suggests that preoperative sympathetic activation caused by anxiety could therefore explain the association between UsEn and hemodynamic fluctuation.
Interestingly, Neumann et al. (25) recently reported that polymorphic variation in the choline transporter gene is associated with the LF/HF ratio. Molecular variation in genes that regulate components of autonomic function may account for the association between UsEn and hemodynamic fluctuation during the induction of anesthesia. Further study will thus be needed in order to elucidate the mechanism underlying the findings of this study.
In conclusion, preoperative UsEn was found to independently predict BP fluctuation during the induction of anesthesia with propofol, fentanyl and vecuronium. UsEn also predicted the occurrence of hypotension during the induction of anesthesia.
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Footnotes |
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This work is attributed to Department of Anesthesiology, Aichi Medical University School of Medicine.
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