Short-term correlation properties of R-R interval dynamics at different anesthesia methods

• Main Authors: Jheng-Yan Lan, 藍正妍
• Other Authors: Shou-Zen Fan
• Format: Others
• Language: zh-TW
• Published: 2010
• Online Access: http://ndltd.ncl.edu.tw/handle/57019289935835364744

碩士 === 臺灣大學 === 臨床醫學研究所 === 98 === Key words: Heart rate variability, spinal anesthesia, general anesthesia, detrended fluctuation analysis, sample entropy, linear analysis Background Time and frequency domain analyses of heart rate variability (HRV) are the most commonly used noninvasive methods to evaluate autonomic regulation of heart rate in healthy subjects as well as in patients with cardiovascular disorders. Because nonlinear phenomena are involved in the genesis of human heart rate fluctuations, new analysis techniques have been developed to probe features in heart rate behavior that are not detectable by traditional analysis methods. Analysis of fractal scaling exponents by detrended fluctuation analysis (DFA) is one such method that describes the fractal-like correlation properties of R-R interval data. Sample entropy (SampEn) is another nonlinear method that quantifies the amount of complexity in the time-series data. Breakdown of short-term fractal-like behaviour of heart rate indicates an increased risk for adverse cardiovascular events and mortality, but the pathophysiological background for altered fractal heart rate dynamics is not known. Despite a large body of data concerning the changes in spectral characteristics of HRV during anesthesia, there is little information on the effects of theses physiological interventions on non-linear characteristics of heart rate behavior. This study was designed to assess the changes in the nonlinear features of HRV caused by the spinal anesthesia and general anesthesia. The main purpose was to gain insight into the physiological background for fractal and complexity characteristics of heart rate dynamics. Short-term fractal scaling exponent (α1)along with spectral components of HRV were analyzed during the following anesthesia interventions in patients : (1) spinal anesthesia group : 1)normal dose (Group HM, n=19), 2)low dose (Group LM, n=20), 3) low dose combine fentanyl (Group LMf, n=20); (2) general anesthesia group: 1)total intravenous propofol infusion (Group P, n=15),2) inhalation induction with desflurane (Group D, n=18) Method After institutional ethical approval and getting informed consent, we recorded the electrocardiogram of 100 ASA class I (American Society of Anesthesiologist physical status class I) patients proposed to receive elective surgery. Patients were excluded if they suffered from severe ischemic heart disease, congestive heart failure, diabetes mellitus, or other disorders known to affect autonomic function. None of the patient was taking medications that affect cardiovascular function. Each patient fasted at least 8h prior to testing. Vigorous exercise, alcohol and coffee were also forbidden for 48 h before the operation. On arrival to the operating room, the patients lay in a supine position in a quiet room at least 5 min prior to data collection. In Group HM and LM, 12mg and 6mg of 0.5% hyperbaric bupivacain were injected respectively. In Group LMf, 6mg of 0.5% hyperbaric bupivacaine was supplemented with 20μg of intrathecal fentanyl. All patients received 100% oxygen via face mask for 2 to 3 min prior to induction of general anesthesia. In Group P, patients received propofol infusion at a rate of 300ug/kg/min . In Group D, anesthesia was induced with 3-6-9-12% desflurane increasing gradually in 2L/min O2 and 2L/min N20. Arterial oxygen saturation (SpO2) and end-tidal carbon dioxide (ETCO2) were monitored, and normoventilation was maintained with gentle IPPV via mask if required. Depth of anesthesia was monitor by AAI (A-Line ARX Index) continuously until the value reached 35. Therefore, the HRV measurements were performed at AAI values of 60 to 35 and less than 35. The electrocardiogram data was transferred into the hard disk in a personal computer and offline analysis was performed. Results Short-term fractal scaling exponent (α1) decreased during spinal anesthesia in three groups ( Group HM:from 1.24±0.15 to 0.78±0.11;Group LM:from 1.32±0.25 to 0.98±0.21;Group LMf:from 1.28±0.17 to 0.8±0.21，P<0.0001).α1 increased during both general anesthesia group at AAI value of 60 to 35. Thenα1 decreased during the AAI value less than 35 (Group P: from 1.14±0.2 to 0.94±0.35,P<0.05; Group D:from 1.1±0.26 to 0.7±0.31,P<0.0001). Conventional HRV indices did not show the dynamic changes in Group P.Group HM, LM, LMf and Group D decreased the normalized low frequency spectral power and LF/HF ratio and increased normalized high frequency spectral power (p<0.05). SampEn value decreased in Group LM, LMf and Group D. In addition, the receiver operating characteristic (ROC) was used to estimate the sensitivity and specificity of classification of subjects in awake and after anesthesia states using different parameters. The results show that the DFAα1 is a better indicator for distinguishing baseline from anesthesia state. Conclusion Spinal and deep general anesthesia result the breakdown of short-term fractal-like behaviour of heart rate. Incremental depth of anesthesia until AAI less than 35 results in bidirectional changes in correlation properties of R-R interval dynamics. The results suggest that decrease sympathetic outflow at the same time activation of vagal outflow explains the breakdown of fractal-like behaviour of human heart rate dynamics. Change in α1 can be detected also in light anesthesia levels, when the conventional measures of HRV can not be applied. In addition, α1 is a better indicator for distinguishing baseline from spinal anesthesia state.