Heart Rate Variability Dynamics During Low-Dose Propofol and Dexmedetomidine Anesthesia

Abstract

Heart rate variability (HRV) has been observed to decrease during anesthesia, but changes in HRV during loss and recovery of consciousness have not been studied in detail. In this study, HRV dynamics during low-dose propofol (N = 10) and dexmedetomidine (N = 9) anesthesia were estimated by using time-varying methods. Standard time-domain and frequency-domain measures of HRV were included in the analysis. Frequency-domain parameters like low frequency (LF) and high frequency (HF) component powers were extracted from time-varying spectrum estimates obtained with a Kalman smoother algorithm. The Kalman smoother is a parametric spectrum estimation approach based on time-varying autoregressive (AR) modeling. Prior to loss of consciousness, an increase in HF component power indicating increase in vagal control of heart rate (HR) was observed for both anesthetics. The relative increase of vagal control over sympathetic control of HR was overall larger for dexmedetomidine which is in line with the known sympatholytic effect of this anesthetic. Even though the inter-individual variability in the HRV parameters was substantial, the results suggest the usefulness of HRV analysis in monitoring dexmedetomidine anesthesia.

Instantaneous monitoring of heart beat dynamics during anesthesia and sedation

“…Within a multimodal framework including EEG, cardiovascular and respiratory assessment, we have devised a point process framework able to successfully characterize the variations in heartbeat dynamics when applied to PROP and DMED administration protocols. Previous results from our groups and other authors have stressed the importance of dynamic autonomic monitoring during anesthesia, and particularly during PROP and DMED [13,15,19,20,34,44,[46][47][48]51,53,54,72,73,75]. In this presentation, we provide further evidence that our refined methods for analyzing the heartbeat dynamics during administration of specific anesthetic drugs are able to overcome nonstationary limitations, thus providing new real-time monitoring approaches to patients receiving anesthesia.…”

“…A recent review article by Mazzeo and colleagues [34] summarizes some of the most relevant findings and limits of HRV as a diagnostic and prognostic tool in anesthesia and concludes that 'investigation of HRV as a method of monitoring the depth of anesthesia, assessing the response to painful stimuli, did not yield uniform results and needs more extensive investigations.' Several other important studies have considered HRV to quantify anesthesia [35][36][37][38][39][40][41][42][43][44][45][46][47][48][49][50][51], demonstrating the usefulness of HRV measures. In particular, time-varying identification methods have provided successful characterization of PROP and DMED effects on the ANS [19,20,24,28,44,51].…”

“…Several other important studies have considered HRV to quantify anesthesia [35][36][37][38][39][40][41][42][43][44][45][46][47][48][49][50][51], demonstrating the usefulness of HRV measures. In particular, time-varying identification methods have provided successful characterization of PROP and DMED effects on the ANS [19,20,24,28,44,51]. Several other studies have considered ANS measures in an attempt to monitor pain during anesthesia [52][53][54][55][56][57][58][59][60][61][62][63].…”

Electroencephalogram reactivity to verbal command after dexmedetomidine, propofol and sevoflurane-induced unresponsiveness

“…Sevoflurane was studied similarly but subjects in this third group did not participate in the positron emission tomography study. Other results of the project have been reported previously [25][26][27][28]. The study protocol was approved by the Ethical Committee of the Hospital District of Southwest Finland (Turku, Finland) and the National Agency for Medicines.…”

“…While the filter algorithm is applicable for real-time processing, smoother algorithm is preferable when all the measurements are available for off-line analysis [32]. Detailed description of the exact algorithm applied here can be found in Georgiadis et al [33], as well as in Tarvainen et al [28], where the selections affecting the adaptation of the algorithm in a changing environment are presented in detail. The adaptation of the derived algorithm is adjusted with one parameter, namely an update coefficient (UC) [28,33].…”