Monitoring nociception during general anesthesia with cardiorespiratory coherence.

A novel wavelet transform cardiorespiratory coherence (WTCRC) algorithm has been developed to measure the autonomic state. WTCRC may be used as a nociception index, ranging from 0 (no nociception, strong coherence) to 100 (strong nociception, low coherence). The aim of this study is to estimate the sensitivity of the algorithm to nociception (dental dam insertions) and antinociception (bolus doses of anesthetic drugs). WTCRC's sensitivity is compared to mean heart rate (HRmean) and mean non-invasive blood pressure (NIBPmean), which are commonly used clinical signs. Data were collected from 48 children receiving general anesthesia during dental surgery. The times of dental dam insertion and anesthetic bolus events were noted in real-time during surgeries. 42 dental dam insertion and 57 anesthetic bolus events were analyzed. The change in average WTCRC, HRmean, and NIBPmean was calculated between a baseline period before each event and a response period after. A Wilcoxon rank-sum test was used to compare changes. Dental dam insertion changed the WTCRC nociception index by an average of 14 (82 %) [95 % CI from 7.4 to 19], HRmean by 7.3 beats/min (8.1 %) [5.6-9.6], and NIBPmean by 8.3 mmHg (12 %) [4.9-13]. A bolus dose of anesthetics changed the WTCRC by -15 (-50 %) [-21 to -9.3], HRmean by -4.8 beats/min (4.6 %) [-6.6 to -2.9], and NIBPmean by -2.6 mmHg (3.4 %) [-4.7 to -0.50]. A nociception index based on cardiorespiratory coherence is more sensitive to nociception and antinociception than are HRmean or NIBPmean. The WTCRC algorithm shows promise for noninvasively monitoring nociception during general anesthesia.

An evaluation of a novel software tool for detecting changes in physiological monitoring.

BACKGROUND: We have developed a software tool (iAssist) to assist clinicians as they monitor the physiological data that guide their actions during anesthesia. The system tracks the statistical properties of multiple dynamic physiological processes and identifies new trend patterns. We report our initial evaluation of this tool (in pseudo real-time) and compare the detection of trend changes to a post hoc visual review of the full trend. We suggest a combination of criteria by which to evaluate the performance of monitoring devices that aim to enhance trend detection. METHODS: Nineteen children and 28 adults consented to be included in the study, encompassing more than 68 h of anesthesia. In each surgical case, an anesthesiologist reported all perceived clinical changes in monitoring in real-time. A trained observer simultaneously documented the verbally reported changes and every anesthesiologist action. The same cases were subsequently evaluated offline (in pseudo real-time) by a novel software tool (iAssist). Heart rate, end-tidal carbon dioxide, exhaled minute ventilation, and respiratory rate were modeled using a dynamic linear growth model whose noise distribution was estimated by an adaptive Kalman filter based on a recursive expectation-maximization method. Changes were detected by adaptive local Cumulative Sum testing. Changes in the mean arterial noninvasive blood pressures and oxygen saturation were detected using adaptive Cumulative Sum testing on a filtered residual from an exponentially weighted moving averaging filter. In post hoc analysis, each change detected by iAssist was graded independently by two clinicians using a graphical display of the whole case. Missed changes were recorded. RESULTS: The iAssist software tool detected 869 true positive changes (at an average of 12.76/h) with a sensitivity of 0.91 and positive predictive value of 0.87. The post hoc review identified 91 missed changes (at an average of 1.34/h), resulting in an overall ratio of true positive rates to false-negative rates of 9.55. The clinicians in real-time reported 209 changes in trend (at an average of 3.07/h). CONCLUSION: The algorithms perform favorably compared with a visual inspection of the complete trend. Further research is needed to identify when and how to draw the clinician's attention to these changes.

Saturation of the right-leg drive amplifier in low-voltage ECG monitors.

Electrocardiogram (ECG) monitoring is a critical tool in patient care, but its utility is often balanced with frustration from clinicians who are constantly distracted by false alarms. This has motivated the need to readdress the major factors that contribute to ECG noise with the goal of reducing false alarms. In this study, we describe a previously unreported phenomenon in which ECG noise can result from an unintended interaction between two systems: 1) the dc lead-off circuitry that is used to detect whether electrodes fall off the patient; and 2) the right-leg drive (RLD) system that is responsible for reducing ac common-mode noise that couples into the body. Using a circuit model to study this interaction, we found that in the presence of a dc lead-off system, even moderate increases in the right-leg skin-electrode resistance can cause the RLD amplifier to saturate. Such saturation can produce ECG noise because the RLD amplifier will no longer be capable of attenuating ac common-mode noise on the body. RLD saturation is particularly a problem for modern ECG monitors that use low-voltage supply levels. For example, for a 12-lead ECG and a 2 V power supply, saturation will occur when the right-leg electrode resistance reaches only 2 MΩ. We discuss several design solutions that can be used in low-voltage monitors to avoid RLD saturation.

Real-time cardiorespiratory coherence is blind to changes in respiration during general anesthesia.

PURPOSE: A novel real-time cardiorespiratory coherence (CRC) algorithm has been developed to monitor nociception during general anesthesia. CRC uses custom designed filters to track and analyze the respiratory sinus arrhythmia (RSA) as it moves in time and frequency. CRC is a form of sensor fusion between heart rate and respiration, estimating the strength of linear coupling between the two signals. The aim of this study was to estimate the effect of changes in respiration rate (RR) and peak airway pressure (PPaw) on CRC. The response of CRC was compared to a prior offline wavelet-based algorithm (WTCRC) as well as traditional univariate heart rate variability (HRV) measures. A nociception index was created for each algorithm, ranging from 0 (no nociception) to 100 (strong nociception). METHODS: Following ethics approval and informed consent, data were collected from 48 children receiving general anesthesia during dental surgery. The times of change in RR and PPaw events were noted in real-time. A total of 43 RR and 35 PPaw change events were analyzed post hoc in pseudo real-time. The nociception index averages were compared between a baseline period and a response period around each event. A Wilcoxon rank-sum test was used to compare changes. RESULTS: The change in RR changed the CRC nociception index by an average of -2.2 [95% CI from -10 to 4.7] (P > 0.3), and the change in PPaw changed the CRC nociception index by an average of 5.4 [-1.0 to 11] (P > 0.1). The changes were smaller than those of many traditional HRV measures. CONCLUSIONS: Real-time CRC was blind to the changes in respiration, and was less sensitive than many of the traditional HRV measures. A nociception index based on CRC can thus function across a wider range of respiratory conditions than can many traditional univariate HRV measures. The real-time CRC algorithm shows promise for monitoring nociception during general anesthesia.

Sleep-wake and circadian-dependent variation of cardiorespiratory coherence.

The risk of adverse cardiovascular events is elevated in the morning compared to the rest of the day. A circadian and a sleep-wake dependent variation in the regulation of the cardiovascular system could contribute to this increased cardiovascular risk. Using an ultradian sleep-wake cycle (USW) procedure, our aim was to explore the effects of the sleep-wake and circadian cycles on cardiorespiratory coherence (CRC) as a measure of autonomic nervous system (ANS) state. Our results suggest a shift toward parasympathetic dominance with deepening of sleep. Conversely, REM sleep is associated with a sympathetic dominance comparable to levels observed during wakefulness. A circadian rhythm was observed for CRC during wakefulness and all sleep stages. Maximal sympathetic dominance was observed in the morning, as measured by CRC during wakefulness and REM sleep, consistent with studies showing increased cardiac risk in the morning. This study provides evidence that circadian and sleep processes interact to influence the ANS modulation of the heart.

Real-time cardiorespiratory coherence detects antinociception during general anesthesia.

Heart rate variability (HRV) may provide anesthesiologists with a noninvasive tool for monitoring nociception during general anesthesia. A novel real-time cardiorespiratory coherence (CRC) algorithm has been developed to analyze the strength of linear coupling between heart rate (HR) and respiration. CRC values range from 0 (low coherence, strong nociception) to 1 (high coherence, no nociception). The algorithm uses specially designed filters to operate in real-time, minimizing computational complexity and time delay. In the standard HRV high frequency band of 0.15 - 0.4 Hz, the real-time delay is only 5.25 - 3.25 s. We have assessed the algorithm's response to 60 anesthetic bolus events (a large dose of anesthetics given over a short time; strongly antinociceptive) recorded in 47 pediatric patients receiving general anesthesia. Real-time CRC responded strongly to bolus events, changing by an average of 30%. For comparison, three traditional measures of HRV (LF/HF ratio, SDNN, and RMSSD) responded on average by only 3.8%, 14%, and 3.9%, respectively. Finally, two traditional clinical measures of nociception (HR and blood pressure) responded on average by only 3.9% and 0.91%, respectively. CRC may thus be used as a real-time nociception monitor during general anesthesia.