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Neuroscience in Anesthesiology and Perioperative Medicine: Technical Communication

Validation of a Stand-Alone Near-Infrared Spectroscopy System for Monitoring Cerebral Autoregulation During Cardiac Surgery

Ono, Masahiro MD, PhD*; Zheng, Yueying MD; Joshi, Brijen MD; Sigl, Jeffrey C. PhD§; Hogue, Charles W. MD

Author Information
doi: 10.1213/ANE.0b013e318271fb10

Monitoring of cerebral blood flow (CBF) autoregulation with a moving linear regression correlation coefficient between arterial blood pressure (ABP) and middle cerebral artery transcranial Doppler (TCD) measured blood flow velocity has been validated in volunteers and in patients with head trauma, carotid artery stenosis, acute ischemic stroke, subarachnoid hemorrhage, and those undergoing cardiac surgery.1–5 By allowing clinicians to individualize ABP targets, monitoring of autoregulation may have benefits for improving neurologic outcomes for patients undergoing cardiac surgery with cardiopulmonary bypass (CPB) who have a high prevalence of cerebral vascular disease.

There is no “gold standard” for measuring CBF to determine autoregulation.6 Monitors of brain oxygenation, such as direct tissue O2 tension and jugular bulb O2 saturation measurements, have been used as surrogates of CBF for monitoring autoregulation.6,7 Near-infrared spectroscopy (NIRS) is increasingly used during cardiac surgery to monitor regional cerebral oxygen saturation (rScO2). Because these measurements are weighted toward venous blood, regional cerebral O2 saturation is an indicator of the adequacy of cerebral O2 supply versus demand. We have found that rScO2 provides a clinically acceptable surrogate of CBF for experimental and clinical autoregulation monitoring.5,8,9 Unlike TCD methods, the use of rScO2 as a proxy for CBF does not require a trained technician, is noninvasive and continuous, and thus could be widely applied in a broad range of clinical settings. Our previously validated methods, though, require complex signal processing and analysis using specialized software and a personal computer that limits its applications mostly to research. The availability of a stand-alone, “plug-and-play” system for monitoring cerebral autoregulation would provide clinicians with a method for optimizing ABP during CPB and in other clinical areas.

The purpose of this study was to evaluate the accuracy of an investigational prototype NIRS cerebral autoregulation monitor with specialized hardware and software compared with TCD methods. We hypothesized that the average autoregulation indices during CPB obtained using an investigational prototype NIRS monitor will be correlated and have good agreement with those calculated using standard Doppler methods. We further hypothesized that the lower limit of CBF autoregulation determined using the investigational prototype NIRS autoregulation monitor will be equivalent with that obtained using TCD autoregulation methods. A secondary aim of the study was to compare NIRS autoregulation indices from the prototype monitor with our standard personal computer–based methods.


Using a protocol approved by the Johns Hopkins Medical Institute’s research review board, and after receiving written informed consent, 70 patients undergoing cardiac surgery at the Johns Hopkins Hospital with CPB between July 30, 2010, and July 29, 2011, were enrolled in this study. Patient care during surgery including management during CPB were similar to our previous reports.5,9,10 Briefly, the patients received midazolam, fentanyl, pancuronium, and isoflurane for anesthesia and muscle relaxation. The patient’s ABP was monitored with a direct radial artery catheter. Nonpulsatile CPB was with a nonocclusive roller pump with flows of 2.0 and 2.4 L/min/m2 and a membrane oxygenator. α-Stat pH management was used, and the patients were monitored with continuous in-line arterial blood gas monitoring calibrated hourly with arterial blood gas measurements. ABP during CPB was based on usual institutional practice. Clinicians caring for the patients were blinded to the autoregulation monitoring data.

Autoregulation Monitoring

Autoregulation measurements were observed during spontaneous fluctuations in ABP that occur during the conduct of cardiac surgery. No medications or maneuvers were performed to manipulate ABP for measuring autoregulation. Bilateral middle cerebral artery CBF velocity was monitored with TCD (Doppler Box, DWL, Compumedics, Charlotte, NC) using two 2.5-MHz transducers held in place with brackets fitted on a headband. The depth of insonation was varied between 35 mm and 52 mm until representative spectral middle cerebral artery flow was identified and the probes slightly manipulated to obtain the maximal flow signal. The TCD signals were monitored throughout the procedure to ensure that the probes remained appropriately positioned. Bilateral rScO2 was monitored by means of an NIRS monitor (Somanetics INVOS, Covidien, Boulder, CO) using sensors placed on the right and left forehead. Baseline calibration was performed while the patients were breathing room air. The algorithm for derivation of rScO2 has been described.11 ABP was obtained from the operating room hemodynamic monitor (GE Medical, Milwaukee, WI). The ABP and TCD signals were processed by using a personal computer–based system using ICM+ software (University of Cambridge, Cambridge, UK) to compute reference indices of autoregulation as previously described.5,9,10 The ABP and rScO2 signals from the same InvosTM were also processed by an investigational prototype NIRS-based monitor (Covidien) with customized software running on a multiparameter monitoring system (VitalSync; Covidien), which computed a second index of autoregulation (cerebral oximetry index [Cox]) for evaluation. Specifically, the analog ABP signal from the operating room monitor and the TCD signals were connected to an analog-to-digital convertor directly connected to the personal computer–based system. Using a customized cable, the ABP signal from the hemodynamic monitor was simultaneously connected directly to the prototype NIRS-based monitor containing an internal analog-to-digital convertor. The digital output of the latter was then processed by the prototype autoregulation monitor. A schemata of the signal acquisition system is shown in Figure 1.

Figure 1
Figure 1:
A schematic diagram of the prototype near-infrared spectroscopy (NIRS)–based autoregulation monitor and the additional equipment used in the study. Digital signals from the same standard Invos 5100 monitor (Covidien, Boulder, CO) were simultaneously sampled by the personal computer–based system and the prototype monitor. Arterial blood pressure (ABP) signals were digitized with an analog-to-digital convertor (ADC) that was internal for prototype monitor. Mean velocity index (Mx) and cerebral oximetry index (COx) were then calculated as the Pearson correlation coefficient between ABP and transcranial Doppler (TCD) cerebral blood flow velocity or cerebral oximetry signals, respectively (see text for details). Note: ICM+ (University of Cambridge, Cambridge, UK) software was used for the personal computer–based autoregulation monitoring.

The personal computer–based system sampled the ABP, TCD, and NIRS signals at 60 Hz and time-integrated them as nonoverlapping 10-second mean values, which is equivalent to applying a moving average filter with a 10-second time window and resampling at 0.1 Hz.5,9,10 This approach eliminates high-frequency noise from the respiratory and pulse frequencies, while allowing detection of oscillations and transients occurring < 0.05 Hz. A continuous, moving Pearson correlation coefficient was performed between the ABP and the TCD signals, rendering mean velocity index (Mx). The same calculation was performed using the ABP and rScO2 signals rendering COx. For each consecutive 10-second period, averaged paired values of 300-second duration were used for analysis, incorporating 30 data points for each index.

The prototype NIRS autoregulation monitor used a proprietary algorithm, resulting in a simultaneously determined, independent autoregulation index COx, which could be compared with both metrics computed by the personal computer–based system (Mx and COx). These methods included similar sampling frequencies and filtering processes as the personal computer–based system. COx was computed as a Pearson correlation coefficient between ABP and rScO2 signals using a similar period of sampling and data averaging. Intact CBF autoregulation is indicated by values of Mx and COx that approach 0 or that are negative because CBF and ABP are not correlated. When ABP is below the autoregulation limit, Mx and COx approach 1, indicating that CBF is pressure passive.

Sample-Size Estimates

The sample-size estimates of the study were based on the correlation and agreement between the measures of Mx and COx during CPB. This estimate was based on our previous experiences with similar monitoring of 227 adult patients undergoing CPB where the Mx value (mean ± SD) during CPB was 0.23 ± 0.17. We randomly sampled data from this population comparing the simultaneous Mx and COx measurements. This preliminary analysis demonstrated that 50 patients would provide correlation between Mx and COx with P = 0.0385 and bias of –0.10 ± 0.21. A final sample of 70 patients was chosen to allow for incomplete data collection due to unanticipated technical difficulties.

Data Analysis

Time-averaged values for Mx and for COx obtained with the NIRS autoregulation prototype monitor recorded during CPB were compared with linear regression and Pearson correlation. Bland–Altman analysis was used to compare the differences in Mx and COx versus the average of these values.12 This analysis was repeated for COx obtained from the personal computer–based system and COx obtained from the prototype monitor. Values of Mx and COx were further categorized into 5 mm Hg bins of ABP for each patient. The Mx cutoff indicating the lower limit of autoregulation is not clearly known, but it is likely to be between 0.3 and 0.5 as previously noted.1,5,8–10 The lower limit of autoregulation was defined in this study as the ABP where Mx incrementally increased to ≥0.4. When Mx was ≥0.4 at all ABP during CPB, the autoregulation threshold was defined as that ABP where Mx had the lowest value. The average COx value at the ABP associated with the lower limit of autoregulation was determined. This value was then applied to the data as the COx lower limit of autoregulation. ABP at the lower limit of autoregulation determined with COx was compared with that determined by Mx with Wilcoxon signed rank test. Associations between indices were assessed with Pearson correlation, using the Fisher transformation, to calculate the 95% confidence intervals (CIs). Analysis was performed with GraphPad Prism software (GraphPad Software, Inc, La Jolla, CA), Stata software (Version 9.0; Stata Corp, College Station, TX), and SPSS (SPSS version 17, IBM Statistics, Armonk, NY).


Clinical data from the 70 patients included in the study are listed in Table 1. The average Mx for the cohort was 0.27 ± 0.16 and the average COx derived from the prototype monitor was 0.34 ± 0.21. There was significant correlation between Mx and COx derived from the prototype NIRS autoregulation monitor (r = 0.510; 95% CI, 0.414–0.595; P < 0.001) and good agreement between the methods (bias, –0.07 ± 0.19) as shown in Figures 2 and 3, respectively. Comparison was made between COx determined with the prototype monitor and that determined with our personal computer–based method that has been previously validated.5,8 There was strong correlation (r = 0.957; 95% CI, 0.945–0.966; P < 0.001) and good agreement between COx determined with both methods (0.06 ± 0.06).

Table 1
Table 1:
Patient Medical and Operative Data
Figure 2
Figure 2:
Correlation and 95% confidence intervals between mean velocity index (Mx) and cerebral oximetry index (COx). Mx was determined with a personal computer–based system as the correlation coefficient between transcranial Doppler measured cerebral blood flow velocity and mean arterial blood pressure (ABP). COx is the correlation between near-infrared spectroscopy (NIRS)–measured cerebral oximetry and mean ABP.
Figure 3
Figure 3:
Bias and 95% confidence intervals between mean velocity index (Mx) and cerebral oximetry index (COx).

A lower limit of autoregulation was observed in all patients with Mx and COx. The ABP at the lower limit of autoregulation based on Mx monitoring was 63 ± 11 mm Hg (95% prediction interval, 52–74 mm Hg). The average COx at this ABP was 0.38 ± 0.26 for the personal computer–based method and 0.44 ± 0.26 for the prototype monitor. Based on the COx determined by the prototype monitor, the ABP at the lower limit of autoregulation was 59 ± 9 mm Hg (95% prediction interval, 50–68 mm Hg; P = 0.026 versus Mx).


These results show that COx determined with an investigational prototype NIRS autoregulation monitor is correlated and in good agreement with previously validated TCD methods for autoregulation monitoring. The ABP at the lower limit of autoregulation was similar between the 2 methods, suggesting that COx using this monitor may be an acceptable substitute for Mx monitoring during CPB.

During CPB, systemic blood flow is calculated on the basis of body surface area and temperature and then adjusted, depending on indicators of adequate global tissue perfusion (mixed venous O2 saturation, pH, etc.). CBF is assumed to be sufficient because CBF–arterial pressure autoregulation remains intact with CPB flows between 1.6 and 2.4 L/min/m2 when -stat pH management is used.13,14 Supported by the latter data, an ABP of 50–60 mm Hg is widely considered to be the minimal acceptable ABP during CPB. This practice fails to consider that CBF–ABP autoregulation has wide individual variation, may be altered in many common conditions (e.g., hypertension, diabetes, stroke), and is derived using statistical methods that have been questioned (i.e., based on limited data from individuals).15–22 It is important to note that the current arbitrary standard of care for managing ABP during CPB may predispose the increasing number of surgical patients with cerebral vascular disease to cerebral hypoperfusion and ischemic brain injury.23,24 In fact, a high proportion of strokes after cardiac surgery are hypoperfusion-type watershed strokes that have been shown to be related to decreases in ABP during CPB.24

Previous laboratory and clinical studies have validated COx as a reliable monitor of CBF autoregulation. In piglets made hypotensive by inflation of a balloon in the inferior vena cava, COx was correlated (r = 0.67) and had good agreement (bias, 0.03) with Doppler flux monitoring of the frontal–partial cortex.8 A COx value of >0.36 had 92% sensitivity (73%–99%) and 63% specificity (48%–76%) for identifying the autoregulation threshold. In a study of 60 adult patients, we found significant correlation (r = 0.55, P < 0.0001) and good agreement (bias, 0.08 ± 0.18) between Mx and COx during CPB.5 In patients undergoing CPB, the average lower limit of autoregulation was found to be 66 mm Hg, but this value ranged between 40 mm Hg and 90 mm Hg.5,25 The range of ABPs at the lower limit of autoregulation in this study (33–83 mm Hg) is similar to our previous studies. The data from this study corroborate other investigations during CPB and in noncardiac surgery settings, showing that COx monitoring is a clinically reliable method for autoregulation monitoring.5,8,26–28

Although a decrease in ABP is a common consequence of general and regional anesthesia, there is currently no universally accepted definition of intraoperative hypotension. In fact, in a systematic review, Bijker et al.29 identified 130 articles in the anesthesiology literature that referred to 140 different definitions of intraoperative hypotension. An absolute threshold or relative change in systolic blood pressure or ABP from baseline or the requirement of a clinical intervention for treatment was most often applied. Monitoring of CBF autoregulation may provide a more clinically precise method for individualizing an ABP threshold that might compromise organ perfusion. In fact, our current data and previous reports suggest a wide variability in the lower limit of autoregulation, making a priori definition of intraoperative hypotension nearly impossible.5,9,10,25 Furthermore, in our previous investigation, we found that monitoring the patients with COx was more accurate than clinical history and preoperative ABP in identifying the ABP at the lower limit of autoregulation.25 Although there may be varying organ tolerance to low ABP depending on disease state, in patients undergoing CPB, ABP management aimed at assessments of cerebral perfusion with NIRS was found to decrease the frequency of major organ morbidity and mortality.30

The prototype monitor used in this study requires simple connection of the ABP output signal from the operating room hemodynamic monitor to a modified NIRS monitor now currently available. Further refinement of the methods could further enhance the clinical application of the monitor to any clinical situation where NIRS and invasive ABP monitoring are currently performed. The future development of interfaces with noninvasive ABP monitoring systems could extend this use to other operative and critical areas for more physiologic targeting of ABP.31

As mentioned, our use of an Mx ≥ 0.4 as indicating the lower limit of autoregulation, while supported by experimental studies, is admittedly arbitrary.1,5,8–10 Clinically, rather than attempting to determine an exact autoregulation threshold, clinicians may rather target an ABP associated with the lowest Mx or the ABP with optimal autoregulation. Indeed, optimizing cerebral perfusion pressure within the autoregulation range is associated with improved outcomes in patients with traumatic brain injury.32,33 In previous studies, we have noted that some patients have an impaired autoregulation pattern during CPB based on an average Mx ≥ 0.4 or an Mx ≥ 0.4 at all ABPs.10,25,34 In these situations, though, an autoregulation “curve” is still often present albeit with a limited plateau. The latter might allow for targeting an ABP in an optimal autoregulatory range by choosing that ABP associated with the lowest Mx. In this study, we denoted the mean ABP at the lower limit of autoregulation when Mx was ≥ 0.4 at all ABPs during CPB. This approach is only is relevant for our comparison of the ABP at the limit of autoregulation between our standard methods of autoregulation testing and the NIRS-based autoregulation monitor. Our approach does not affect the primary analysis where we compare the average Mx with COx with correlation and bias analyses.

In conclusion, monitoring CBF autoregulation with a modified, stand-alone NIRS monitor is correlated and in good agreement with TCD-based methods. The availability of such a device would allow widespread autoregulation monitoring as a means of individualizing ABP targets during CPB.


Name: Masahiro Ono, MD, PhD.

Contribution: This author helped collect the data, analyze the data, and prepare the manuscript.

Attestation: Masahiro Ono has viewed and approved the manuscript.

Conflict of Interest: The author has no conflict of interest to declare.

Name: Yueying Zheng, MD.

Contribution: This author helped collect the data, analyze the data, and prepare the manuscript.

Attestation: Yueying Zheng has viewed and approved the manuscript.

Conflict of Interest: The author has no conflict of interest to declare.

Name: Brijen Joshi, MD.

Contribution: This author helped collect the data, analyze the data, and prepare the manuscript.

Attestation: Brijen Joshi has viewed and approved the manuscript.

Conflict of Interest: The author has no conflict of interest to declare.

Name: Jeffrey C. Sigl, PhD.

Contribution: This author helped analyze the data and prepare the manuscript.

Attestation: Jeffrey C. Sigl has viewed and approved the manuscript.

Conflict of Interest: Dr. Sigl is an employee of Covidien PLC, the makers of the prototype autoregulation monitor used in this study.

Name: Charles W. Hogue, MD.

Contribution: This author helped conceptualize the study, collect the data, analyze the data, and prepare the manuscript.

Attestation: Charles W. Hogue has viewed and approved the manuscript.

Conflict of Interest: Dr. Hogue received research funding for this study from Covidien, Inc.


Dr. Charles Hogue is the Associate Editor-in-Chief for Cardiovascular Anesthesiology for the journal. This manuscript was handled by Dr. Gregory Crosby, Section Editor for Neuroscience in Anesthesiology and Perioperative Medicine, and Dr. Hogue was not involved in any way with the editorial process or decision


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