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Predicting the Limits of Cerebral Autoregulation During Cardiopulmonary Bypass

Joshi, Brijen MD*; Ono, Masahiro MD; Brown, Charles MD*; Brady, Kenneth MD; Easley, R. Blaine MD§; Yenokyan, Gayane PhD; Gottesman, Rebecca F. MD, PhD; Hogue, Charles W. MD*

doi: 10.1213/ANE.0b013e31823d292a
Cardiovascular Anesthesiology
Chinese Language Editions

BACKGROUND: Mean arterial blood pressure (MAP) targets are empirically chosen during cardiopulmonary bypass (CPB). We have previously shown that near-infrared spectroscopy (NIRS) can be used clinically for monitoring cerebral blood flow autoregulation. The hypothesis of this study was that real-time autoregulation monitoring using NIRS-based methods is more accurate for delineating the MAP at the lower limit of autoregulation (LLA) during CPB than empiric determinations based on age, preoperative history, and preoperative blood pressure.

METHODS: Two hundred thirty-two patients undergoing coronary artery bypass graft and/or valve surgery with CPB underwent transcranial Doppler monitoring of the middle cerebral arteries and NIRS monitoring. A continuous, moving Pearson correlation coefficient was calculated between MAP and cerebral blood flow velocity and between MAP and NIRS data to generate mean velocity index and cerebral oximeter index. When autoregulated, there is no correlation between cerebral blood flow and MAP (i.e., mean velocity and cerebral oximetry indices approach 0); when MAP is below the LLA, mean velocity and cerebral oximetry indices approach 1. The LLA was defined as the MAP at which mean velocity index increased with declining MAP to ≥0.4. Linear regression was performed to assess the relation between preoperative systolic blood pressure, MAP, MAP in 10% decrements from baseline, and average cerebral oximetry index with MAP at the LLA.

RESULTS: The MAP at the LLA was 66 mm Hg (95% prediction interval, 43 to 90 mm Hg) for the 225 patients in which this limit was observed. There was no relationship between preoperative MAP and the LLA (P = 0.829) after adjusting for age, gender, prior stroke, diabetes, and hypertension, but a cerebral oximetry index value of >0.5 was associated with the LLA (P = 0.022). The LLA could be identified with cerebral oximetry index in 219 (94.4%) patients. The mean difference in the LLA for mean velocity index versus cerebral oximetry index was −0.2 ± 10.2 mm Hg (95% CI, −1.5 to 1.2 mm Hg). Preoperative systolic blood pressure was associated with a higher LLA (P = 0.046) but only for those with systolic blood pressure ≤160 mm Hg.

CONCLUSIONS: There is a wide range of MAP at the LLA in patients during CPB, making estimation of this target difficult. Real-time monitoring of autoregulation with cerebral oximetry index may provide a more rational means for individualizing MAP during CPB.

Published ahead of print November 21, 2011 Supplemental Digital Content is available in the text.

From the *Department of Anesthesiology and Critical Care Medicine and Division of Cardiac Surgery, Department of Surgery, Johns Hopkins University School of Medicine, Baltimore, MD; The Texas Children's Hospital, Houston, TX; §Division of Pediatric Cardiovascular Anesthesiology, Texas Children's Hospital; Biostatistics Center, Johns Hopkins University Bloomberg School of Public Health, Baltimore, MD; and Department of Neurology, Johns Hopkins University School of Medicine.

Funding: National Institutes of Health and the Mid-Atlantic Affiliate of the American Heart Association.

Conflict of Interest: See Disclosures at end of article.

Reprints will not be available from the authors.

Address correspondence to Charles W. Hogue, MD, Department of Anesthesiology and Critical Care Medicine, Johns Hopkins University School of Medicine, Johns Hopkins Hospital, 600 N. Wolfe St., Tower 711, Baltimore, MD 21287. Address e-mail to chogue2@jhmi.edu.

Accepted October 10, 2011

Published ahead of print November 21, 2011

Because cerebral blood flow autoregulation is functional during cardiopulmonary bypass (CPB) using α-stat pH management, arterial blood pressure targets are empirically chosen often to a mean arterial blood pressure (MAP) of 50 mm Hg, depending on patient age, preoperative blood pressure, or medical history.1,2 The foundation of this practice, though, is based on data that are more than 15 years old and derived from patients who were generally younger and had fewer comorbid conditions than patients in current practices.35 The accuracy of empiric targeting of MAP for identifying a pressure above the lower limit of autoregulation (LLA) in clinical practice is not known. Other data suggest a benefit of higher MAP during CPB on myocardial and neurologic outcomes, but these results have limitations and have not been independently reconfirmed.69 Importantly, whether the practice of targeting low MAP during CPB is appropriate for the increasing number of elderly patients with cerebral vascular disease is not known.10,11Our group, in fact, has found a high frequency of hypoperfusion type “watershed” strokes after cardiac surgery that were associated with MAP during CPB that was ≥10 mm Hg lower than before CPB.11

Real-time autoregulation monitoring can be accomplished by the continuous calculation of the correlation between transcranial Doppler-measured cerebral blood flow velocity of the middle cerebral artery and cerebral perfusion pressure (termed mean velocity index).1214 Our group has demonstrated in laboratory experiments and in patients undergoing cardiac surgery that near-infrared spectroscopy (NIRS) signals provide an acceptable surrogate for monitoring changes in cerebral blood flow for autoregulation monitoring.1517 The latter approach might provide a method for individualizing MAP during CPB without the need for specialized equipment and without the limitations of transcranial Doppler monitoring (e.g., need for transtemporal insonating window, interference from electric cautery, movement artifact).

The hypothesis of this study was that real-time autoregulation monitoring using NIRS-based methods is more accurate for delineating the MAP at the LLA during CPB than empiric determinations based on age, preoperative history, and preoperative arterial blood pressure.1820

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METHODS

The prospective observational cohort study was approved by the IRB of the Johns Hopkins Medical Institutions, and written informed consent was provided by all patients. Patients undergoing cardiac surgery with CPB were enrolled from December 8, 2008, to October 4, 2010. Patients in the current study included some patients previously enrolled in a prospective study evaluating the accuracy of NIRS-based cerebral blood flow autoregulation monitoring during CPB.16,21

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Intraoperative Care

Patient care during surgery has been reported and included direct radial artery blood pressure and nasal temperature monitoring.16,21The patients received midazolam, fentanyl, and isoflurane for anesthesia and pancuronium for skeletal muscle relaxation. Nonpulsatile CPB flow between 2.0 and 2.4 L/min/m2 was used with a membrane oxygenator and a 27-μm arterial line filter. Alpha-stat pH management was used. Isoflurane concentrations during CPB were kept between 0.5% and 1.0% on a vaporizer connected to the oxygenator inflow. Hemoglobin level and arterial blood gases were measured after tracheal intubation, 10 minutes after initiation of CPB, and then hourly. Gas flow to the oxygenator during CPB was manipulated to maintain normocarbia based on continuous in-line arterial blood gas monitoring or arterial PaCO2 results. During CPB, blood pressure targets, transfusion of packed red blood cells, and the rate of rewarming were based on standard clinical practice.

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Autoregulation Monitoring

The patients were attached to NIRS monitors (INVOS, Somanetics, Inc., Boulder, CO) via self-adhesive sensors placed on the right and left forehead. Transcranial Doppler monitoring of the right and left middle cerebral artery (Doppler Box, DWL, Compumedics, Charlotte, NC) was performed using 2 2.5-MHz transducers fitted on a headband. Our signal acquisition and analysis methods have been described.16,21 Digitized arterial blood pressure, transcranial Doppler, and NIRS signals were processed with a personal computer using ICM+ software (University of Cambridge, Cambridge, UK). Filtering of the arterial blood pressure, Doppler, and NIRS signals was performed to limit analysis to the frequency of slow vasogenic waves (0.05 Hz to 0.003 Hz), which are relevant to autoregulation.22,23 Exclusion of wave components outside this bandwidth eliminates confounding inputs from the analysis. Specifically, low-pass filtering eliminates respiratory and pulse frequencies, which cause false positive readings of passivity; high-pass filtering eliminates drifts associated with hemodilution at the onset of bypass, blood transfusions, cooling, rewarming, etc. These drifts are not vasogenic in nature, and confound the interpretation that passivity is loss of autoregulation. Low-pass filtering was accomplished with nonoverlapping 10-second time-integrated mean values. High-pass filtering was performed with a detrending cutoff set at 0.003 Hz to remove slow, nonvasogenic drifts.

Next, a continuous, moving Pearson correlation coefficient between MAP and cerebral blood flow velocity and NIRS signals was performed, generating the variables' mean velocity index and cerebral oximetry, respectively. As previously described, consecutive, paired, 10-second averaged values from 300 seconds duration were used for each calculation, incorporating 30 data points for each index.16,21 Blood pressure in the autoregulation range is indicated by a mean velocity index value that approaches zero, while a mean velocity index value approaching +1 indicates dysregulated cerebral blood flow (i.e., flow and MAP not correlated or correlated, respectively). Similarly, values for cerebral oximetry index approaching 1 and zero indicate dysregulated and autoregulated cerebral blood flow, respectively. Clinicians in the operating room had access to the raw NIRS data for clinical management, but they were blinded to the autoregulation monitoring results.

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Data Analysis

Right and left transcranial Doppler recordings and NIRS data were used for analysis unless only unilateral recordings were available. To assess the LLA, we categorized values for mean velocity index in 5-mm Hg bins of MAP for each patient. The mean velocity index cutoff indicated that the LLA is not clearly known, but it is likely to be between 0.25 and 0.5.14,21,24 For the purpose of this study we defined the LLA as the MAP at which mean velocity index incrementally increased from <0.4 to ≥0.4. When mean velocity index was ≥0.4 at all MAP during CPB, the autoregulation threshold was defined as that MAP at which mean velocity index had the lowest value. Baseline blood pressure was defined as that measured during the preoperative evaluation that was performed either the day before surgery or the morning of surgery.

Multiple linear regression was used to estimate the effect of demographic variables (age, gender), medical history (diabetes, hypertension, and prior cerebrovascular accident), preoperative blood pressure (MAP, systolic blood pressure, pulse pressure), and time-averaged cerebral oximetry index characteristics on MAP at the LLA. Relationships between continuous predictors and MAP at the LLA were examined using scatter plots with nonparametric smoothers. Linear splines with relevant knots were used in the models to incorporate deviations from linear relationships. Model fit and homoscedasticity were evaluated by plots of residuals against predicted values. Plots of residuals against quintiles of normal distribution were used to assess normality of residuals. Undue influence on model fit was assessed by Cook's D statistic. In the sensitivity analyses, the points with high influence were excluded. The differential effect of hemodynamic indices by age and gender was checked by including interaction terms and testing their significance at the 0.05 level. Because of the relatively low proportion of missing values and the hypothesized missing data mechanism, complete case analysis was used under the assumption of missing completely at random.25 A receiver operator characteristic (ROC) curve was constructed comparing 10% decrements in MAP from the baseline measurements for identifying the LLA during CPB. Statistical analysis was performed with Stata software version 11 (StataCorp LP, College Station, TX).

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RESULTS

The demographic and operative data for the 232 enrolled patients are listed in Table 1. The average age of the patients was 66 ± 12 years. Thirty-nine percent of patients were >70 years of age, and 8% were older than 80 years. The patients had a high frequency of comorbid conditions, including a history of stroke in 8.6% of patients. Carotid artery ultrasound was obtained before surgery on the basis of clinical indications in 121 patients, revealing a high prevalence of carotid artery atherosclerosis. A clear autoregulation threshold was not observed in 7 patients. This might indicate that MAP was above our definition of the LLA throughout CPB. Physiologic variables during CPB are listed in Table 2. The MAP at the LLA was 66 mm Hg (95% prediction interval, 43 to 90 mm Hg) for the 225 patients in which this limit was observed. The distribution of MAPs at the LLA is shown in Figure 1.

Table 1

Table 1

Table 2

Table 2

Figure 1

Figure 1

The results of the linear regression models are listed in Table 3. For some of the adjusted models deviations from homoscedasticity were revealed. Therefore, the models were rerun using robust variance estimates (Huber–White estimator of variance).26,27 Because the results and conclusions were not altered, we present the results from the regression with model-based standard errors. We did not find major deviations from normality or highly influential observations. There was no relationship between preoperative MAP and the MAP at the LLA (P = 0.829) after adjusting for age, gender, prior stroke, diabetes, and hypertension. The model predicts that there is nonlinear relationship between cerebral oximetry index and average MAP at the LLA after adjusting for age, gender, prior stroke, diabetes, and hypertension. When <0.5, there is no linear relationship between cerebral oximetry index and MAP at the LLA. However, for values of cerebral oximetry index ≥0.5, the adjusted model estimates that patients with 0.1 higher cerebral oximetry index have 3.6 mm Hg lower average MAP at the LLA threshold (95% confidence interval [CI]: 0.5 to 6.7 mm Hg lower, P value = 0.022). There was a nonlinear effect of preoperative systolic blood pressure on MAP at the LLA. For patients whose preoperative systolic blood pressure is ≤160 mm Hg, the model estimates that those who have 5 mm Hg higher preoperative systolic blood pressure have 0.6 mm Hg higher MAP at the LLA (95% CI: from 0.01 to 1.1 mm Hg, P value = 0.046) after adjustment for age, gender, and comorbidities. For patients whose preoperative systolic blood pressure is >160 mm Hg, there was a trend for higher preoperative systolic blood pressure to be associated with lower MAP at the LLA (P = 0.118). Diabetes, hypertension, and prior cerebrovascular accident were not associated with MAP at the LLA.

Table 3

Table 3

The MAP at the LLA for men and women with various conditions is shown in Figure 2. Our results suggest that men might have higher MAP at the LLA than women (P = 0.068). The MAP at the LLA was not different between patients with or without hypertension, diabetes, or prior stroke regardless of age or gender. There was no difference in MAP at the LLA for patients with a pulse pressure ≥70 mm Hg (n = 73, 31.5% of patients) in comparison with the reference groups with pulse pressure <50 mm Hg (71.0 mm Hg vs 67.3 mm Hg, P = 0.113).

Figure 2

Figure 2

The sensitivity, specificity, and area under the ROC curve for various cutoffs of MAP from preoperative baseline measurement for predicting the LLA are shown in Table 4. A 15% decrement in MAP from baseline had a 91% sensitivity for detecting the MAP at the LLA, but the specificity was low (37%). The area under the ROC curve for this model was low (0.7354). In contrast, 140 (60.3%), 196 (84.5%), and 219 (94.4%) of the 232 patients had an LLA determined with cerebral oximetry index within 5 mm Hg, 10 mm Hg, and 15 mm Hg of that determined by mean velocity index, respectively. The mean difference in the LLA for mean velocity index versus cerebral oximetry index was −0.2 ± 10.2 mm Hg (95% CI, −1.5 to 1.2 mm Hg). Cerebral oximetry index detected an LLA in all but 5 patients using our criteria. In these patients an LLA would have been present with a cerebral oximetry index threshold ≤0.3. In 7 patients, mean velocity index remained <0.4 with declining MAP, and thus a clear autoregulation threshold was not observed. In these patients an LLA was detected with our cerebral oximetry index threshold.

Table 4

Table 4

Thirteen patients suffered a perioperative stroke. The LLA for patients with stroke (74 ± 15 mm Hg, 95% confidence intervals: 63 to 84 mm Hg) tended to be higher than for patients not suffering a stroke (66 ± 12 mm Hg, 95% confidence intervals: 65 to 68 mm Hg, P = 0.054). There was no difference in the percentage of time spent below the LLA during CPB for patients with and without stroke (mean difference −4.6%, 95% CI: −23.3% to 14%, P = 0.359). In comparison with patients without a stroke, those with a stroke were more likely to have diabetes (P = 0.029), have a history of stroke (P = 0.005), and have longer duration of CPB (P < 0.0001) and aortic cross-clamping (P = 0.0284).

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DISCUSSION

In a cohort of mostly elderly and high-risk patients we found that the average MAP at the LLA during CPB was 66 mm Hg. There was much variability in the LLA, though, with a 95% prediction interval between 43 and 90 mm Hg. We further found that predicting the MAP at the LLA during CPB based on clinical history and preoperative arterial blood pressure was imprecise. In contrast to clinical predictors, we found that the NIRS-based cerebral oximetry index was significantly associated with the MAP at the LLA. Women tended to have a lower MAP at the LLA than did men, whereas patients with a stroke tended to have a higher MAP at the autoregulation threshold than did those without stroke.

The current understanding of cerebral blood flow autoregulation in patients during CPB is mostly based on data derived using 133xenon washout or N2O dilution methods.1,2,4,5,28,29 These studies were often limited to pooled data or to a limited number of discrete measurements made when CPB flow was maintained and MAP manipulated with vasoactive drugs. On the basis of these data, a basic tenet of patient management during CPB has been that a MAP as low as 20 mm Hg to 55 mm Hg may be tolerated because autoregulation is intact and CPB flow is relatively constant.1,2,4,5,28,29 However, this practice was challenged by Gold et al.,6 who reported that targeting a MAP of 80 to 100 mm Hg during CPB was associated with a lower combined frequency of stroke and myocardial outcomes than when MAP was targeted at 50 to 60 mm Hg. The external validity of these results was questioned because of the small sample size (n = 248) and an unexpectedly high rate of stroke in the control group (7.2%).7 Nonetheless, there is currently little evidence to guide clinicians on the most appropriate MAP targets during CPB. This may have implications for current practices that include increasing proportions of elderly patients with cerebrovascular disease.10,11

Continuous cerebral blood flow autoregulation monitoring as used in this study provides an individual estimate of autoregulation based on fluctuations in MAP that occur during the course of surgery. This approach allows perhaps for a more precise determination of the LLA than discrete and intermittent measurements. The continuous nature of the measurement is important as cerebral blood flow autoregulation is dynamic and potentially influenced by many perioperative perturbations, including rewarming from hypothermia, volatile anesthetics (dose dependently), and anemia.30,31 Our observations suggest that predicting the exact MAP target during CPB to remain above the LLA is difficult on the basis of clinical history and preoperative blood pressure measurement. Although there was a positive relationship between the MAP at the LLA and systolic blood pressure (i.e., higher MAP at the LLA with increasing systolic blood pressure), this association was limited to patients with systolic blood pressure ≤160 mm Hg, and the association was less robust compared with cerebral oximetry index ≥0.5 (Table 3). The sensitivity of predicting the MAP at the LLA based on preoperative MAP had wide confidence intervals and low specificity (Table 4). Furthermore, the low area under the ROC curve suggests that factors other than preoperative MAP influence the ability to predict this end-point. Ultimately, the exact tolerance of error in predicting MAP during CPB will depend on the patient population. Our observations suggest that a MAP in the usual clinical range of 50 to 70 mm Hg during CPB might result in cerebral blood flow being pressure passive in some patients predisposing to cerebral hypoperfusion.10,11 At the same time, maintaining empirically high MAP targets may unnecessarily expose some patients with a low LLA to higher cerebral blood flow, potentially increasing cerebral embolic load and predisposing to cerebral edema.5,32

Transcranial Doppler monitoring during cardiac surgery is associated with many known limitations, including the need to frequently readjust the transducer and interference from electric cautery. These limitations are particularly germane before CPB when continuous monitoring is difficult during harvest of the internal mammary artery when electrocautery use and patient repositioning are frequent. For this reason, we do not provide estimates of the LLA before CPB for comparing transcranial Doppler results with NIRS data. In contrast, NIRS output, and hence autoregulation monitoring, is continuous throughout surgery, and it is not susceptible to the same limitations of transcranial Doppler. Thus, in clinical practice, MAP targets derived from NIRS would be available before CPB and even after surgery when patient movement may limit transcranial Doppler monitoring.

We did not find a difference in the MAP at the LLA for patients with or without diabetes, hypertension, or prior stroke. These conditions have been suggested to result in a rightward shifted LLA.1820 Our comparison group was not a normal control group, but rather included patients with cardiovascular disease and its associated endothelial dysfunction that results in abnormalities in microcirculatory process maintaining cerebral blood flow autoregulation.33 We did observe that patients suffering stroke had a higher MAP at the LLA than did patients without a stroke (74 ± 15 mm Hg versus 66 ± 12 mm Hg, P = 0.054). These findings are tempered, though, by the small number of patients in our study, which does not allow for risk adjustments. Our use of arterial blood pressure measurement from the preoperative evaluation might not represent a true baseline measurement. This measurement, however, is what clinicians usually evaluate when planning perioperative care. Thus, our methods represent a pertinent clinical practice situation.

In our study we used a time-domain approach for cerebral blood flow monitoring that assumes that changes in transcranial Doppler-measured blood flow velocity over short periods of time result from changes in MAP. This method does not require assumptions of stationarity as with frequency domain methods (e.g., based on phase shifts, transfer functions) of cerebral blood flow autoregulation assessments that are not consistently present during surgery and in critical care settings.14 The signal-to-noise ratio with our approach, however, is less than with other cerebral blood flow autoregulation testing methods because the output (cerebral blood flow velocity) and input (MAP) contain both noise and autoregulation information. Time averaging of the data and focusing on slow wave fluctuations in cerebral blood flow velocity (0.003 to 0.04 Hz) are used to improve the signal-to-noise ratio. Changes in cerebral blood flow velocity in this frequency range are believed to represent autoregulatory compensatons to slow hemodynamic oscillations.3436

Our results support our prior findings showing that NIRS can be exploited for autoregulation monitoring.16,24,37,38 In a piglet laboratory model we found that cerebral oximetry index was significantly correlated with cerebral blood flow autoregulation monitoring based on laser Doppler methods.24 In that study, and in an investigation of neurosurgical patients, NIRS waveforms at frequencies lower than 0.04 Hz had high coherence with laser Doppler or transcranial Doppler-measured cerebral blood flow velocity, respectively.24,35 We have found significant coherence between slow waves of cerebral blood flow velocity and NIRS in patients during CPB.16 These data together suggest that cerebral blood flow autoregulation monitoring is possible in patients during CPB using NIRS. Basing MAP targets during CPB on real-time cerebral blood flow autoregulation data, compared with the current standard of care of empirically derived targets, might ensure adequate cerebral blood flow during surgery and lead to improved neurological outcomes.

As mentioned, mean velocity index indicating the limits of autoregulation is likely to be between 0.25 and 0.5.14,21,24,39 We acknowledge that the use of a mean velocity index of ≥0.4 for defining the LLA is somewhat arbitrary. This is in part due to the nature of autoregulation whereby vasoreactivity responsible for mediating this response continues until extremely low blood pressure.14 That is, a discrete threshold at which cerebral blood flow becomes completely blood pressure–passive is unlikely. Thus, the correlation between cerebral blood flow and MAP does not precipitously increase to values closer to 1 at some MAP. Rather, mean velocity index incrementally increases with declining MAP. Thus, our approach may have resulted in some error in depicting an exact MAP at the LLA if this occurred at a lower or higher mean velocity index in some patients. Regardless, the value of mean velocity index we chose was associated with delirium in patients with sepsis.35 The effects of nonpulsatile CPB flow on cerebral autoregulation are not known. Studies in animals did not show a convincing effect of nonpulsatile CPB on global cerebral blood flow or regional oxygen saturation.40 Further, the issue of nonpulsatile versus pulsatile CPB perfusion would likely affect autoregulation determinations by either the continuous methods we used or the intermittent measurements used with 133xenon washout or the Kety–Smith method. Regardless, arterial blood pressure is rarely truly “nonpulsatile” during CPB, because of small variations in blood pressure from the nonocclusive roller pump variation of flow. Finally, our methods use averaged blood pressure and cerebral blood flow velocity in the calculations that would reduce the influence of nonpulsatile CPB.

In conclusion, there is a wide range of MAP at the LLA in patients during CPB making estimating this target difficult. Real-time monitoring of autoregulation using cerebral oximetry index may provide a more rationale means for individualizing MAP during CPB.

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DISCLOSURES

Name: Brijen Joshi, MD.

Contribution: This author helped conduct the study, analyze the data, and write the manuscript.

Attestation: Brijen Joshi has seen the original study data, reviewed the analysis of the data, approved the final manuscript, and is the author responsible for archiving the study files.

Conflicts: Brijen Joshi reported no conflict of interest.

Name: Masahiro Ono, MD.

Contribution: This author helped design the study, conduct the study, analyze the data, and write the manuscript.

Attestation: Masahiro Ono has seen the original study data, reviewed the analysis of the data, approved the final manuscript, and is the author responsible for archiving the study files.

Conflicts: Masahiro Ono reported no conflict of interest.

Name: Charles Brown, MD.

Contribution: This author helped conduct the study, analyze the data, and write the manuscript.

Attestation: Charles Brown has seen the original study data, reviewed the analysis of the data, approved the final manuscript, and is the author responsible for archiving the study files.

Conflicts: Charles Brown reported no conflict of interest.

Name: Kenneth Brady, MD.

Contribution: This author helped design the study, analyze the data, and write the manuscript.

Attestation: Kenneth Brady has seen the original study data, reviewed the analysis of the data, approved the final manuscript, and is the author responsible for archiving the study files.

Conflicts: Kenneth Brady consulted for Somanetics, received royalties from Somanetics, and received research funding from Somanetics. Ken Brady has consulted for Somanetics, Inc., in a relationship that was managed by the committee for outside interests at the Johns Hopkins University School of Medicine.

Name: R. Blaine Easley, MD.

Contribution: This author helped design the study, analyze the data, and write the manuscript.

Attestation: R. Blaine Easley has seen the original study data, reviewed the analysis of the data, approved the final manuscript, and is the author responsible for archiving the study files.

Conflicts: R. Blaine Easley reported no conflict of interest.

Name: GayaneYenokyan, PhD.

Contribution: This author helped design the study, conduct the study, analyze the data, and write the manuscript.

Attestation: GayaneYenokyan has seen the original study data, reviewed the analysis of the data, approved the final manuscript, and is the author responsible for archiving the study files.

Conflicts: GayaneYenokyan reported no conflict of interest.

Name: Rebecca F. Gottesman, MD, PhD.

Contribution: This author helped design the study, analyze the data, and write the manuscript.

Attestation: Rebecca F. Gottesman reviewed the analysis of the data, approved the final manuscript, and is the author responsible for archiving the study files.

Conflicts: Rebecca F. Gottesman reported no conflict of interest.

Name: Charles W. Hogue, MD.

Contribution: This author helped design the study, conduct the study, analyze the data, and write the manuscript.

Attestation: Charles W. Hogue has seen the original study data, reviewed the analysis of the data, approved the final manuscript, and is the author responsible for archiving the study files.

Conflicts: Charles W. Hogue received research funding from Somanetics and consulted for Ornim.

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Recuse Note

Charles W. Hogue is Associate Editor-in-Chief for Cardiovascular Anesthesiology for Anesthesia & Analgesia. This manuscript was handled by Steve Shafer, Editor-in-Chief, and Dr. Hogue was not involved in any way with the editorial process or decision.

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