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Original Research Articles: Original Clinical Research Report

A Processed Electroencephalogram–Based Brain Anesthetic Resistance Index Is Associated With Postoperative Delirium in Older Adults: A Dual Center Study

Cooter Wright, Mary MS*; Bunning, Thomas MD*; Eleswarpu, Sarada S. MD*; Heflin, Mitchell T. MD, MHS; McDonald, Shelley R. DO, PhD; Lagoo-Deenadalayan, Sandhya MD, PhD; Whitson, Heather E. MD†,§; Martinez-Camblor, Pablo PhD; Deiner, Stacie G. MS, MD; Berger, Miles MD, PhD*,§,#

Author Information
doi: 10.1213/ANE.0000000000005660

Abstract

KEY POINTS

  • Question: Is a processed electroencephalogram (EEG)–based brain anesthetic resistance index associated with postoperative delirium risk in older surgical patients?
  • Findings: An intraoperative processed EEG–based measure of lower brain anesthetic resistance (ie, Duke Anesthesia Resistance Scale [DARS] <28.755) was independently associated with postoperative delirium risk in a combined patient cohort from 2 different institutions.
  • Meaning: A processed EEG–based measure of brain anesthetic resistance could potentially be used to identify older adults at increased risk for postoperative delirium

Postoperative delirium is a common complication in older surgical patients and has been associated with increased length of stay, functional decline, and 1-year postoperative mortality risk.1–3 Recent guidelines call for or at least encourage4,5 electroencephalography (EEG)-based anesthetic management to reduce delirium rates. In research settings, intraoperative anesthetized raw EEG features such as burst suppression and alpha band power have been associated with postoperative delirium6–9 and preoperative cognitive impairment,10,11 respectively.

However, processed EEG measures are used more frequently than raw EEG for intraoperative brain monitoring in current anesthesiology practice, and there is equipoise regarding whether processed EEG measures are associated with postoperative delirium. Processed EEG values measure the brain’s response to anesthetic drug dosage (as well as surgical stress), although the anesthetic dose required to produce specific processed EEG values or raw EEG states varies across patients. Further, these individual differences in anesthetic dose–dependent EEG responses are not completely explained by age, gender, or weight. Fritz et al12 demonstrated that increased anesthetic sensitivity (as indicated by EEG burst suppression at lower anesthetic dosage) is associated with increased postoperative delirium risk. On the other hand, anesthetic resistance is the concept that some individuals may require higher or lower anesthetic dosage to produce the EEG patterns seen at typical anesthetic doses in most patients.

Here we studied an anesthetic resistance index based on processed EEG (ie, bispectral index [BIS]) values and age-adjusted end-tidal volatile anesthetic concentrations. Then we tested the hypothesis that low values of this brain anesthetic resistance index would be associated with increased postoperative delirium risk in older surgical patients. In essence, we theorized that lower BIS values in response to relatively lower anesthetic doses would serve as a marker of decreased neurophysiologic resistance of the brain to the sedative/hypnotic effects of gamma amino butyric acid (GABA)-ergic anesthetic drugs, similar to the way that significant sedation in response to small amounts of alcohol is commonly viewed as a marker of lower alcohol “tolerance.” We theorized that just as reduced alcohol tolerance can serve as a marker of decreased neurocognitive function,13 decreased anesthetic brain resistance would be associated with a perioperative neurocognitive disorder (eg, postoperative delirium).

METHODS

Patient Population

All Duke Perioperative Optimization of Senior Health clinic14 patients seen from June 24, 2013 to September 25, 2015 were screened for inclusion into this study (N = 278, see Supplemental Digital Content, Methods, https://links.lww.com/AA/D596, for additional details). The Duke University Medical Center Institutional Review Board (IRB) approved this study and waived the informed consent requirement. These data were combined with data prospectively collected from patients enrolled in an observational cohort study approved by the Mt Sinai Medical Center IRB and registered with clinicaltrials.gov (NCT02650687). All Mt Sinai patients provided informed consent before participation and were enrolled between November 2015 and 2018. The Mt Sinai observational cohort study was primarily focused on postoperative cognitive dysfunction, although it also obtained postoperative delirium data. The Mount Sinai IRB waived the requirement for patients in this study to provide additional informed consent for the inclusion of their deidentified data in this article. We obtained intraoperative data including BIS values, intraoperative end-tidal anesthetic concentrations (ETAC), and additional baseline medication information for both Duke and Mt Sinai patients. Data were saved directly from operating room monitors onto secure servers at both institutions.

Duke and Mt Sinai patients were included in this study if they had surgery of >1 hour duration and had end-tidal anesthetic gas values and BIS data available for more than 50% of the case minutes. To exclude total intravenous anesthetic cases, we excluded any case in which the patient received >500 mg/h of propofol. Anesthetic case length was defined by the case start and end times documented by the anesthesia provider.

Intraoperative Anesthetic Dosage

ETAC was recorded continuously from 5 minutes after incision until 5 minutes before the end of surgery, to capture the anesthetic “plateau phase” of the case.15 Using a previously described method16 to avoid data artifacts, the end-tidal minimum alveolar concentration (MAC) fraction was recorded once per minute, and the median value over each 5-minute case epoch was obtained. The mean of these median values was then calculated to determine the overall end-tidal MAC fraction. Next, we used MAC values at age 40 from our recent meta-regression analysis of age-related changes in MAC in published studies17 to calculate the age-adjusted end-tidal MAC fraction (aaMAC), again using the mean of median values obtained from each 5-minute case epoch.

aaMAC=ETAG¯MAC40×10.00301(patientage40)

MAC-hours was defined as the product of the case duration (in hours) and the aaMAC value from Equation (1).

BIS Value

BIS (Covidien) processed EEG values from electrodes placed on the left forehead were utilized for all cases at both institutions. The Duke operating rooms utilized 2-channel unilateral BIS electrodes connected via an E-BIS module to display the BIS index and raw waveform on the anesthesia GE (General Electric) monitors. The Mt Sinai cases utilized BIS Vista monitors (Covidien).

At both sites, the BIS proprietary algorithm transformed raw EEG data to a number from 0 to 100, with >90 indicating an awake state, <60 indicating unconsciousness and general anesthesia, and <40 indicating deep sedation. BIS values were obtained from 5 minutes after incision until 5 minutes before the “end of surgery” time stamp, again to target the anesthetic “plateau phase.” BIS was recorded once per minute, and the median value from each 5-minute case epoch was used to calculate a case average, as described.15

Inhaled Anesthetic Resistance Measurement

To gauge the appropriateness of BIS values for a given aaMAC dose, and to measure the degree of BIS decrease for a given aaMAC dose, we developed the Duke Anesthesia Resistance Scale (DARS), defined as

DARS= 12.5aaMac BIS

Here, BIS = mean of the median BIS readings during the case. The constant term of 2.5 represents the highest aaMAC value given in over 17,000 cases performed at a single academic center over a roughly 2-year period,15 and approximates the highest aaMAC value used in typical adult anesthesiology practice. The DARS denominator thus measures the difference between the highest volatile anesthetic dose possible in clinical practice and the actual dose received by a given patient. A high DARS value could thus result from a high BIS reading and/or a large aaMAC, and vice versa.

Delirium Evaluation and Diagnosis

All Duke patients in this study were monitored daily after their index surgery by fellowship-trained geriatricians,14 all of whom underwent detailed training on the standard Diagnostic and Statistical Manual ofMental Disorders, Fifth Edition (DSM-V) clinical criteria for diagnosing delirium during their geriatrics fellowships. These attending geriatricians closely examined patients for delirium and then coded it (if present) with one of the following International Classification of Diseases, Ninth Edition (ICD9) codes in the patient’s chart: 290.11, 290.3, 290.41, 291.0, 292.81, 293.0, 293.1, 298.2, 348.3, 348.31, 348.39, 349.82, 437.2, 572.2, 768.7, 768.71, 768.72, 768.73, 780.09, or 780.97.13 Duke patients were defined as having postoperative delirium if any of these ICD9 codes was present in their patient record at any point during their postoperative index hospitalization. No interrater delirium reliability assessments were conducted between attending geriatricians in the Duke cohort, as attending-level geriatrician or psychiatrist assessments are already considered the “gold standard” for the evaluation of delirium.18

For Mt Sinai patients, delirium assessments were performed twice daily by research staff using the Confusion Assessment Method for the Intensive Care Unit (CAM-ICU) instrument. Delirium assessment training for Mt Sinai study staff was performed by the neuropsychiatry team; interrater reliability for Mt Sinai staff delirium assessments was not performed because the CAM-ICU questions have very little subjectivity.19 Mt Sinai patients were considered to have postoperative delirium if they had a positive delirium assessment at any point during hospitalization after their index surgery.

At both Duke and Mt Sinai, to avoid potential bias in delirium assessments, individuals performing the delirium assessments were blinded (ie, not given access) to intraoperative DARS scores.

Anticholinergic Cognitive Burden Score

The anticholinergic cognitive burden (ACB) assigns prescribed medications with known anticholinergic activity a score from 0 to 3 based on degree of predicted cognitive impairment in older adults, in line with a multidisciplinary consensus opinion validated to predict adverse outcomes such as delirium.20,21

Statistical Analysis

Analysis was conducted on the combined patient cohort (ie, from Duke and Mt Sinai). Categorical and numeric patient characteristics were summarized and compared between patients with versus without delirium using Pearson χ2, t tests, Wilcoxon rank sum, or Fisher exact test as appropriate. Normality was assessed via Shapiro-Wilks tests; nonparametric statistics were used when evidence of nonnormality was found.

We compared DARS values between patients with and without delirium via a Wilcoxon ranked sum test and using restricted cubic splines in univariable logistic regression. We found evidence of a threshold effect for DARS in the log-odds results for delirium; thus, we used the Youden index22 to identify the cut point for DARS that maximizes the sensitivity and specificity associated with delirium risk. We determined the univariable association of the dichotomized “low” DARS variable with delirium using χ2 test and odds ratio (OR) estimation from logistic regression, controlling for institutional site. Subsequently we performed multivariable logistic regression for delirium based on low DARS while adjusting for potential confounding from a priori known delirium risk factors, for example, age, procedure duration, and anticholinergic burden score, as well as institutional site and intraoperative medications administered (opioids, midazolam, propofol, ketamine, phenylephrine, dexmedetomidine, and nitrous oxide use). Because our observed delirium incidence was low, we used Firths penalized likelihood in our multivariable logistic regression analysis to control for multiple potential confounding factors. Finally, to generate risk ratios (RRs), we also analyzed the relationship between low DARS and delirium via log-linear binomial regression.

This was an exploratory study designed to develop a composite processed EEG–based measure of brain resistance to volatile anesthetics and to evaluate the relationship between this measure and postoperative delirium. At the time the project was conceived, there was no prior literature describing either a processed EEG measure of brain anesthetic resistance, or the relationship between such a measure and postoperative delirium risk. Nonetheless, we reasoned that this study would likely have sufficient power, as prior studies relating other EEG metrics to other neurocognitive variables have often had slightly smaller sample sizes10,23 than the cohort studied here. A power analysis demonstrates that a sample size of 139 patients with a 25% delirium incidence and a 30% incidence of low DARS scores would provide 80% power with α = .05 to detect an OR of ≥3.0 for an association between low DARS and postoperative delirium risk.

All statistical analyses were performed using SAS v 9.4 (SAS Inc), or R v 3.6.1 (R Foundation) with the rms, OptimalCutpoints, and boot packages, and P < .05 was considered statistically significant.

RESULTS

We identified 69 Duke patients and 70 Mt Sinai patients who met our inclusion criteria (Figure 1). Baseline and intraoperative characteristics for patients with versus without delirium are shown in Tables 1 and 2, respectively. Postoperative delirium occurred in 35 of the 139 patients, that is, in ~25% of the patients (Table 1). Patients with versus without postoperative delirium were generally similar (Tables 1, 2). Neither aaMAC (median [Q1, Q3], 0.86 [0.76, 0.98] vs 0.96 [0.80, 1.17]; P = .074) nor BIS values (mean [standard deviation {SD}], 46.72 [8.89] vs 48.88 [8.77]; P = .212) differed significantly among patients with versus without postoperative delirium. DARS scores were significantly lower in patients with versus without delirium (median of 27.92 [24.85, 36.74] vs 32.88 [28.95, 38.15]; P = .015; Table 2); see Methods for the DARS equation. Patients who developed postoperative delirium also received higher intraoperative phenylephrine dosage than those who did not develop delirium (1.74 [0.63, 5.32] vs 0.75 [0.25, 3.33] mg; P = .038).

Table 1. - Cohort Baseline Characteristics by Delirium Status
Characteristic Overall (N = 139) No delirium (n = 104) Delirium (n = 35) P
Site .071a
 Duke 69 (49.6%) 47 (45.2%) 22 (62.9%)
 Mt Sinai 70 (50.4%) 57 (54.8%) 13 (37.1%)
Age 73 (6) 73 (6) 74 (7) .179b
Gender (male) 60 (43.2%) 46 (44.2%) 14 (40.0%)
BMI (kg/m2) 29.1 (7.7) 29.0 (7.7) 29.3 (8.1) .837b
ACB scorec 0 [0, 1] 0 [0, 1] 0 [0, 2] .479d
ASA physical status .661d
 II 28 (20.1%) 23 (22.1%) 5 (14.3%)
 III 106 (76.3%) 76 (73.1%) 30 (85.7%)
 IV 5 (3.6%) 5 (4.8%) 0 (0.0%)
Surgery category .124e
 General 59 (42.4%) 48 (46.2%) 11 (31.4%)
 Orthopedic 50 (36.0%) 32 (30.8%) 18 (51.4%)
 Thoracic 16 (11.5%) 14 (13.5%) 2 (5.7%)
 Urologic 14 (10.1%) 10 (9.6%) 4 (11.4%)
Factors are summarized as count (%) for categorical variables and mean (SD) or median [Q1, Q3] for numeric variables.
Abbreviations: ACB, anticholinergic cognitive burden; ASA, American Society of Anesthesiologists; BMI, body mass index; SD, standard deviation.
aχ2 test.
bt test.
cACB score not available for 2 patients; 1 with delirium and 1 without.
dWilcoxon rank sum test.
eFisher exact test.

Table 2. - Cohort Intraoperative Measures by Delirium Status
Overall (N = 139) No delirium (n = 104) Delirium (n = 35) P
Procedure length (min) 151 [110, 213] 144 [110, 190] 177 [110, 244] .099a
Fentanyl or hydromorphone used/dose (ME) 135 (97.1%)/35 [25, 49] 102 (98.1%)/35 [25, 50] 33 (94.3%)/33.3 [25, 45] .448a
Midazolam used/dose (mg) 41 (29.5%)/2 [1, 2] 28 (26.9%)/2 [2, 2] 13 (37.1%)/1 [1, 2] .561a
Propofol used/dose (mg) 85 (61.2%)/130 [100, 190] 63 (60.6%)/120 [100, 200] 22 (62.9%)/142 [120, 190] .448a
Ketamine used/dose (mg) 46 (33.1%)/71 [50, 100] 33 (31.7%)/61 [35, 87] 13 (37.1%)/85 [68, 106] .286a
Phenylephrine used/dose (mg) 103 (74.1%)/1.10 [0.30, 4.18] 75 (72.1%)/0.75 [0.25, 3.33] 28 (80.0%)/1.74 [0.63, 5.32] .038a
Dexmedetomidine used/dose (μg) 6 (%)/19 [16, 20] 4 (3.8%)/17 [12, 19] 2 (5.7%)/34 [20, 48] .605a
Nitrous oxide used 4 (2.9%) 4 (3.9%) 0 (0.0%) .572b
Primary gas used .064c
 Desflurane 42 (30.2%) 28 (26.9%) 14 (40.0%)
 Isoflurane 56 (40.3%) 40 (38.5%) 16 (45.7%)
 Sevoflurane 41 (29.5%) 36 (34.6%) 5 (14.3%)
Case average aaMAC 0.94 [0.79, 1.15] 0.96 [0.80, 1.17] 0.86 [0.76, 0.98] .074a
aaMAC hours 2.17 [1.53, 3.59] 2.11 [1.53, 3.52] 2.74 [1.46, 3.74] .508a
Case average BIS 48.33 (8.82) 48.88 (8.77) 46.72 (8.89) .212d
Case average BIS <45 50 (36.0%) 36 (34.6%) 14 (40.0%) .566c
DARS 31.76 [27.03, 37.94] 32.88 [28.95, 38.15] 27.92 [24.85, 36.74] .015a
Data are summarized as count (%) for categorical variables and mean (SD) or median [Q1, Q3] for numeric variables.
Abbreviations: aaMAC, age-adjusted end-tidal minimum alveolar concentration; BIS, bispectral index; DARS, Duke Anesthesia Resistance Scale; ME, morphine equivalents; SD, standard deviation.
aWilcoxon rank sum test.
bFisher exact test.
cχ2 test.
dt test.

F1
Figure 1.:
STROBE diagram for the analysis cohort formulation. BIS indicates bispectral index; POSH, Perioperative Optimization of Senior Health; STROBE, STrengthening the Reporting of OBservational studies in Epidemiology.

Lower DARS values were nonlinearly associated with increased delirium risk (Figure 2A), and more than 75% of all patients had DARS values <40 (see bar and whisker plot of DARS distribution across patients, bottom of Figure 2A). To determine an optimal threshold for defining a low DARS range associated with delirium, we used the Youden index. A threshold of 28.755 maximized the combined sensitivity and specificity of low DARS with postoperative delirium risk. To investigate the robustness of associations between low DARS status and delirium, we calculated a bootstrap 95% confidence interval (CI; 26.18–29.80) for this low DARS threshold (Figure 2A). The distribution of DARS scores in patients with versus without delirium is shown in Figure 2B.

F2
Figure 2.:
Low DARS and delirium. A, The relationship between DARS scores (x-axis) and delirium risk (y-axis) using a spline fit. The vertical straight line depicts the threshold for defining the low DARS range that maximizes the Youden index; the dashed vertical lines show the 95% bootstrap confidence lower and upper bounds for this threshold. The bar and whisker plot underneath (A) shows the DARS score distributions among all patients; the center line within the shaded region is the median DARS score; the edges of the shaded box represent the lower and upper quartile boundaries; and the whiskers represent 1.5 times the interquartile range, with points beyond that range shown as individual dots. B, The DARS score distributions among patients with versus without delirium, using the same format as in the bottom of (A). C–E, Delirium rates in patients with versus without a low DARS score, using the optimum bound (C), lower bound (D), or upper bound (E) threshold to define the low DARS range. Error bars in (C–E) represent the 95% confidence interval. *P < .05 from the logistic regression model controlling for site. DARS indicates Duke Anesthesia Resistance Scale.

Patients with versus without low DARS (as defined by a threshold of 28.755) generally had similar characteristics, except that patients with low DARS scores not surprisingly tended to receive lower case average aaMAC (mean [SD] 0.80 [0.17] vs 1.11 [0.36]; P < .001) and had lower case average BIS values (mean [SD] 42 [6] vs 51 [8]; P < .001; Supplemental Digital Content, Table 1, https://links.lww.com/AA/D596). In a logistic regression analysis, adjusting for site, low DARS status (ie, <28.755) was associated with a nearly 4-fold increase in the odds of delirium (Figure 2C; OR = 4.30, 95% CI, 1.89–10.01; P = .001). Similarly, logistic regression analysis, adjusting for site, using the lower or upper 95% confidence bound for the low DARS range also found a roughly 4-fold increased odds of delirium (Figure 2D, E, OR [95% CI] = 4.64 [1.92–11.39], P = .001, and 3.24 [1.46–7.40], P = .004, respectively).

In multivariable models controlling for institutional site, patient age, ACB score, procedure duration, gender, and ASA physical status, a low DARS was associated with increased odds of postoperative delirium, whether it was defined as <26.18 (OR [95% CI = 3.74 [1.52–9.32]; P = .005), <28.755 (OR [95% CI] = 3.79 [1.63–9.10]; P = .003), or <29.80 (OR [95% CI] = 3.16 [1.37–7.55]; P = .009). Finally, even after controlling for other intraoperative medications and institutional site, a low DARS was associated with increased odds of postoperative delirium, whether a low DARS was defined as <26.18 (OR [95% CI] = 4.57 [1.84–11.58]; P = .002), <28.755 (OR [95% CI] = 4.21 [1.80–10.16]; P = .002), or <29.80 (OR [95% CI] = 3.44 [1.47–8.34]; P = .006). These 9 different multiple variable models are summarized in Table 3 and Figure 3; full model details are provided in Supplemental Digital Content, Table 2, https://links.lww.com/AA/D596.

Table 3. - Summary of Associations Between Low DARS and Postoperative Delirium Risk Using the 3 Different Cut Point Locations for Low DARS, in 4 Different Multiple Variable Logistic Regression Models
Model Low DARS effect,
lower DARS confidence bound (26.18)
Low DARS effect,
optimum cut point (28.755)
Low DARS effect,
upper DARS confidence bound (29.80)
OR (95% CI) P value OR (95% CI) P value OR (95% CI) P value
1. DARS + site 4.64 (1.92–11.39) .001 4.30 (1.89–10.01) .001 3.24 (1.46–7.40) .004
2. DARS + site + risk factors 3.74 (1.52–9.32) .005 3.79 (1.63–9.10) .003 3.16 (1.37–7.55) .009
3. DARS + site + medications 4.57 (1.84–11.58) .002 4.21 (1.80–10.16) .002 3.44 (1.47–8.34) .006
4. DARS + site + risk factors + medications 3.75 (1.45–9.87) .009 3.64 (1.49–9.25) .007 3.12 (1.28–7.96) .017
Risk factors modeled include age, ACB score, procedure duration, gender, and ASA physical status. Medications modeled include opioids (in morphine equivalents), midazolam, propofol, ketamine, phenylephrine, dexmedetomidine, and nitrous oxide use.
Abbreviations: ACB, anticholinergic cognitive burden; ASA, American Society of Anesthesiologists; CI, confidence interval; DARS, Duke Anesthesia Resistance Scale; OR, odds ratio.

F3
Figure 3.:
Forest plot for log of the odds ratio (and 95% CI) results from 3 models at the 3 different thresholds/cut points for the low DARS range. CI indicates confidence interval; DARS, Duke Anesthesia Resistance Scale; OR, odds ratio.

Similarly, a log-linear model controlling for site showed a significant RR for postoperative delirium in patients with a low DARS (RR [95% CI] = 2.77 [1.55–4.96]; P = .006). This result remained significant whether the lower or upper confidence bound was used to define a low DARS (P < .001 and P = .006, respectively; Supplemental Digital Content, Table 3, https://links.lww.com/AA/D596).

DISCUSSION

We found that low values of a processed EEG measure of brain resistance to volatile anesthetics (ie, DARS) was independently associated with postoperative delirium risk. Compared to patients with a DARS of 28.755 or higher, those with a DARS <28.755 had roughly 4 times higher odds of developing postoperative delirium. Although neither BIS nor aaMAC alone was associated with postoperative delirium risk, a low DARS was associated with postoperative delirium risk. The DARS effectively scales the BIS score by the difference between the maximum aaMAC fraction likely used in clinical practice (2.5) minus the actual aaMAC fraction received by the patient. A lower DARS thus implies a less active brain than would be expected for a given inhaled anesthetic dose (ie, aaMAC fraction), that is, decreased brain resistance to anesthetic-induced decreases in brain activity. Because low DARS scores were associated with increased postoperative delirium risk, they could potentially be used clinically to direct scarce resources (such as geriatrics consultations) toward patients at relatively higher delirium risk.

These data complement the finding that increased anesthetic sensitivity (ie, burst suppression divided by a composite anesthetic dosage measure) is associated with postoperative delirium risk.12 BIS values <30 are linearly (and inversely) related to burst suppression ratio.24 Thus, the relationship identified here between lower DARS and increased delirium risk may partly reflect an association between increased burst suppression at lower inhaled anesthetic doses and increased postoperative delirium risk.12 Further, the fact that there is a linear relationship between burst suppression ratio and BIS index numbers only below a BIS index value of 30 may help explain why there is a nonlinear threshold relationship between the DARS and delirium risk. Indeed, the BIS number also shows noncontinuous threshold associations with other raw EEG parameters, such as spectral edge frequency 95%.25 Although the BIS produces numbers from 0 to 100, it was never intended to be used as a linear index of sedation state. Instead, the manufacturer has suggested that there is a threshold relationship between the BIS and intraoperative awareness with explicit recall, and recommends that clinicians maintain patients undergoing general anesthesia at a BIS value below 60 to reduce the risk of intraoperative awareness with explicit recall.26 Thus, given all of these nonlinear threshold relationships between the BIS numbers and both raw EEG parameters and clear clinical outcomes (such as awareness with explicit recall), it is unsurprising that there is also a nonlinear threshold relationship between an anesthetic resistance index based on BIS values (the DARS) and postoperative delirium risk.

Our data also demonstrate that a low DARS was associated with increased delirium risk even after controlling for other drugs that can affect the EEG (ie, opioids,27,28 ketamine,29 neuromuscular blockers,30 nitrous oxide,31 etc). This suggests that lower brain anesthetic resistance in response to volatile anesthetics is relatively greater in magnitude than the EEG effects of these other anesthetic drug adjuncts, at least at the doses in which they were used here. These results suggest that a low DARS identifies core neurologic features of a brain at risk for postoperative delirium independent of specific intraoperative anesthetic drug administration, at least in cases based primarily on volatile anesthetics.

Lower brain anesthetic resistance (ie, low DARS) was more closely associated with delirium risk than age, similar to the finding that neurophysiologic brain age is more closely related to delirium than is chronologic age,6 and consistent with the idea that the variance in organ function increases with age.32 There are age-dependent changes in EEG responses to inhaled anesthetics and propofol,33,34 yet chronological age can clearly be disassociated from biological age.35–37 In fact, markers of “brain age” are more closely associated with postoperative delirium risk than chronologic age, perhaps due to biological changes within the brain that occur at variable rates across people.6 Biological age has also been associated with increased inflammation,36,38 and inflammation increases anesthetic sensitivity in cultured neurons and whole animals.39 Thus, increased brain inflammation at baseline and/or in response to surgical stress40,41 could make the brain less resistant (or more sensitive) to anesthesia, resulting in lower DARS values. Future studies should examine the relationship between neuroinflammation, brain anesthetic resistance (ie, DARS values), and postoperative delirium.

This study has several limitations. First, we studied a brain anesthetic resistance (ie, DARS) measure based on case summary BIS and aaMAC data. It is unclear whether brain anesthetic resistance measures based on shorter epochs would be similarly associated with postoperative delirium risk; this is an important topic for larger, future studies.

Second, the cohort studied here is of moderate size. We performed bootstrapping to define the 95% confidence bounds for a low DARS threshold for delirium risk to determine if the associations were robust to a potentially cohort-specific cut point, and found consistent relationships between low DARS and delirium across the bootstrap interval. However, given the modest number of total delirium events and incidence of DARS <27 in our cohort, future prospective studies with sufficient size are warranted, both to provide a more precise point estimate for the OR for delirium among patients with a low DARS threshold and to more precisely define this low DARS threshold.

Third, the relationship between DARS and delirium observed here was studied in older adults who are at increased risk for postoperative delirium. Thus, the extent to which the association demonstrated here between low DARS scores and postoperative delirium risk can be extrapolated to younger patients is unclear. Future studies should evaluate the extent to which low DARS scores are associated with delirium in young and middle-aged adults.

Fourth, delirium was assessed here via 2 different methods: geriatrician interviews (based on DSM-V criteria) at Duke, versus CAM-ICU assessments performed by research staff at Mt Sinai. The use of these 2 different delirium assessment methods in our cohort may thus have increased statistical error, although both geriatrician interview and CAM-ICU assessments by well-trained staff are highly sensitive and specific for identifying delirium.42

Fifth, the DARS utilizes the BIS, which is based on an unpublished algorithm,43,44 and BIS values are likely erroneously high by several points in older adults.15 Yet, despite this issue, these results demonstrate that BIS values in older adults are still sufficient for finding an association with increased postoperative delirium risk when used together with aaMAC in the DARS equation. Without an “ideal” anesthetic EEG monitor,45,46 the DARS offers a way to utilize BIS data and end-tidal anesthetic dosage to evaluate postoperative delirium risk. Future studies should compare the association strength with delirium of the DARS versus other EEG parameters (eg, burst suppression, alpha power, etc) and examine relationships between these measures and other postoperative outcomes.

Nonetheless, the association between low DARS scores and increased delirium risk is a step forward given the current state of the field, as we are unaware of any other anesthetic resistance index utilizing routinely available intraoperative monitor data that is associated with postoperative delirium risk. The DARS is the first such equation to be studied, and we expect that further refinements of it or other such equations will help improve the utility of these measures for clinical practice.

In summary, these data demonstrate that a processed EEG–based measure of brain anesthetic resistance is independently associated with delirium risk in older surgical patients. Future studies should attempt to replicate these findings, to define more precisely the threshold for a low DARS, to evaluate its use as an intraoperative real-time delirium risk stratification tool, to understand the neurobiological basis of low brain anesthetic resistance, and to study whether altering intraoperative care in patients with low DARS scores could help prevent delirium.

ACKNOWLEDGMENTS

We thank Austin Traylor (Duke Anesthesiology Department) for obtaining intraoperative electronic data from the Epic medical record system, Dr Stacey Chung (Duke Anesthesiology Department) for literature search assistance, and Dr Carl Pieper for thoughtful comments on the manuscript. The Duke Anesthesia Resistance Scale, including the equation, and all related documentation and software, are owned by Duke University. (c) Copyright 2020. Duke University. All Rights Reserved. Developed by Duke University School of Medicine, Duke University.

DISCLOSURES

Name: Mary Cooter Wright, MS.

Contribution: This author obtained, cross-checked, and analyzed data; created figures and tables; contributed written content; and helped review and edit the manuscript.

Conflicts of Interest: None.

Name: Thomas Bunning, MD.

Contribution: This author helped review and analyze the data, and review and edit the manuscript.

Conflicts of Interest: None.

Name: Sarada S. Eleswarpu, MD.

Contribution: This author helped review and analyze the data, and review and edit the manuscript.

Conflicts of Interest: None.

Name: Mitchell T. Heflin, MD, MHS.

Contribution: This author helped obtain data in the Perioperative Optimization of Senior Health (POSH) clinic, analyze the data, and review and edit the manuscript.

Conflicts of Interest: None.

Name: Shelley R. McDonald, DO, PhD.

Contribution: This author helped obtain data in the Perioperative Optimization of Senior Health (POSH) clinic, analyze the data, and review and edit the manuscript.

Conflicts of Interest: None.

Name: Sandhya Lagoo-Deenadalayan, MD, PhD.

Contribution: This author helped obtain data in the Perioperative Optimization of Senior Health (POSH) clinic, analyze the data, and review and edit the manuscript.

Conflicts of Interest: None.

Name: Heather E. Whitson, MS, MD.

Contribution: This author helped obtain data in the Perioperative Optimization of Senior Health (POSH) clinic, analyze the data, and review and edit the manuscript.

Conflicts of Interest: None.

Name: Pablo Martinez-Camblor, PhD.

Contribution: This author helped devise the statistical analysis plan for this manuscript and write the manuscript.

Conflicts of Interest: None.

Name: Stacie G. Deiner, MS, MD.

Contribution: This author helped obtain data at the Mt Sinai study site and write, review, and edit the manuscript.

Conflicts of Interest: S. G. Deiner has served as a consultant for Medtronic Boulder CO and Merck and has served as an expert legal witness.

Name: Miles Berger, MD, PhD.

Contribution: This author conceived of this project, performed study design and conception, assisted with analyzing data, and wrote the manuscript.

Conflicts of Interest: None.

This manuscript was handled by: Oluwaseun Johnson-Akeju, MD, MMSc.

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