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PRESURGICAL DEPRESSION AND ANESTHETIC SENSITIVITY
IN WOMEN UNDERGOING SURGERY
FOR THE REMOVAL OF GYNECOLOGICAL TUMORS
A THESIS PRESENTED TO THE GRADUATE SCHOOL
OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT
OF THE REQUIREMENTS FOR THE DEGREE OF
MASTER OF SCIENCE
UNIVERSITY OF FLORIDA
O 2007 Rachel Andre
To my God, who reminds me daily that life is my stage,
and I am performing for an audience of One
I would like to thank my advisors, Catherine Price and Deidre Pereira, for their time,
support and advisement. I would also like to thank Drs. Mary Herman and Christoph Seubert for
their expertise in the area of anesthesia. A special thank you to Dr. Jules Harrell, whose
mentorship I could not do without. In addition, I would like to thank Kerri Krieger for her
integral role in the data collection process. Most of all, I would like to thank my family and
friends for their encouragement, love, and prayers.
TABLE OF CONTENTS
ACKNOWLEDGMENTS .............. ...............4.....
LIST OF TABLES ............ ...... ._._ ...............7....
LIST OF FIGURES .............. ...............8.....
AB S TRAC T ......_ ................. ............_........9
1 INTRODUCTION ................. ...............11.......... ......
Clinical Assessment of Depth of General Anesthesia ................. ............... ......... ...1 1
Quantifieation of Depth of Anesthesia ................. ...............13......._.. ...
Clinical Significance of Depth of Anesthesia ................. ........__ ............_ .... 14
Depression as a Possible Premorbid Marker of Risk. ................. ..........._.__ .... 16.........
Depression and Frontal EEG ............... .......___ ..... ............1
Depression in the Gynecologic Oncology Population............... ...............2
Purpose of the Present Study ............ ..... .__ ...............24..
Introduction to Anesthetic Sensitivity .............. ...............25....
2 STATEMENT OF PROBLEM................ ...............28
Specific Aim I............... ...............29...
Specific Aim II .............. ...............29....
3 M ETHODS .............. ...............30....
Sample Characteristics.................. ..........3
Procedures and Assessment Instruments ................ ...............31................
Clinical Interview and Consensus Conference ................. ...............32........... ...
Psychological Assessment Measures .............. ...............33....
Other Questionnaires ................ .............. ...............35 .....
Neurop sy chological As se ssment Instruments .............. ...............3 5....
Outcome Variable--Anesthetic Sensitivity .............. ...............36....
Statistical Analy ses............... ...............37
Specific Aim ................. ...............37........ ......
Specific Aim II .............. ...............38....
4 RE SULT S .............. ...............41....
Specific Aim I: Relationship Between Depression and Anesthetic Sensitivity
Independent of Group Classification ................ .... .... ... ......................4
Specific Aim II: Relationship Between Group Classification and Anesthetic Sensitivity....42
5 DI SCUS SSION ............ ..... ._ ...............47...
Summary and Interpretation of the Results ............ .....__ ...............47
Specific Aim I............... ...............47...
Specific Aim II ................ ........ ..... ...............4
Implications and Relevance to the Current Literature ......____ ..... ... ._ ..........._....51
Limitations of the Present Study ............ ..... ._ ...............52..
Directions for Future Research ............ ..... ._ ...............55...
Summary and Conclusion............... ...............5
LIST OF REFERENCES ............ ..... ._ ...............58...
BIOGRAPHICAL SKETCH .............. ...............64....
LIST OF TABLES
Table 3-1. Participant characteristics by group--Means and standard deviations shown............39
Table 4-1. Means and standard deviations for psychological assessment measures. ...................43
Table 4-2. Correlation matrix for AOC and hypothesized covariates. ............. ....................43
LIST OF FIGURES
Figure 1-1. Proposed model showing the major associations conceptualized in the present
study. .............. ...............27....
Figure 3-1. Study design flowchart. ........._.._.. .....___ ...............39..
Figure 3-2. Illustration of 'area under the curve with respect to ground' (AUCG) and 'area
over the curve' (AOC). ............. ...............40.....
Figure 3-3. Formulas for 'area under the curve with respect to ground' (AUCG) and 'area
over the curve' (AOC) .............. ...............40....
Figure 4-1. Relationship between MBMD depression scores and anesthetic sensitivity
(A O C). ............. ...............44.....
Figure 4-2. Relationship between MBMD future pessimism scores and anesthetic sensitivity
(A O C). ............. ...............45.....
Figure 4-3. Relationship between group classification and anesthetic sensitivity (AOC). ........46
Abstract of Thesis Presented to the Graduate School
of the University of Florida in Partial Fulfillment of the
Requirements for the Degree of Master of Science
PRESURGICAL DEPRESSION AND ANESTHETIC SENSITIVITY
IN WOMEN UNDERGOING SURGERY
FOR THE REMOVAL OF GYNECOLOGICAL TUMORS
Chair: Catherine Price
The present investigation examined the role of presurgical depression on anesthetic
sensitivity. Based on theories of depression, frontal activity and anesthetic mechanisms, it was
hypothesized that presurgical depression may place an individual at risk for greater
responsiveness to initial anesthetic induction. Further, it was hypothesized that individuals with
a history of depressive symptomatology would demonstrate greater sensitivity to initial
Twenty-six women between the age of 40 and 81 years (M/SD = 58.9/10.9) planning
surgery under general anesthesia for the removal of gynecologic tumors completed measures of
current depression one day before surgery. The measures used were the Beck Depression
Inventory-Second Edition (BDI-II) and three scales of the Millon Behavioral Medicine
Diagnostic (MBMD)--the Depression, Dejected, and Future Pessimism Scales. A preoperative
health screening was used to classify women with (N = 11) or without (N = 15) a history of
depression. Anesthetic sensitivity was quantified as an individual's cumulative response to
anesthetic drugs during the initial anesthetic induction phase and assessed with a unilateral
frontal lobe EEG index derived from a bispectral index (BISTM) monitor. The dependent
variable, anesthetic sensitivity, was quantified using an 'area over the curve' (AOC) estimation
based on individuals' responses to anesthetic induction.
Higher reports of baseline presurgical depression were correlated with greater anesthetic
sensitivity as measured by the MBMD Depression and Future Pessimism Scales (r = 0.443 and
0.474, respectively, ps < 0.05). However, there was no relationship found between the BDI-II or
the MBMD Dejected Scale and anesthetic sensitivity. Further, there was no evidence of
differences in anesthetic sensitivity among individuals with and without a history of depression.
These preliminary findings suggest that increasing levels of current presurgical depression
may influence anesthetic sensitivity as defined by the AOC quantification. These findings
indicate that premorbid factors may influence anesthetic management and, possibly, surgical
outcome. Future studies need to examine the neurological mechanisms associated with
premorbid anesthetic risk (e.g., frontal lobe EEG in depressed individuals having general
General anesthesia results in immobility, loss of consciousness, and reduced electrical
activity in the brain (Grasshoff, Rudolph, & Antkowiak, 2005; McKechnie, 1992). In particular,
anesthesia is known to suppress frontal lobe activity, a process that has been referred to as "depth
of anesthesia" (Bruhn, Myles, Sneyd, & Struys, 2006), or anesthetic depth. One study suggests
that greater anesthetic depth may be a clinically important predictor of increased incidence of 1-
year mortality among non-cardiac surgical patients (Monk, Saini, Weldon, & Sigl, 2005).
However, there is little research on the predictors of anesthetic depth. It has been hypothesized
that patients who have less physiologic reserve (e.g., physically ill, older, cognitively impaired)
may be more susceptible to the depressant effects of anesthesia (Muravehick, 1998), and may
therefore experience greater anesthetic depth and possibly greater anesthesia-related morbidity
and mortality. Premorbid patient factors that are associated with suppressed frontal lobe activity
may heighten risk for greater anesthetic depth. Depression, for example, has previously been
associated with reduced frontal activity (e.g., Davidson, 1998). Therefore, depression may
compromise reserve and heighten risk for greater anesthetic depth among individuals undergoing
surgery. The following review will examine the clinical assessment of anesthetic depth, explore
the relationship between depression and frontal-specific brain function, and provide a rationale
for examining response to anesthesia in women undergoing surgery for the removal of
Clinical Assessment of Depth of General Anesthesia
Anesthetic depth has been conceptualized as the effect of anesthetic drugs on the brain
cortex and is generally derived from a composite of patient responses to anesthetic drugs.
Anesthesia acts on three parts of the nervous system, producing somewhat of a suppressor effect
(Grasshoff et al., 2005). These are the spinal cord, the cerebral cortex, and the brain-reticular
activating system, which result in immobility and adequate blunting of autonomic responses to
noxious stimuli, loss of consciousness (i.e., to prevent anesthetic awareness), and reduced
electrical activity in the brain, respectively. The primary goal of anesthesia, however, is the
maintenance of homeostasis during a surgical intervention (Ransom & Mueller, 1997), or
conversely, unconsciousness and the prevention of memory formation (Glass, 1998). This is a
highly individualized process, based on patient factors such as comorbid illness, genetic
predisposition, and psychological factors. The effectiveness of general anesthesia is judged then
by the knowledge of the pharmacology of anesthetic agents, as well as the monitoring of clinical
signs, such as changes in heart rate and rhythm, as well as blood pressure, suppression of reflex
responses to stimuli, attention to patient muscle tone (i.e., movement), and control of patient pain
and level of consciousness.
As previously noted, vulnerable patient populations (i.e., those with advanced age or end-
organ impairment, the terminally-ill, and those with otherwise compromised cognitive or
physiologic reserve) tend to have an exaggerated drug effect from an "average" dose of
anesthetic, and therefore require an adjustment in the dose of anesthetic to achieve a standardized
depth of anesthesia (Muravehick, 1998). Indeed, research has shown that patients who are ill
prior to surgery are more vulnerable to surgery itself (e.g., Newman et al., 1995). Bernstein and
Offenbartl (1991), for example, examined the impact of patients' presurgical comorbidities on
postoperative outcomes. Comorbidities included severe mental and cognitive impairments, such
as dementia. Although this was a retrospective investigation, a significant amount of fatal and
nonfatal complications were associated with mental disorders, including dementia,
schizophrenia, bipolar disorder and mental retardation. Of 59 of 975 general anesthesia cases
that resulted in some complication, 32 cases had presurgical dementia (25 of which resulted in
mortality). Further, patients with presurgical cognitive impairments had an equal incidence of
nonfatal complications as the surgery patients as a whole.
Quantification of Depth of Anesthesia
Within the last few years, several monitors have been developed to measure the effect of
anesthetic drugs on cortical function (i.e., brain activity in the frontal cortex). Although it is
neither designated as a routine patient monitor by the American Society of Anesthesiologists
(ASA) nor considered a standard of care, the Bispectral Index Score (BISTM) mOnitor is clinically
most widely used. The bispectral index score (BIS) is a dimensionless EEG-derived value,
ranging from 0 (deep coma) to 100 (fully awakened), that measures the sedative component of
the anesthetic state (i.e., hypnotic depth of anesthesia) via a unilateral electrode (Bruhn et al.,
2006; Renna & Venturi, 2000). While informative, the BIS monitor has been shown to give
misleading information. Though BIS values fall as a function of cortical suppression following
anesthetic induction, a range of effects can be seen across individuals, drugs, and settings. For
example, intraoperative BIS values may be exaggerated because of muscle activity (Messner,
Beese, Romstock, Dinkel, & Tschaikowsky, 2003); baseline BIS values may be affected by
neurological diseases (Renna & Venturi, 2000); and some anesthetics, most notably ketamine, do
not cause dose-dependent BIS depression (Kelley, 2003). Furthermore, because BIS only
measures the hypnotic component of an anesthetic, target ranges for intraoperative BIS values
vary depending on the combination of drugs used. BIS values derived during a balanced
anesthetic with a substantial opioid component typically range from 45 to 60, compared to fully
awakened BIS values, which naturally range from 96 to 100. However, target values for BIS are
less well defined for other anesthetic techniques (Kelley, 2003; Johansen, Sebel & Sigl, 2000;
Song, Joshi & White, 1997). Nonetheless, the interpretation of BIS values necessitates
consideration of factors related to the patient, as well as the anesthetic used and has vast
implications for anesthetic management.
Clinical Significance of Depth of Anesthesia
In 2005, Monk and colleagues reported Eindings that suggest an association between depth
of anesthesia, measured as anesthetic drug effect on the brain cortex, and postoperative mortality
within a year following surgery. These Eindings followed a prospective observational study of
1064 adult patients (18 years old or older) undergoing non-cardiac surgery under general
anesthesia at Shands Hospital at the University of Florida. This study was designed to examine
the relationship between postoperative mortality (defined as mortality within a year following
surgery) and a variety of demographic, clinical, and intraoperative factors. The study employed
use of the A1050 Bispectral Index Score (BISTM) monitor and sensors (Aspect Medical Systems
Inc., MA) to quantify hypnotic depth of anesthesia. BIS data was recorded throughout the
surgical intervention and digitized at 5-minute intervals. Anesthetic depth was calculated as
cumulative deep hypnotic time, defined as the total amount of time (in hours) that BIS values fell
below 45. A relative risk analysis was conducted using Cox proportional hazards modeling to
determine the independent and combined impact of anesthetic depth, comorbid illness,
demographic factors (e.g., age, race), clinical history (e.g., tobacco or alcohol use, preoperative
blood pressure), and intraoperative factors (e.g., surgical duration, intraoperative blood pressure)
on risk for postoperative death.
Results of this study indicated three variables as significant independent predictors of
postoperative mortality--hypnotic depth of anesthesia (i.e., cumulative deep hypnotic time where
BIS was <45), presence of comorbid disease, and intraoperative systolic hypotension. While the
authors acknowledged that death during the first year after surgery was primarily associated with
pre-existing comorbidities and hypotension, not surprisingly, the Einding relating anesthetic
depth to increased mortality at one year garnered the most attention. The primary criticism of
this study was that is was not designed to investigate the relationship between intraoperative
anesthetic management and long-term outcome, suggesting incidental results at best. The use of
a prospective observational method was particularly problematic in its failure to account for
premorbid factors that might have contributed to the adverse outcomes observed. Thus, the
conclusions were confounded by the use of the BISTM monitor as convincing evidence for the
observed relationship without a priori methodological control for known comorbidities, surgical
diagnoses, anesthetic drugs, intraoperative anesthetic management, or other factors generally
associated with mortality. Nonetheless, these associations suggest that intraoperative anesthetic
management may affect long-term outcomes more than previously appreciated, which has vast
implications for preventative intraoperative care.
A few other studies have at least attempted to address the relationship between anesthesia
and adverse events. Rasmussen and colleagues (2003), for example, reported a greater incidence
of postoperative cognitive dysfunction (POCD) at 1-week post-surgery, as well as postoperative
mortality, after general anesthesia compared to regional anesthesia. However, no significant
differences were observed between groups for other postoperative problems, including POCD at
3-months after surgery, delirium, and a number of medical complications (e.g., cardiac event).
Despite these Eindings, Rasmussen and colleagues concluded that the etiology of POCD, as well
as the incidence of mortality, were likely multifactorial rather than the result of anesthesia. In
regard to the report of more deaths in the general anesthesia group, the investigators
acknowledged that their study was not designed to evaluate uncommon postoperative
complications (e.g., mortality). Further, the study provided no conclusive evidence that long-
term cognitive changes are caused by general anesthesia. Still, other studies indicated minimal
risks associated with anesthesia during the perioperative period (Arbous et al., 2001; Sigurdsson
& McAteer, 1996). Thus, the role of anesthesia on postoperative outcomes is, indeed,
controversial. While some studies found minimal complications related to anesthesia (e.g.,
Rasmussen, 2003), others reported more significant outcomes related to anesthesia, to the
greatest extent mortality (e.g., Monk et al., 2005).
Indeed, contrary to the results of the aforementioned studies, anesthesia-related mortality
and complications may likely be explained by the interaction between anesthesia and premorbid
factors, such as comorbid conditions and genetic or psychological factors, rather than by
anesthesia alone. Simply stated, baseline impairment across a variety of domains may lead to
negative outcomes. However, there continues to be a lack of attention to premorbid factors that
may predict anesthetic depth, and consequently index risk for adverse outcomes such as
mortality. Thus, consideration of the possible influence of premorbid patient factors on
anesthetic responsiveness is warranted.
Depression as a Possible Premorbid Marker of Risk
Anxiety and depression are psychological factors known to affect the response to
anesthetic drugs. For instance, patients with higher baseline preoperative anxiety have been
shown to require more intraoperative anesthetic to achieve a clinically sufficient hypnotic state
than patients with lower baseline preoperative anxiety (Maranets & Kain, 1999). In this cross-
sectional study of 57 women undergoing bilateral laparoscopic tubal ligation, a differential
response to anesthesia was demonstrated in groups low, moderate, and high on trait (i.e.,
characteristic) anxiety. These effects were seen for anesthetic induction, as well as maintenance,
using the Aspect A1000 BISTM monitor to control hypnotic depth of anesthesia. In regard to
depression, a recent meta-analysis (Dickens, McGowan & Dale, 2003) reviewed the impact of
patient depression on experimental pain perception. Findings suggest that depressed patients
may have a lower threshold for pain than non-depressed patients. This may have maj or
implications for surgical interventions; namely, increased sensitivity to pain evidenced in
depressed patients would necessitate delivery of enough intraoperative anesthetic to compensate
for that effect. Hence, there is a need for research directed towards examining the relationship
between presurgical depression and response to anesthesia (i.e., depth of anesthesia) and
minimizing the impact of this risk factor.
Depression and Frontal EEG
Anesthesia specifically targets the frontal lobes (Drover & Ortega, 2006); and it has been
hypothesized that depressed individuals may be particularly vulnerable to the effects of
anesthesia. Depression has many known neurological components, which have been validated in
a variety of literature examining the functional and structural role of the prefrontal cortices,
anterior cingulate, amygdala, and hippocampus in affect and emotion regulation (Davidson,
Pizzagalli, Nitschke, & Putnam, 2002). Of particular interest for the present study is the
literature that has previously linked depression to abnormalities in electrical activation of the
prefrontal regions of the brain (e.g., Davidson, Abercrombie, Nitschke, & Putnam, 1999;
Davidson, 1998), which suggests that depression may be one possible risk factor for anesthesia-
related complications. The predictive value of depression for response to anesthesia has not,
however, been evaluated.
Previous research employing a variety of methods (e.g., cerebral blood flow and glucose
metabolism) to elucidate the association between depression and cortical activity have yielded
inconsistent findings. Still, there is substantial research indicating that depression is linked to
neuroanatomical differences, particularly of the frontal region of the brain. The following
review will focus on research that has employed the use of multi-site electroencephal ographs
(EEG) to make inferences about patterns of regional cortical activation in the brain.
Notwithstanding controversy, much of this literature has related depression to
neuroanatomical differences (i.e., abnormalities) in the prefrontal cortex of the brain. In
particular, research suggests that the left hemisphere is involved in depression (e.g., Black, 1975;
d'Elia & Perris, 1973, 1974; Gainotti, 1972; Gasparrini, Satz, Heilman, & Coolidge, 1978; Perini
& Mendus, 1984; Robinson, Kubos, Starr, Rao, & Price, 1984). In a comprehensive review of
this literature, Drevets (1998) noted that several studies provided evidence to support reduced
frontal activation (with respect to alpha frequencies) of the prefrontal cortex in patients with
maj or depressive disorder. To be clear, there is an inverse relationship between alpha power and
region-specific activation (Davidson, 1988; Lindsey & Wicke, 1974). Some investigators, for
instance, described abnormalities in activation of prefrontal regions in depressed individuals as
decreased bilateral or predominantly left-sided activation (e.g., Davidson et al., 1999; George,
Ketter, & Post, 1994). Indeed, the most consistent findings have related increased alpha power
to left frontal hypoactivation, or less left-sided activity (e.g., Bell, Schwartz, Hardin, Baldwin, &
Kline, 1998; Bruder et al., 1997; Davidson, Chapman, & Chapman, 1987; Davidson, Schaffer, &
Saron, 1985; Gotlib et al., 1998; Schaffer, Davidson, & Saron, 1983). Fewer studies have
demonstrated the opposite (i.e., increased alpha power associated with decreases in right frontal
activation), a variation of previous findings, or an absence of abnormality or group differences
altogether (e.g., Kentgen, Tenke, Pine, Fong, Klein, & Bruder, 2000; Reid, Duke, & Allen, 1998;
Rochford, Swartzberg, Chowdhery, & Goldstein, 1976).
Davidson and colleagues, for example, have made significant contributions to this
literature. To provide a few detailed examples, in the early 1990s, Henriques and Davidson
conducted several investigations to examine the differential activation of prefrontal cortical
regions among depressed and healthy individuals. One of these studies examined whether
asymmetrical activation of the prefrontal cortex discriminated between previously depressed and
healthy controls (Henriques & Davidson, 1990). Following the notion that individuals with a
history of depression (current or remitted) are at increased risk for future depression, the
investigators also examined the utility of using region-specific electroencephalography (i.e.,
examination of cortical symmetry) as a state-independent marker of vulnerability to future
depression. A small sample (N = 14) of participants (with and without a history of depression)
was evaluated in respect to emotional state (before and during the EEG protocol), as well as
brain activity (as measured by EEG using three reference points computed from 14 electrodes).
Although power in all frequency bands was examined, results were only significant for alpha
power, which is consistent with most literature in this area.
Findings showed participants with a history of depression demonstrated asymmetrical
activation in the direction of more alpha power, or less left frontal and right posterior activation
as compared to never-depressed control participants. Because the sample differed only in their
history of depression (i.e., patients were carefully matched on several demographic variables,
including age, gender, and socioeconomic status, and there were no significant differences in
self-reported depression, emotional state, or medication history), these results suggest EEG is a
reliable state-independent marker of depression history, which they proposed had implications
for the prediction of future psychopathology or vulnerability to affective disorders. Later studies
use the diathesis-stress model as a conceptual framework to explain how prefrontal asymmetry
may bias affective style, and thereby increase vulnerability to psychopathology (e.g., Davidson,
In another study, Henriques and Davidson (1991) sought to demonstrate differences in left-
sided frontal activation among depressed and never-depressed controls, with specific attention to
the midfrontal and parietal regions. Following a similar procedure as the 1990 investigation, a
small sample (N = 28) was evaluated. Patients with a history of depression (all of whom also
met research criteria for current depression) demonstrated left frontal hypoactivation (i.e., more
left-side alpha power, or less frontal activation) in the midfrontal region. Group differences were
not detected in the parietal region. These Eindings support, at least partially, the investigators'
contention that cortical activation differs during approach- and withdrawal-related behavior, such
that depressed individuals, who are more likely to demonstrate withdrawal-related behaviors
(e.g., loss of initiative, difficulty concentrating, indecisiveness, hopelessness), will also
demonstrate decreased left frontal activation.
While many studies have replicated Eindings demonstrating reduced left relative to right
activation in depressed individuals (e.g., Bell et al., 1998; Bruder et al., 1997; Davidson,
Schaffer, et al., 1985; Davidson, Chapman, et al., 1985; Debener, Beauducel, Nessler, Brock,
Heilemann, & Kayser, 2000; Gotlib et al., 1998; Schaffer, Davidson, & Saron, 1983), it is worth
noting that other Eindings are variable. For example, in addition to discussing the inconsistencies
in the literature, Reid and colleagues (1998) failed to support their hypotheses that there would
be region-specific group differences (here, mid-frontal and lateral-frontal regions) in regard to
alpha activity (Study 1) or that this relationship would be apparent in a range of depressed
individuals (Study 2). In the first study, they hypothesized that their depressed group would
exhibit reduced left frontal activation relative to non-depressed controls. Results did not reveal
group differences in those regions. They did, however, show differences in the parietal region.
Further, among a sample of depressed individuals (Study 2), asymmetry was not related to
depression severity. These findings were surprising given the support for the hypotheses in the
previous literature; however, there were few methodological differences (i.e., changes from
previous methodologies) and limitations that may have contributed to these observations. One
methodological difference, which appears to have had a significant influence on the findings,
was the length of EEG recordings employed in the present study (8 min) compared to others (30
sec to 1 min). In fact, decomposition of intervals of EEG recordings into shorter blocks (2 min),
revealed group differences commensurate to previous findings.
In sum, research conducted within the last 25 years has extensively illustrated the
relationship between generalized slowing in the prefrontal cortex (i.e., asymmetrical activation of
frontal regions of the brain) and depression. Despite the complexity of this literature and the
variable findings, these studies have advanced our understanding of the neurological basis of
depression. Indeed, use of electroencephalography to make inferences about patterns of regional
cortical activation in the brain has significant implications for mediation of various outcomes
(e.g., identification of individuals at risk for future depression). Though the relationship between
cortical activity and depression has been largely substantiated in the literature, no attention has
been directed towards implications for medical outcomes. For example, one can surmise that
depressed individuals (who are predisposed to reduced frontal activation) may be particularly
sensitive to anesthesia, which has a suppressing effect on the frontal cortex. Essentially, the
underlying implication is that depression may be an index of anesthetic response, which has vast
implications for healthcare delivery (i.e., anesthetic management). Furthermore, filling gaps in
the literature is of particular importance in populations where depression is at least marginally
Depression in the Gynecologic Oncology Population
Stress and depression are leading indicators of mortality, particularly among individuals
diagnosed with cancer. Indeed, cancer patients experience numerous sources of acute and
chronic stress (Spiegel, 1997; Vess, Moreland, Schwebel, & Kraut, 1988), which may manifest
as a dysregulation of the circadian rhythmicity of cortisol secretion (Luecken, Dausch, Gulla,
Hong, & Compas, 2004; Mormont & Levi, 1997; Ockenfels, Porter, Smyth, Kirschbaum,
Hellhammer, & Stone, 1995; Sephton, Sapolsky, Kraemer, & Spiegel, 2000). Further, this
dysregulation has been linked to both psychosocial stress and cancer progression, especially
among patients with more advanced cancers (Sephton & Spiegel, 2003; Touitou et al., 1996).
Depression, the second psychological stressor indicated in mortality, has also been linked
to dysregulated cortisol (Cohen, de Moor, Devine, Baum, & Amato, 2001), as well as to fatigue
(Bower, Ganz, Dickerson, Petersen, Aziz, & Fahey, 2005), both of which are common features
observed among individuals with cancer. It is not surprising then that depression, like stress, can
negatively impact at-risk individuals by increasing risk for or complicating the course of cancer
and its treatment and even speeding the progression of the disease (Katon & Sullivan, 1990). In
addition, depression is linked to an increase in all-cause-mortality (Watson, Haviland, Greer,
Davidson, & Bliss, 1999), which is particularly problematic among individuals with cancer.
Though the impact of depression on cancer prognosis is posited in the literature, little has
been done in the way of addressing the impact of depression on individuals with imminent
cancer diagnoses (i.e., those who are awaiting a conclusive diagnosis of cancer). In most cases,
cancer diagnosis is preceded by a series of clinical tests to identify or assess the nature of clinical
signs (e.g., presence of a tumor) and to determine the severity of pathology. This can be a
potentially stressful process. As in the case of cancers etiologically related to an overgrowth of
cells, surgical intervention to extract the tumor(s) is often necessary. Such cases warrant an
adequate evaluation of the relationship between stressful life events conceptualizedd as the
combination of physical, environmental, emotional, and psychosocial variables),
physiologic/cognitive reserve, and prognosis, as well as factors that may impact medical
outcomes (e.g., complications with anesthesia).
Though little is known of prevalence rates of depression among individuals awaiting
cancer diagnosis (i.e., those with known clinical signs but awaiting conclusive diagnoses),
prevalence rates for individuals with comorbid depression and a variety of cancer types have
been estimated. For example, depression occurs in 12 to 23% of patients with gynecologic
cancers (Massie, 2004). This means that a subgroup of the gynecologic oncology population
(i.e., those who have gynecologic tumors) face the same prognostic risks as those already
diagnosed with cancer. Additionally, because some proportion of these women will eventually
receive a diagnosis of cancer, it is reasonable to expect the incidence of depression among them
to be less than the upper limit of the range estimated for women with definitive cancer diagnoses
(i.e., <23%). To be more specific, the prevalence of depression among women with gynecologic
tumors could be estimated based on the known incidence of cancer diagnosis within this
population. Based on an estimated 80% incidence of cancer diagnosis in this population, it is
likely that 18.4% of these women have comorbid depression, which is enough to warrant clinical
Earlier, it was implied that depressed individuals might be particularly sensitive to
anesthesia. This was based namely on the known predisposition of depressed individuals to
reduced frontal activation, as well as the posited suppressing effect of anesthesia on the frontal
cortex. While anesthesia-related complications have declined significantly over the last few
decades (i.e., following the advent of more sophisticated intraoperative monitoring and
anesthetic management techniques), they are not uncommon, particularly among individuals who
are more susceptible to the effects of anesthesia (e.g., depressed individuals). Though statistics
do not indicate an enormous incidence of depression among gynecologic oncology patients (both
with and without conclusive diagnoses), the incidence is large enough to merit attention.
Particularly among patients awaiting a diagnosis, independent of direction (i.e. malignant or
benign), this diagnostic period can be especially stressful (even more so for those who are
already depressed), which may complicate the course of treatment. Thus, examination of
depression as a premorbid risk factor for anesthesia- related complications can be useful in
understanding differences in response to anesthesia, which ultimately has implications for
prevention and intervention.
Purpose of the Present Study
The present study purposed to draw a conceptual link between depression, brain function
(i.e., electrical activity in the frontal lobe), and depth of anesthesia. The former literature review
sought to achieve the following obj ectives: (a) to define depth of anesthesia and explore how it
has been quantified in previous research, (b) to examine and summarize the large body of
literature linking depression to asymmetrical activation of the frontal cortex, and (c) to provide a
rationale for examining response to anesthesia in women undergoing surgery for the removal of
Despite the sufficient evidence available to propose a model linking the findings of the
aforementioned areas, the impact of depression on a variety of intraoperative factors has been
largely overlooked. In fact, the vast maj ority of research in areas of clinical interest, including
postoperative cognitive dysfunction (POCD) and anesthetic awareness, has only addressed the
psychological impact of these complications (e.g., post-traumatic stress disorder following
anesthetic awareness), often glazing over or neglecting the preoperative piece (i.e., the impact of
comorbid disorders, as well as latent psychosocial factors such as a pre-existing history of
depression). So, although previous research has shown an increased incidence of postoperative
depression attributable to pain, complications of anesthesia, and other underlying causes across a
variety of patient populations (Elkins, Whitfield, Marcus, Symmonds, Rodriguez, & Cook, 2005;
Le Grand et al., 2006; Lindal, 1990; Miller, Jones, & Carney, 2005; Munro & Potter, 1996), no
study to date has examined the relationship between presurgical depression and 'anesthetic
Introduction to Anesthetic Sensitivity
No line of research has formerly or directly documented a relationship between depression
and 'anesthetic sensitivity'. This can be attributed to the novelty of the concept. The present
study proposed a model linking depression and anesthetic sensitivity via the conceptual
framework of the literature linking depression to asymmetrical activation of the frontal cortex
(Figure 1-1). Here, anesthetic sensitivity referred to an individual's cumulative response to
anesthetic drugs (measured in the same way as depth of anesthesia using digitized EEG derived
from a patient state monitor) during the initial anesthetic induction phase (refer to methods
outlined in Chapter 3 for a more detailed explanation). To be clear, the present study represented
the first attempt to examine the demographic, biological, and psychological correlates of
Specifically, the purpose of the present study was to examine the relationship between
presurgical depression and anesthetic sensitivity in an at-risk population, some of which had a
history of depressive symptomatology. To assess this, women over the age of 40 undergoing
surgery for the removal of gynecologic tumors completed several self-report mood measures,
with particular focus on depressive symptomatology, the day before their surgery. Additionally,
intraoperative data related to the participants' responsiveness to anesthesia was collected.
Participants were classified into two groups based on history of depressive symptomatology and
compared on the basis of anesthetic sensitivity and current symptomatology. Identification of an
interaction between depression and anesthetic depth is believed to improve our ability to predict
anesthetic sensitivity, as well as to develop preoperative, as well as intraoperative, interventions
to minimize associated outcomes.
Asymmetrical Activation of
the Frontal Cortex (EEG)
Figure 1-1. Proposed model showing the major associations conceptualized in the present study.
STATEMENT OF PROBLEM
The preceding review of literature provided a framework for undertaking the current
investigation hypothesizing a relationship between depression and anesthetic sensitivity. As
previously established, general anesthesia results in suppression of frontal lobe activity, a process
that has been referred to as "depth of anesthesia" (Bruhn et al., 2006), which may be a clinically
important predictor of increased incidence of intraoperative and postoperative complications. To
the greatest extent, 1-year mortality among non-cardiac surgical patients has been reported to be
related to increased anesthetic depth (Monk et al., 2005). Though much is known about the
mechanisms of anesthesia, there is little research on the predictors of response to anesthesia. To
this end, it has been hypothesized that patients who have less physiologic or cognitive reserve
may be more susceptible to the depressant effects of anesthesia (Muravehick, 1998). As
previously alluded, premorbid patient factors that are associated with suppressed frontal lobe
activity, such as depression, may heighten risk for greater anesthetic depth (identified here as
'anesthetic sensitivity'). Hence, the present study examined the impact of depression on
anesthetic sensitivity in a sample of women undergoing surgery for the removal of gynecologic
No line of research has formerly or directly documented a relationship between depression
and anesthetic sensitivity. It is, indeed, a novel concept. Here, anesthetic sensitivity was defined
as an individual's cumulative response to anesthetic drugs during the initial anesthetic induction
phase. It was measured in much the same way as depth of anesthesia (using digitized EEG
derived from a patient state monitor) and was calculated with respect to 'area over the curve'
(AOC) of BIS during the anesthetic induction phase (more on this in the following chapter).
Further, this study assessed the effect of history of depression on anesthetic sensitivity by
classifying participants into two depressed groups (depressed versus not depressed) based on an
interview. The incidence of depression among surgical patients (particularly those who have or
are at risk for cancer) is also thought to be significant, and thus the effects of anesthesia on this
sample was reasonably expected to be apparent. Although correlational analyses do not provide
causal evidence for the relationship between depression and anesthetic sensitivity, the current
study might represent a significant movement towards identifying areas for clinical intervention
at the preoperative, intraoperative, and postoperative levels.
The current study addressed the following specific aims:
Specific Aim I
To examine the relationship between presurgical depression and anesthetic sensitivity
in an at-risk population (i.e., gyn-oncology). Given the known effect of anesthesia on the
frontal lobe (e.g., McKechnie, 1992) and the association between depression and reduced frontal
activity (e.g. Davidson, 1998), it was hypothesized that depression severity would be positively
related to greater sensitivity to anesthesia. Specifically, it was predicted that individuals who
report more depressive symptoms prior to surgery would show greater responsiveness to initial
anesthetic induction, measured as 'area over the curve' (AOC).
Specific Aim II
To evaluate whether anesthetic effects differ among individuals with and without a
history of depressive symptomatology. It was hypothesized that there would be group
differences in response to anesthesia. Specifically, it was predicted that individuals with a
history of depressive symptomatology would demonstrate greater sensitivity to initial anesthetic
induction, also measured as AOC.
Participants were a subgroup of 76 women concurrently enrolled in an ongoing
longitudinal study of anesthetic management, cognitive dysfunction, and mortality. They
included 26 women, all above the age of 40, undergoing lower abdominal surgery for the
removal of gynecologic tumors (i.e., one or a combination of the following procedures (not
exhaustive): total or partial abdominal hysterectomy, bilateral salpingectomy/oophorectomy,
exploratory laparoscopy, appendectomy, lymph node dissection/sampling, cytoreduction,
appendectomy, omentectomy, and colectomy). Eleven of these women were identified as having
a history of depressive symptomatology based on a consensus conference that took into account
a report of a combination of factors, including current and/or past depressive symptomatology,
diagnosis of clinical depression, and self-reported treatment for depressive symtomatology,
including a history of antidepressent use as determined by self-report and/or review of available
medical records. The remaining age- and education-matched participants were 15 women with
no known history of depressive symtomatology.
The following inclusion and exclusion criteria were applied. Participants were required to
be over the age of 40 and native English speakers. Also, participants were also required to score
> 24 on the Mini-Mental State Exam (MMSE). Additional exclusion criteria applied exclusively
to fulfill research aims for the larger longitudinal study included (a) severe cardiovascular
compromise or an ej section fraction of < 20%, (b) need for regional anesthesia and/or emergency
surgery, (c) malignant hyperthermia, (d) choline esterase deficiency, (e) porphyria, (f) allergy to
lidocaine, (g) inability to tolerate a normal dose of hypnotic during anesthetic induction (based
on the clinical judgment of the attending anesthesiologist), and (h) conditions that would
confound interpretation of neurocognitive tests such as blindness, severe hearing impairment,
and brain metastases.
Forty-three of the 76 women enrolled in the larger longitudinal study consented to
participate in additional psychological and neurocognitive testing. Although all were eligible, 17
possible participants were excluded from the current analysis The remaining 26 participants
were between the age of 40 and 81 years (M/SD = 58.9/10.9), of average intelligence (M/SD =
103.3/19.0), and, on average, were at least high school educated (M/SD = 12.7/2.3 years). The
sample represented a variety of ethnic backgrounds, including 19 Caucasian participants, 4
African-American participants, one Hispanic participant, one Native-American participant, and
one participant of Pacific Island origin. There were no significant differences between the
groups with and without a history of depressive symptomatology with respect to age [t (24) =
1.43, p = 0. 17], intelligence (as measured by the Wechsler Abbreviated Scale of Intelligence;
WASI) [t (17) = 1.67, p = 0. 11], and presence of comorbid disease (as measured by the Charlson
Comorbidity Index; CCI) [t (24) = 1.06, p = 0.30]. The group with a positive history of
depressive symptomatology was, however, relatively less educated [t (19) = 2.66, p = 0.02].
Table 3-1 summarizes participant characteristics.
Procedures and Assessment Instruments
Participants were systematically recruited via close collaboration with the scheduling staff
of the UF-Shands Gynecologic Oncology Clinic and the principal investigators of the larger
longitudinal study examining 'Anesthetic Depth and Mortality' in this patient population. As
part of this larger investigation, all patients were to have gynecological surgery to identify, to
SThirteen participants were excluded because their Bispectral Index Scores (BIS) records were invalid, inaccessible,
or missing. Three participants did not complete psychological measures. One participant did not meet the minimum
criteria for MMSE score >24.
remove, and identify the pathology of gynecological masses. During a routine examination and
assessment for surgery, patients meeting study criteria were identified and invited to participate
in the study. Interested participants provided informed consent for participation following
University of Florida Institutional Review Board guidelines. Consented participants were
scheduled for admission to the General Clinical Research Center (GCRC), where they completed
a brief clinical interview and neurocognitive and psychological testing the day before their
surgery. Before the surgical procedure, all participants received the same weight-based
induction of anesthesia. Anesthesia was then maintained with one of three randomized,
prescribed anesthetics. The same surgeon performed all procedures. See Figure 3-1 for an
overview of the study design.
Clinical Interview and Consensus Conference
Participants underwent a presurgical clinical interview to obtain relevant background and
demographic information, medical and psychiatric history, as well as family health history. A
thorough review of history of depression, anxiety, and other mood disorders was made.
Participants endorsing a history of depression as defined by self-report of current and/or past
depressive symptomatology (but not exclusively current symptomatology), diagnosis of clinical
depression, and/or treatment for depressive symtomatology, including a history of antidepressent
use or psychotherapy focused on addressing clinical depression were considered for
classification in the history of depressive symptomatology group. In some cases, classification
was made on the basis of Eindings from a review of available medical records. Final
determination of group classification was made via consensus conference. Post hoc comparisons
of groups were made on the basis of these classifications.
Psychological Assessment Measures
Several mood measures were administered to participants the day before surgery to assess
baseline mood status, including the Beck Depression Inventory--Second Edition (BDI-II) and
the Millon Behavioral Medicine Diagnostic (MBMD).
Beck Depression Inventory--Second Edition (BDI-II; Beck, Steer, & Brown, 1997):
The BDI-II is a 21-item self-report inventory. It is the most widely used screening instrument to
detect depressive symptomatology and is commonly used to assess cognitive and somatic
dimensions of depression occurring within two weeks of administration. The BDI-II has been
reported to have exceptional reliability and validity (Beck et al., 1997).
Million Behavioral Medicine Diagnostic (MBMD; Millon, Antoni, Millon, Meagher, &
Grossman, 2001): The MBMD is a 165-item, self-report, true/false questionnaire used to assess
the psychological factors that may influence the course of treatment of medically ill patients. It
contains 38 scales that tap into the following dimensions: response patterns, negative health
habits, psychiatric indications, coping styles, and stress moderators. The MBMD has been used
extensively in health psychology research, as well as clinically to help identify factors that may
impact health care delivery. The MBMD has demonstrated adequate reliability and validity
(Millon et al., 2001). The subscales of interest for this study were the Depression Scale, the
Dej ected Scale, and the Future Pessimism Scale, the predominant psychiatric indicator, coping
style, and stress moderator, respectively, in this patient population. Though these scales are
highly correlated, they have been shown to tap into unique dimensions of behavior and will,
therefore, be assessed independently.
The Depression Scale is one of five psychiatric indicators of the MBMD. This scale
focuses on the patient's cognitive and somatic state, as indicated by changes in appetite, feelings
of hopelessness, social isolation, anhedonia, self-deprecation, and a number of other depressive
symptoms. Examples of MBMD Depression Scale items include, "I've lost interest in things that
I used to find pleasurable" and "I have been having serious thoughts about suicide." Though
elevation on this scale does not warrant a conclusive diagnosis of clinical depression, as defined
by the Diagnostic and Statistical Manual of Mental Disorders--Fourth Edition Text Revision
(DSM-IV-TR; American Psychiatric Association, 2000), the scale provides supportive evidence
for a diagnosis of depression.
The Dejected Scale, one of the 11 coping styles subscales, is designed to identify patients
that are predisposed to pessimism and demonstrate marked inability to persevere in the face of
personal problems (e.g., medical diagnosis) as indicated by persistent and sometimes
characteristic disheartenment, hopelessness, and disconsolation. Sample items on this scale
include I spend much of my time brooding about things" and "My life has always gone from
bad to worse."
Finally, the Future Pessimism Scale assesses patients' present outlook toward their
prognosis and future health status. Research has shown this stress moderator to influence several
medical outcomes, including adherence to and confidence in medical recommendations,
emotional response to medical diagnosis, as well as disease course. Unlike the Depression and
Dej ected Scales, the Future Pessimism Scale is a relatively less global assessment of patient' s
response style, reflecting rather patient' s current response to a current medical diagnosis.
Sample items on this scale include "Life will never be the same again for me" and "My future
looks like it will be full of problems and pain."
Taken together, these subscales of the MBMD have vast implications for assessment of
patients' prognosis in the context of health maintenance (e.g., adherence to medical regimen) and
healthcare delivery (e.g., improving communication between patients and healthcare providers).
Charlson Comorbidity Index (CCI; Charlson, Pompei, Ales, & MacKenzie, 1987):
The CCI is a 17-item questionnaire designed to identify and classify comorbid conditions that
may alter the risk of mortality, or disease process. Comorbidity is defined as the presence of one
or more disorders, or diseases, in addition to a primary medical diagnosis. The measure indexes
diseases such as coronary artery disease (CAD), peripheral artery disease, cerebrovascular
disease, pulmonary disease, diabetes, and metastatic solid tumor, among others, which are
assigned a score based on severity (e.g., mild liver disease = 1; HIV/AIDS = 6).
Neuropsychological Assessment Instruments
In addition to psychological assessment measures, participants were administered several
neuropsychological tests to assess baseline cognitive status, including a brief assessment of
baseline mental status, using the Mini-Mental State Exam (MMSE), as well as intellectual
ability, using the Wechsler Abbreviated Scale of Intelligence (WASI). For the current study,
only the MMSE and the WASI will be discussed as they provide an index of global cognitive
function from which to match comparison groups.
Mini-Mental State Exam (MMSE; Folstein, Folstein & McHugh, 1975): The MMSE
provides a structured approach to mental status testing and screening for general cognitive
decline. It is comprised of 11 simple questions, yielding a maximum score of 30. The MMSE
was used to characterize general, global changes in cognitive function relative to temporal
orientation, verbal memory, attention, language, and visuoconstruction ability. Individuals with
MMSE score < 24 were excluded from the study.
Wechsler Abbreviated Scale of Intelligence (WASI; Psychological Corporation,
1999): The WASI is a short (approximately 30 minutes) and reliable measure of general
intelligence. It has four subtests: Vocabulary, Block Design, Similarities, and Matrix
Reasoning. Like other widely used Wechsler scales, the WASI is nationally standardized and
provides summary scores for Verbal IQ, Performance IQ, Two-subscale IQ and Full Scale IQ. A
Two-subscale IQ based on performance on the Vocabulary and Matrix Reasoning subtests was
used in the current study.
Outcome Variable--Anesthetic Sensitivity
The current investigation involved the measurement of anesthetic sensitivity, defined as an
individual's initial responsiveness to anesthesia from presurgical baseline to the intraoperative
anesthetic maintenance phase. Anesthetic sensitivity was measured intraoperatively using a
Bispectral Index Score (BISTM) mOnitor (Aspect Medical Systems Inc., MA), a digitally
processed electroencephalograph (EEG) parameter used to quantitatively measure hypnotic
depth of anesthesia (i.e., the direct effects of anesthetics on the brain cortex) during surgical
procedures. BIS is represented as a value between 0 and 100 and is calculated as a rolling
average of raw (i.e., artifact-free) EEG data, or the smoothing rate. BIS values generally fall in
the range of 96 to 100 for fully awakened individuals and falls variably as frontal wave activity
declines (i.e., in response to anesthetic induction). Standardized placement of the unilateral
BISTM Sensor for this protocol was across the participant' s left frontal lobe. Baseline BIS was
recorded immediately after the BISTM Sensor was mounted onto patients (i.e., before surgery) and
subsequent BIS were digitally recorded through the duration of the surgical intervention using
the 30-second smoothing rate (as opposed to the 15-second smoothing rate), which decreases
Data was abstracted from the BIS TM monitor and downloaded to a database for use in the
current analysis. For the purpose of the primary aim of this investigation, BIS was quantified as
'area over the curve' (AOC), or the difference between the total area and 'area under the curve'
(AUC) as conceptualized by Pruessner, Kirschbaum, Meinlschmid, and Hellhammer (2003), who
proposed two formulas for calculation of AUC. The current study employed the formula for
'AUC with respect to ground' (AUCG), in which individuals' responsiveness to anesthesia is
examined during the critical period defined as baseline to anesthetic maintenance, designated as
6.5 minutes post-anesthetic induction. Because variability in intraoperative factors increases
greatly during anesthetic maintenance, this cutoff was determined to be an acceptable threshold
to observe the effects of initial anesthetic induction as illustrated in Figure 3-2.
The psychological assessment measures used to assess mood in the current study (i.e., the
BDI-II and the MBMD) were hand-scored following scoring instructions provided in the
respective administration and scoring manuals. Raw scores for both measures were entered as
continuous variables in order to examine Aim I, with higher scores indicating increasing
symptom severity. The formula for calculation of 'area under the curve in respect to ground
(AUCG)' WAS used to estimate 'area over the curve (AOC)' (see Figure 3-3).
The statistical software package SPSS 14.0 for Windows (SPSS Inc., IL) was used to
conduct the statistical analysis for this research study.
To examine the relationship between presurgical depression and anesthetic sensitivity in an
at-risk population (i.e., gyn-oncology) regardless of group assignment, Pearson' s correlations
were used. Given the known neurological component of depression and the expansive research
on the impact of compromised cognitive/physiologic reserve on anesthetic responsiveness in
vulnerable populations, it was hypothesized that depression severity would be positively related
to greater sensitivity to anesthesia, as determined by AOC estimates.
Specific Aim II
To evaluate magnitude of anesthetic effects (i.e., responsiveness to initial anesthetic
induction) among individuals with and without a history of depressive symptomatology, group
comparisons were made using an independent samples t-test.
(N 7 6)
BIS Records (N 26)
Table 3-1. Participant characteristics by group--Means and standard deviations shown.
No History of History of Depressive Significance
Years of Education
IQ, Wechsler Abbreviated Scale of Intelligence (WASI; Psychological Corporation, 1999)
CCI, Charlson Comorbidity Index (Charlson et al., 1987)
Subj ect Recruitment and Screening (N = 76)
Admission to GCRC
(N = 43)2
Clinical Interview and
Consensus Conference (N 26)
History of Depressive
(N = 111
Figure 3-1. Study design flowchart.2
2 Seventeen possible participants were excluded from the current analysis. Thirteen participants were excluded
because their bispectral index score (BIS) records were invalid, inaccessible, or missing. Three participants did not
complete psychological measures. One participant did not meet the minimum criteria for MMSE score >24.
No History of Depressive
(N = 151
100 - - -
I I AUCG
0 0 1 2 3 4 5 6 7
TIME (in minutes)
Figure 3-2. Illustration of 'area under the curve with respect to ground' (AUCG) and 'area over
the curve' (AOC).
AOC7 = total area A UCG,
Pruessner et al., 2003.
Figure 3-3. Formulas for 'area under the curve with respect to ground' (AUCG) and 'area over
the curve' (AOC)
Independent samples t-tests confirmed group differences in mood in some, but not all of
the administered questionnaires. Table 4-1 shows the results of these independent samples t-
tests. Consistent with expectations, there were significant differences between the groups for
depression as measured by the Beck Depression Inventory--Second Edition (BDI-II), [t (24) = -
2.89, p < 0.05; r = .51], as well as the Millon Behavioral Medicine Diagnostic (MBMD)
Depression and Dejected Scales, [t (24) = -2.90, p < 0.01; r = 0.51] and [t (24) = -2.69, p < 0.05;
r = 0.48], respectively. These represent moderate effects. There were no significant differences
between groups, however, for the Future Pessimism Scale [t (24) = -.895, p = 0.380; r = 0.18].
Differences between groups for the somatic and cognitive indices of the BDI-II were also
detected (ps < 0.05) and are reported in Table 4-1. It is noteworthy that for all significant
differences, the group with a history of depressive symptomatology demonstrated a trend
towards significantly more depression at the mean level across measures. It should also be noted
that mean reports of depression on both the BDI-II and the MBMD did not reach clinical
significance for either group.
Specific Aim I: Relationship Between Depression and Anesthetic Sensitivity Independent
of Group Classification
Pearson's correlational analyses were conducted to assess the relationship between
depression and anesthetic sensitivity. Anesthetic sensitivity was measured with respect to
calculations of 'area over the curve' (AOC), which was mathematically derived from the 'area
under the curve with respect to ground' (AUCG) formula for each participant (Pruessner et al.,
2003; also, see Chapter 3, Methods, page 36). All variables of interest were relatively normally
distributed. It was hypothesized that depression severity would be positively related to greater
sensitivity to anesthesia, as determined by AOC estimates. Depression severity was
operationalized using scores on the BDI-II, as well as three scales of the MBMD (i.e.,
Depression, Dejected, and Future Pessimism). Results indicated an association between two of
the MDMD scales, the Depression and Future Pessimism Scales (ps < 0.05). Specifically, higher
reports of baseline presurgical depression (as measured by the MBMD Depression Scale; r =
0.443, p = 0.02) and greater pessimism towards current medical diagnosis (r = 0.474, p = 0.02)
were correlated with greater anesthetic sensitivity. See Figures 4-1 and 4-2 for visual
illustrations of these trends. There were no significant relationships found between depression
severity as measured by the BDI-II (r = 0. 122, p = 0.55 1) or the Dej ected Scale of the MDMD (r
= 0.141, p = 0.492) and anesthetic sensitivity.
Specific Aim II: Relationship Between Group Classification and Anesthetic Sensitivity
The relationship between group classification and anesthetic sensitivity was assessed using
an independent samples t-test with group classification as the independent variable and AOC
estimate for anesthetic sensitivity as the dependent variable. Greater AOC estimates were
predicted for the group with history of depressive symptomatology; stated differently, greater
responsiveness to the initial effects of anesthetic induction would be seen in the group with
history of depressive symptomatology. The independent samples t-test failed to rej ect the null
hypothesis. Explicitly, there was no significant group difference in AOC estimates. AOC
estimates were no different for participants without a history of depressive symptomatology (M~=
527.26, SD = 105.42) as compared to those with a history of depressive symptomatology (M~=
569.47, SD = 127.73), [t (24) = -0.923, p = 0.37] (see Figure 4-3).
A correlational analysis was conducted to examine possible factors that might contribute to
the effect of depression on anesthetic response. Age, comorbid illness (as measured by the
Charelson Comorbidity Index, CCI), and pathology status (i.e., whether tumor was benign or
malignant) were examined. None of these possible covariates were related to the outcome
variable of AOC (all ps > 0.05; see Table 4-2). These results, therefore, preclude examination of
the impact of patient factors such as age, the presence of comorbid illness, and pathology status
on group differences in AOC at this time. Possible reasons for the lack of group differences in
anesthetic sensitivity are addressed in the next chapter.
Table 4-1. Means and standard deviations for psychological assessment measures.
Psychological Assessment No History of History of Significance
Measures Depressive Depressive
(N= 15) (N= 1 )
BDI-II (Total) 7.0 (5.5) 19.7 (13.8) p =.013
BDI-II (Somatic) 4.9 (3.1) 8.5 (4.7) p =.025
BDI-II (Cognitive) 2.1 (2.9) 11.2 (9.6) p =.011
MBMD Depression 30.8 (23.7) 59.9 (27.3) p = .008
MBMD Dej ected 10.5 (18.9) 43.6 (37.6) p = .018
MBMD Future Pessimism 48.3 (25.4) 56.9 (22.8) ns
Table 4-2. Correlation matrix for AOC and hypothesized covariates.
Age CCI Total Pathology AOC
Age 1.00 0.288 0.383 0.052
CCI Total 0.288 1.00 0.730** -0.361
Pathology Status 0.383 0.730** 1.00 -0.023
AOC 0.052 -0.361 -0.023 1.00
**Correlation is significant at the 0.01 level (2-tailed).
CCI, Charlson Comorbidity Index (Charlson et al., 1987)
History of Depression
Too~oo Fit line for Total
bg O x
r = 0.443, p = 0.02
0.00 20.00 40.00 60.00 80.00 100.00
MBMD Depression Scale
Figure 4-1. Relationship between MBMD depression scores and anesthetic sensitivity (AOC).
History of Depression
o X yes,,,
5 00.00-1 O
1 I I
MBMD Future Pessimism Scale
Figure 4-2. Relationship between MBMD future pessimism scores and anesthetic sensitivity
527.26 + 105.42
569.47 + 127.73
History of Depressive Symptomatology
Figure 4-3. Relationship between group classification and anesthetic sensitivity (AOC).
Note: Error bars represent standard error of the mean.
The present study examined two aims. The first aim examined the relationship between
presurgical depression severity and anesthetic sensitivity in a group of women undergoing
surgery for the removal of gynecologic tumors. Given the known effect of anesthesia on the
frontal lobe (e.g., Grasshoff et al., 2005; McKechnie, 1992) and the association between
depression and altered frontal lobe activity (e.g. Davidson, 1998), it was hypothesized that
individuals with presurgical depression would be more sensitive to anesthesia. That is, these
individuals would demonstrate a greater decline in their frontal lobe EEG frequency, as
measured by a Bispectral Index monitor (BIS; Aspect Medial Systems Inc., MA) immediately
following anesthetic induction. For the present investigation, an 'area over the curve' (AOC)
algorithm was used to quantify EEG change from a pre-anesthesia baseline to 6.5 minutes post-
The second aim examined whether anesthetic effects would differ among individuals with
and without a history of depressive symptomatology. It was hypothesized that there would be
group differences in response to anesthesia such that individuals with a history of depressive
symptomatology would demonstrate greater sensitivity to initial anesthetic induction.
Summary and Interpretation of the Results
Specific Aim I
The hypothesized positive relationship between presurgical severity of depression and
anesthetic sensitivity was supported by two of the four depression scales. Depression severity
was operationalized using scores on three scales of the MBMD (Millon et al., 2001l)--the
Depression, Dej ected, and Future Pessimism Scales--as well as the BDI-II (Beck et al., 1997).
Results provided some support for an association between depression and anesthetic sensitivity.
Specifically, participants' self-reports on the Depression and Future Pessimism Scales of the
MBMD were related to anesthetic sensitivity. This pattern was not observed, however, when
assessed with the BDI-II and the Dej ected Scale of the MBMD. Thus, although all four
measures were highly correlated, only data from the Depression and Future Pessimism Scales
(MBMD) supported the proposed hypothesis.
There is some indication that Eindings may be at least partially attributable to scale
differences. Compared to the BDI-II, the MBMD Depression Scale is a subtler, less face valid
measure of patient mood status. It provides a more global picture of a patient' s mood (Millon et
al., 2001); and, unlike the BDI-II, it represents a personality style, in addition to tapping into
acute symptoms of depression. Compared to even the other indices of depression on the
MBMD, the Depression Scale focuses on the patient' s mood state (e.g., decreased appetite,
discouragement, anhedonia), with particular sensitivity to characteristic signs of depression.
Examples of MBMD Depression Scale items include, "I've lost interest in things that I used to
Eind pleasurable" and "I have been having serious thoughts about suicide." Similarly, the Future
Pessimism Scale of the MBMD also provides an assessment of patient' s outlook towards current
medical diagnosis. In fact, previous research has shown this stress moderator to influence
several medical outcomes, including disease course (Millon et al., 2001). Sample items on this
scale include "Life will never be the same again for me" and "My future looks like it will be full
of problems and pain."
These Eindings are very promising. Though results were measure-specifie, the observed
association between higher scores on the MBMD Depression and Future Pessimism Scales and
increased anesthetic sensitivity suggests that these measures may discriminate those who are at
greatest risk for anesthesia-related complications. Why depression may relate to anesthetic
sensitivity could be explained by anatomical differences (i.e., of the frontal cortex) in those who
report greater severity of depressive symptomatology. Indeed, those who report greater
depression may likely evidence increased vulnerability to anesthesia, which acts on the frontal
lobe. This may have important implications for future research in the area of anesthetic
sensitivity, which will be addressed in the following section.
Specific Aim II
Results of the secondary analysis did not provide evidence to support the second
hypothesis of the current study. Individuals who were classified as "depressed" based on
interview information did not demonstrate a greater responsiveness to anesthesia when compared
to "non-depressed" individuals. This may be partially explained by intragroup variability in
AOC estimates, as well as sample size limitations. As Figure 4-3 illustrates, there was much
overlap between the groups in terms of AOC, with the group with a positive history of
depression showing much more variability in AOC.
The fact that there is a relationship between some indices of depression and anesthetic
sensitivity would suggest that individuals with a history of depressive symptoms would
demonstrate greater sensitivity to anesthesia. Indeed, a number of factors could contribute to the
aforementioned relationship (i.e., between depression severity and anesthetic sensitivity). The
cerebral reserve literature (e.g., Stern, 2002; Satz, 1993), for instance, would suggest that factors
such as age, education, intelligence (IQ), and comorbidity could account for group differences in
the outcome. An exploratory analysis evaluating the relationship of the outcome variable,
anesthetic sensitivity, with the aforementioned covariates did not reveal any significant
relationships. Although education was found to be significantly different between groups with or
without a history of depression (with the group having a history of depression being less
educated), the lack of a relationship between education and the outcome variable suggests that it
is not a significant contributor to the observed relationship between depression severity and
anesthetic sensitivity. This strengthens the finding by allowing us to attribute anesthetic
sensitivity to depression, and possibly reduced frontal activity in the brain.
Still, the lack of group differences in anesthetic sensitivity warrants attention. Specifically,
the variability in AOC estimates in the group with a history of depressive symptoms needs to be
addressed. One possible explanation is the composition and size of the group with a history of
depression. Few of the participants classified in this group actually reported clinically significant
depression (i.e., scale scores >75) when the MBMD was administered. The criteria applied in
the consensus conference to classify participants into the groups with and without a history of
depression were very sensitive. Considerations included current symptomatology, previous
history of depressive symptoms, and formal diagnosis and/or treatment (i.e., therapy and /or
medication) for clinical depression. Despite the attention to multiple factors in making group
assignments, reports of current symptomatology within the group with a history of depression
were variable. This suggests that differences in response to anesthesia may be manifested
differentially among those that have a history of sub-clinical depression versus those with a
history of severe depression. That is, there may be within-group differences in anesthetic
Though participants in each group endorsed levels of current depressive symptomatology
on the depression measures commensurate with their classification, the consensus conference
method was imperfect. Classification of participants may have been confounded, in some cases,
by limited evidence for classifying participants in one or the other group. For example, for a
3 Of the 11 participants in the group with a history of depression, 3 participants reported clinically significant levels
of depression as measured by the MBMD Depression Scale; and 1 participant reported clinically significant levels of
depression as measured by the Future Pessimism Scale.
subgroup of participants who did not complete a full clinical interview (i.e., with detailed query
of psychological history), the consensus was based on available medical records, which generally
favored a classification into the group with no history of depression. The possibility of
misclassification, in addition to variability in depression and sample size limitations, may have
played a significant role in the current findings.
Implications and Relevance to the Current Literature
The results of the present study evaluating the predictive value of presurgical depression
on anesthetic sensitivity have great implications and relevance to the current literature. Anxiety
and depression have been previously shown to affect anesthetic responsiveness. One study
showed that patients with higher preoperative anxiety required more intraoperative anesthetic
than patients with lower baseline preoperative anxiety (Maranets & Kain, 1999). A meta-
analysis (Dickens, McGowan & Dale, 2003) examining the impact of patient depression on
experimental pain perception suggests that depressed patients may have a lower threshold for
pain than non-depressed patients, and therefore require increased doses of anesthetic drugs to
compensate for that effect. Nonetheless, these studies have been limited in scope; namely, they
have not addressed the independent impact of depression on anesthetic response.
Other lines of research have, however, laid the foundation for the current investigation,
which proposes a model linking depression and anesthetic sensitivity via the conceptual
framework of the literature linking depression to asymmetrical activation of the frontal cortex.
To resummarize, general anesthesia results in suppression of frontal lobe activity, a process that
has been referred to as "depth of anesthesia" (Bruhn et al., 2006), or anesthetic depth. Previous
research has shown that greater anesthetic depth may be a clinically important predictor of
increased incidence of 1-year mortality among non-cardiac surgical patients (Monk et al., 2005).
However, there is little research on the predictors of anesthetic depth. It has been hypothesized
that patients who have less physiologic reserve may be more susceptible to the depressant effects
of anesthesia (Muravehick, 1998), and may therefore experience greater anesthetic depth and
possibly greater anesthesia-related outcomes. Premorbid patient factors that are associated with
suppressed frontal lobe activity may heighten risk for greater anesthetic depth. Depression, for
example, has previously been associated with reduced frontal activity (e.g., Davidson, 1998).
Therefore, depression may compromise reserve and heighten risk for greater anesthetic depth
among individuals undergoing surgery; hence, the strength of the present study.
Results from the primary aim of the current investigation partially support the role of
depression in response to anesthesia. Indeed, it is possible that there are other factors that may
mediate the relationship between anesthetic sensitivity and adverse intraoperative and
postoperative outcomes. However, depression can negatively impact at-risk individuals by
increasing risk for or complicating the course of cancer and its treatment and even speeding the
progression of the disease (Katon & Sullivan, 1990). As the results of the primary aim indicate,
in order to adequately assess the relationship between stressful life events conceptualizedd as the
combination of physical, environmental, emotional, and psychosocial variables),
physiologic/cognitive reserve, and prognosis, depression should be routinely considered as a
marker of increased vulnerability. Considering the prevalence and impact of depression on
patients with gynecologic tumors, including those with imminent cancer diagnoses, as well as the
sensitivity of the MBMD in detecting depression in medical populations, the current study is an
important and necessary addition to our clinical knowledge and practice. Specifically, it has vast
implications for interventions that consider depressive symptoms in presurgical assessments.
Limitations of the Present Study
Several methodological limitations are noted for the present study. As previously
mentioned, anesthetic sensitivity was measured using 'area over the curve' (AOC), a term
mathematically derived from the formula for 'area under the curve' (AUC), which is commonly
used to measure physiological or endocrinological changes over time. Though this method is
problematic in that calculations of AUC (or any derivative, such as AOC) have not been
standardized (Pruessner et al., 2003), it was determined to be the best method to address the
current hypothesis. Further, the examination of anesthetic sensitivity in relation to depression is
relatively novel. In this case, 'area over the curve' (AOC) was calculated by subtracting 'area
under the curve with respect to ground' (AUCG) fTOm the total area. The rationale for using
AOC rather than AUC, which essentially provides the same information with respect to changes
in a physiologic phenomenon over time, related to ease of interpretation. Considering the
difference between changes in response to anesthesia compared to changes in cortisol levels, for
example, it seemed better to express findings as a positive relationship (e.g., higher depression
scores are related to greater AOC estimates, or anesthetic sensitivity) as opposed to an inverse
one (e.g., higher depression scores are related to lower AUC estimates).
Another issue in relation to using AOC estimates to measure anesthetic sensitivity is the
limited number of events (i.e., records of Bispectral Index scores) used to calculate AOC; stated
differently, the duration of time considered in the estimation of anesthetic sensitivity may have
been to short to observe the desired effect. Individuals' responsiveness to anesthesia was
examined during the critical period defined as baseline to anesthetic maintenance, designated as
6.5 minutes post-anesthetic induction. While extending this period would provide a more
accurate picture of anesthetic sensitivity, issues with variability in intraoperative factors, such as
medications administered, patient homeostatic status, procedures performed, and complications,
would likely confound our AOC estimates.
Also, use of BIS as an indicator of anesthetic depth has not been validated or established as
the gold standard measure of anesthetic depth (Bruhn et al., 2006). Recall, BIS is a
dimensionless EEG-derived value that utilizes a unilateral sensor (integrated from 3 or 4
electrodes) to obtain an electroencephalographic signal from the forehead (Bruhn et al., 2006). It
differs from the traditional EEG in that it provides a single variable that is derived from several
disparate descriptors of EEG (Bruhn et al., 2006). Though BIS is highly correlated with
behavioral assessments of depth of anesthesia (e.g., anesthetic awareness), caution should be
used when drawing conclusions about the ability ofBIS to assess EEG waves. Specifically,
caution should be used when using BIS to discriminate between depressed and non-depressed
individuals on the basis of a correlation between depression and reduced frontal activity in the
frontal cortex. This is particularly significant considering the research in this area has
traditionally employed the use of traditional EEGs, which typically use more electrodes (as in an
electrode cap). To provide a few examples, Reid, Duke and Allen (1998), Bruder and
colleagues (1997), and Henriques and Davidson (1991) used 27, 30, and 14 electrode sites,
Whether depression increases risk for anesthesia-related complications by increasing
sensitivity to anesthetic induction is still unknown. Though the relationship between depression
and anesthetic sensitivity was partially supported, we are unable to assume causality from a
correlational design. Further evaluation of this relationship is warranted. Indeed, a longitudinal
design may help clarify the long-term impact of depression on surgical outcomes. Also,
consideration of other potential covariates may be indicated.
Finally, to address a more operational limitation of the present study, the lack of significant
findings for a relationship between depression and anesthetic sensitivity across all the measures
used, as well as the failure to detect group differences, may be limited by the small sample size.
As previously mentioned, 17 of the 43 participants who consented to participate in additional
psychological and neurocognitive testing (i.e., as part of their enrollment in the concurrent
longitudinal study) were excluded from the current analysis. The primary reason for exclusion
was invalid, inaccessible, or otherwise missing BIS data. Some systematic factors that may have
contributed to the loss of this data are being considered. Indeed, the current analyses may have
been enhanced by a larger sample. However, the current findings still highlight the need to
identify patients at-risk for adverse intraoperative and postoperative outcomes, which may have
vast implications for improving patient care before, during, and after surgical interventions.
Directions for Future Research
Again, results of the present study suggest that depression may be an important marker of
anesthetic sensitivity. More research is needed to evaluate this relationship, as well as to identify
other premorbid indices of risk for adverse outcomes. Some possibilities may include patients
with reduced presurgical frontal function (e.g., as measured by neuropsychological assessment),
dementia, mental retardation, or neurological damage (i.e., to the prefrontal cortex of the brain).
In fact, there has been some research to suggest that reduced frontal-specific abilities, such as
working memory and higher order problem solving, is associated with general cognitive slowing
in these populations (Devenny et al., 2000; Jelic et al., 2000; Lindal, 1990; Numminen et al.,
2001; and Sinanovic et al., 2005). Similar to studies linking depression to reduced frontal
activity, these studies have, for the most part, used EEGs to ascertain these relationships.
Furthermore, there is a need to validate the research linking depression to neuroanatomical
abnormalities in the frontal cortex of the brain in the present population. Confirming that
depressed individuals are more susceptible to anesthetic effects because of their predisposition to
reduced frontal activity is an important addition to the current literature and a likely next step.
This can be achieved by obtaining presurgical EEG profiles for each participant.
Additionally, the current findings suggest that future research could incorporate findings
from research examining the physiological and neurological components of depression. For
example, researchers might investigate the relationship between cortisol levels and depression,
among other possible physiological or psychological stressors (e.g., stress, anxiety), and their
combined impact on anesthetic sensitivity. This is based on previous research that has linked
depression to dysregulated cortisol across populations, including cancer (Cohen et al., 2001;
Sephton et al., 2000). Thus, examining the relationship between depression and cortisol in this
sample may have significant implications for understanding how the two factors may moderate
individuals' anesthetic response.
Summary and Conclusion
In sum, the present study examined the relationship between depression and anesthetic
sensitivity in a group of women, age 40 and older, undergoing surgery for the removal of
gynecologic tumors. The first aim tested the hypothesis that depression severity, as assessed by
four independent measures of depressed mood, would demonstrate greater sensitivity to initial
anesthetic induction. Further, it was hypothesized that there would be group differences in
anesthetic response, with women in the history of depressive symptomatology group
demonstrating relatively more anesthetic sensitivity. Results provided some evidence for a
relationship between depression severity and anesthetic sensitivity; however, the group
difference hypothesis was not supported. One possible explanation for this discrepancy is that
the depression-anesthetic sensitivity link is measure-specific. Specifically, the measures that
were correlated with anesthetic sensitivity seem to be more sensitive to the assessment of current
The present study is an important first step in examining premorbid factors that may
influence anesthetic response, and thereby, contribute to adverse intraoperative and postoperative
outcomes. From the literature, it is clear that examination of risk factors such as depression may
be useful in identifying individuals who are at increased risk for negative outcomes associated
with anesthesia. Although correlational analyses will not provide causal evidence for the
relationship between depression and anesthetic sensitivity, the current study represents a
significant movement towards identifying areas for clinical intervention at the preoperative,
intraoperative, and postoperative levels. For example, the MBMD Depression Scale, one of the
measures that demonstrated sensitivity to identifying individuals at increased risk for negative
anesthetic response, is an invaluable assessment tool that has vast implications for moderating
factors that may complicate or undermine treatment efforts. Needless to say, the current study
emphasizes the need for interdisciplinary efforts in prevention and intervention in this patient
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Rachel Andre was born and raised in Miami, FL. She is a Phi Beta Kappa graduate of
Howard University in Washington, D.C., where she earned a Bachelor of Science in psychology.
Her minor area of concentration was chemistry. Ms. Andre is currently pursuing her doctorate in
clinical psychology at the University of Florida, specializing in health psychology. Current
clinical and research interests are in the area of obesity research and treatment, culture and body
image, as well as the psychosocial impact of health problems at the individual and community
levels. Areas of particular interest to Ms. Andre are those that have vast public health
implications (e.g., sexually transmitted diseases such as HIV/AIDS and HPV; obesity).