<%BANNER%>

Presurgical Depression and Anesthetic Sensitivity in Women Undergoing Surgery for the Removal of Gynecological Tumors


PAGE 1

1 PRESURGICAL DEPRESSION AND ANESTHETIC SENSITIVITY IN WOMEN UNDERGOING SURGERY FOR THE REMOVAL OF GYNECOLOGICAL TUMORS By RACHEL ANDR A THESIS PRESENTED TO THE GRADUATE SCHOOL OF THE UNIVERSITY OF FLOR IDA IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF SCIENCE UNIVERSITY OF FLORIDA 2007

PAGE 2

2 2007 Rachel Andr

PAGE 3

3 To my God, who reminds me daily that life is my stage, and I am performing for an audience of One

PAGE 4

4 ACKNOWLEDGMENTS I would like to thank my advi sors, Catherine Price and Deidre Pereira, for their time, support and advisement. I would also like to tha nk 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 additi on, I would like to thank Kerri Krieger for her integral role in the data collection process. Mo st of all, I would like to thank my family and friends for their encouragement, love, and prayers.

PAGE 5

5 TABLE OF CONTENTS page ACKNOWLEDGMENTS...............................................................................................................4 LIST OF TABLES................................................................................................................. ..........7 LIST OF FIGURES................................................................................................................ .........8 ABSTRACT....................................................................................................................... ..............9 CHAPTER 1 INTRODUCTION..................................................................................................................11 Clinical Assessment of Dept h of General Anesthesia............................................................11 Quantification of Depth of Anesthesia............................................................................13 Clinical Significance of Depth of Anesthesia.................................................................14 Depression as a Possible Pr emorbid Marker of Risk..............................................................16 Depression and Frontal EEG...........................................................................................17 Depression in the Gynecologic Oncology Population.....................................................22 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 METHODS........................................................................................................................ .....30 Sample Characteristics......................................................................................................... ...30 Procedures and Assessment Instruments................................................................................31 Clinical Interview and Consensus Conference................................................................32 Psychological Assessment Measures..............................................................................33 Other Questionnaires.......................................................................................................35 Neuropsychological Assessment Instruments.................................................................35 Outcome VariableAnest hetic Sensitivity....................................................................36 Statistical Analyses..........................................................................................................37 Specific Aim................................................................................................................... .37 Specific Aim II................................................................................................................38 4 RESULTS........................................................................................................................ .......41 Specific Aim I: Relationship Between Depression and Anesthetic Sensitivity Independent of Group Classification..................................................................................41 Specific Aim II: Relationship Between Group Cl assification and Anesth etic Sensitivity....42

PAGE 6

6 5 DISCUSSION..................................................................................................................... ....47 Summary and Interpretation of the Results............................................................................47 Specific Aim I................................................................................................................. .47 Specific Aim II................................................................................................................49 Implications and Relevance to the Current Literature............................................................51 Limitations of the Present Study.............................................................................................52 Directions for Future Research...............................................................................................55 Summary and Conclusion.......................................................................................................56 LIST OF REFERENCES............................................................................................................. ..58 BIOGRAPHICAL SKETCH.........................................................................................................64

PAGE 7

7 LIST OF TABLES Table page Table 3-1. Participant charac teristics by groupMeans and st andard 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

PAGE 8

8 LIST OF FIGURES Figure page Figure 1-1. Proposed model showing the major a ssociations conceptualized in the present study.......................................................................................................................... .........27 Figure 3-1. Study design flowchart............................................................................................ ...39 Figure 3-2. Illustration of area under the curve w ith respect to ground (AUCG) and area over the curve (AOC).......................................................................................................40 Figure 3-3. Formulas for area under th e curve with respect to ground (AUCG) and area over the curve (AOC).......................................................................................................40 Figure 4-1. Relationship between MBMD depr ession scores and anesthetic sensitivity (AOC).......................................................................................................................... ......44 Figure 4-2. Relationship between MBMD future pessimism scores and anesthetic sensitivity (AOC).......................................................................................................................... ......45 Figure 4-3. Relationship between group classi fication and anesthetic sensitivity (AOC)...........46

PAGE 9

9 Abstract of Thesis Presen ted 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 By Rachel Andr May 2007 Chair: Catherine Price Major: Psychology The present investigation examined the role of presurgical depression on anesthetic sensitivity. Based on theories of depression, fr ontal activity and anesth etic mechanisms, it was hypothesized that presurgical depression may place an individual at risk for greater responsiveness to initial anesthetic induction. Fu rther, it was hypothesized that individuals with a history of depressive symp tomatology would demonstrate gr eater sensitivity to initial anesthetic induction. Twenty-six women between the age of 40 and 81 years ( M/SD = 58.9/10.9) planning surgery under general anesthesia for the remova l of gynecologic tumors completed measures of current depression one day before surgery. Th e measures used were the Beck Depression Inventory-Second Edition (BDI-II) and three s cales of the Millon Behavioral Medicine Diagnostic (MBMD)the Depression, Dejected, and Future Pessimism Scales. A preoperative health screening was used to cl assify women with (N = 11) or without (N = 15) a history of depression. Anesthetic sensitivity was quantifie d as an individuals cumulative response to anesthetic drugs during the initial anesthetic induction phase and asse ssed with a unilateral frontal lobe EEG index derive d from a bispectral index (BIS) monitor. The dependent

PAGE 10

10 variable, anesthetic sensitivity, was quantified using an area over the curve (AOC) estimation based on individuals responses to anesthetic induction. Higher reports of baseline presur gical depression were correlat ed with greater anesthetic sensitivity as measured by the MBMD Depression and Future Pessimism Scales ( r = 0.443 and 0.474, respectively, p s < 0.05). However, there was no re lationship found between the BDI-II or the MBMD Dejected Scale and anesthetic sens itivity. Further, there was no evidence of differences in anesthetic sensitivity among indivi duals with and without a history of depression. These preliminary findings suggest that increasing levels of current presurgical depression may influence anesthetic sensitivity as define d 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 anesthesia).

PAGE 11

11 CHAPTER 1 INTRODUCTION General anesthesia results in immobility, loss of consciousness, and reduced electrical activity in the brain (G rasshoff, Rudolph, & Antkowiak, 2005; McKechnie, 1992). In particular, anesthesia is known to suppress fron tal 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 1year mortality among non-cardiac surgical pa tients (Monk, Saini, Weldon, & Sigl, 2005). However, there is little research on the predicto rs 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 e ffects of anesthesia (M uravchick, 1998), and may therefore experience greater anesthetic depth an d 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). Theref ore, depression may compromise reserve and heighten risk for great er anesthetic depth am ong individuals undergoing surgery. The following review will examine the c linical assessment of an esthetic depth, explore the relationship between depressi on and frontal-specific brain f unction, and provide a rationale for examining response to anesthesia in women undergoing surgery for the removal of gynecologic tumors. 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 sy stem, producing somewhat of a suppressor effect

PAGE 12

12 (Grasshoff et al., 2005). These ar e the spinal cord, the cerebral cortex, and the brain-reticular activating system, which result in immobility an d 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 fact ors. The effectiveness of gene ral 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 populati ons (i.e., those with advanced age or endorgan impairment, the terminally-ill, and thos e with otherwise compromised cognitive or physiologic reserve) tend to have an exagge rated drug effect from an average dose of anesthetic, and therefore require an adjustment in the dose of anesth etic to achieve a standardized depth of anesthesia (Muravchick, 1998). Indee d, research has shown th at 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 imp act of patients presurgical comorbidities on postoperative outcomes. Comorbid ities included severe mental a nd 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 reta rdation. Of 59 of 975 ge neral anesthesia cases

PAGE 13

13 that resulted in some complication, 32 cases had presurgical dementia (25 of which resulted in mortality). Further, patients with presurgica l cognitive impairments had an equal incidence of nonfatal complications as the su rgery patients as a whole. Quantification of Depth of Anesthesia Within the last few years, several monitors ha ve been developed to measure the effect of anesthetic drugs on cortical func tion (i.e., brain activity in the fr ontal 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 (BIS ) monitor is clinically most widely used. The bispectral index scor e (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 anes thesia) 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 cortic al suppression following anesthetic induction, a range of effects can be seen across indi viduals, drugs, and settings. For example, intraoperative BIS values may be exag gerated because of muscle activity (Messner, Beese, Romstock, Dinkel, & Tschaikowsky, 2003); baseline BIS values may be affected by neurological diseases (Renna & Venturi, 2000); a nd some anesthetics, most notably ketamine, do not cause dose-dependent BIS depression (Kel ley, 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 us ed. BIS values derived during a balanced anesthetic with a substantial opi oid component typically range fr om 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 techni ques (Kelley, 2003; Johans en, Sebel & Sigl, 2000;

PAGE 14

14 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 findings th at suggest an association between depth of anesthesia, measured as anesthetic drug eff ect on the brain cortex, and postoperative mortality within a year following surgery. These findings 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 (d efined 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 (BIS) monitor and sensors (Aspect Medical Systems Inc., MA) to quantify hypnotic depth of anesth esia. BIS data was recorded throughout the surgical intervention and digitized at 5-minute intervals. Anes thetic 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 conduc ted using Cox proportional hazards modeling to determine the independent and combined impact of anesthetic depth, comorbid illness, demographic factors (e.g., age, race), clinical hi story (e.g., tobacco or alcohol use, preoperative blood pressure), and intraoperativ e factors (e.g., surgical duration, intraoperative blood pressure) on risk for postoperative death. Results of this study indicated three vari ables as significant in dependent predictors of postoperative mortalityhypnotic depth of an esthesia (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 y ear after surgery was primarily associated with

PAGE 15

15 pre-existing comorbidities and hypotension, not su rprisingly, the finding relating anesthetic depth to increased mortality at one year garnered the most atte ntion. The primary criticism of this study was that is was not designed to inve stigate the relationship between intraoperative anesthetic management and long-term outcome, sugge sting incidental results at best. The use of a prospective observational method was particularly problematic in its failure to account for premorbid factors that might ha ve contributed to the adverse outcomes observed. Thus, the conclusions were confounded by the use of the BI S monitor as convincing evidence for the observed relationship without a pr iori methodological control for known comorbidities, surgical diagnoses, anesthetic drugs, intraoperative anes thetic management, or other factors generally associated with mortality. Nonetheless, these as sociations suggest that in traoperative anesthetic management may affect long-term outcomes more than previously appreciated, which has vast implications for preventativ e 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 cognitiv e dysfunction (POCD) at 1-week postsurgery, 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 findings, Rasmussen and colleagues concluded that the etiology of POCD, as well as the incidence of mortality, were likely multifactor ial rather than the result of anesthesia. In regard to the report of more deaths in th e general anesthesia group, the investigators acknowledged that their study was not desi gned to evaluate uncommon postoperative complications (e.g., mortality). Further, the st udy provided no conclusi ve evidence that long-

PAGE 16

16 term cognitive changes are caused by general anes thesia. Still, other studies indicated minimal risks associated with anesthesia during the perioperative period (Arbous et al., 2001; Sigurdsson & McAteer, 1996). Thus, the role of anesth esia on postoperative outcomes is, indeed, controversial. While some studies found mini mal complications related to anesthesia (e.g., Rasmussen, 2003), others reported more signific ant outcomes related to anesthesia, to the greatest extent mortality (e.g., Monk et al., 2005). Indeed, contrary to the results of the aforem entioned studies, anesthesia-related mortality and complications may likely be explained by the interaction between anes thesia and premorbid factors, such as comorbid conditions and gene tic or psychological fa ctors, rather than by anesthesia alone. Simply state d, baseline impairment across a va riety 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 possibl e influence of premorbid patient factors on anesthetic responsiven ess is warranted. Depression as a Possible Premorbid Marker of Risk Anxiety and depression are ps ychological factors known to affect the response to anesthetic drugs. For instance, patients with higher baseline preopera tive 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 crosssectional study of 57 women unde rgoing bilateral laparoscopic tubal ligation, a differential response to anesthesia was demonstrated in groups low, moderate, and high on trait (i.e., characteristic) anxiety. These e ffects were seen for anesthetic induction, as well as maintenance, using the Aspect A1000 BIS monitor to control hypnotic depth of anesthesia. In regard to depression, a recent meta-analysis (Dickens, Mc Gowan & Dale, 2003) reviewed the impact of

PAGE 17

17 patient depression on experimental pain percep tion. Findings suggest th at depressed patients may have a lower threshold for pain than nondepressed patients. This may have major implications for surgical interventions; name ly, 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 re search 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 l obes (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 examini ng the functional and structural ro le of the prefrontal cortices, anterior cingulate, amygdala and hippocampus in affect and emotion regulation (Davidson, Pizzagalli, Nitschke, & Putnam, 200 2). Of particular interest for the present study is the literature that has previously lin ked depression to abnormalities in electrical ac tivation of the prefrontal regions of the br ain (e.g., Davidson, Abercrombie, Nitschke, & Putnam, 1999; Davidson, 1998), which suggests that depression may be one possible risk factor for anesthesiarelated complications. The pred ictive value of depression for response to anesthesia has not, however, been evaluated. Previous research employing a variety of me thods (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, part icularly of the frontal region of the brain. The following

PAGE 18

18 review will focus on research that has employe d the use of multi-site electroencephalographs (EEG) to make inferences about patterns of re gional cortical activa tion 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 th at the left hemisphere is invol ved in depression (e.g., Black, 1975; dElia & Perris, 1973, 1974; Gainotti, 1972; Gaspa rrini, Satz, Heilman, & C oolidge, 1978; Perini & Mendus, 1984; Robinson, Kubos, Starr, Rao, & Pric e, 1984). In a comprehensive review of this literature, Drevets (1998) not ed that several studies provid ed evidence to support reduced frontal activation (with respect to alpha frequencies) of the prefr ontal cortex in patients with major depressive disorder. To be clear, there is an inverse re lationship between alpha power and region-specific activatio n (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 consiste nt findings have relate d increased alpha power to left frontal hypoactivation, or less left-sided activity (e.g., Be ll, Schwartz, Hardin, Baldwin, & Kline, 1998; Bruder et al., 1997; Davidson, Ch apman, & Chapman, 1987; Davidson, Schaffer, & Saron, 1985; Gotlib et al., 1998; Schaffer, Da vidson, & Saron, 1983). Fewer studies have demonstrated the opposite (i.e., increased alpha powe r associated with decr eases 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 exampl es, in the early 1990s, Henriques and Davidson

PAGE 19

19 conducted several investigations to examine the differential activation of prefrontal cortical regions among depressed and heal thy individuals. One of th ese studies examined whether asymmetrical activation of the pr efrontal cortex discriminated be tween 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 us ing region-specific elec troencephalography (i.e., examination of cortical symmetry) as a state-independent marker of vulnerability to future depression. A small sample (N = 14) of particip ants (with and without a history of depression) was evaluated in respect to emotional state (b efore and during the EEG protocol), as well as brain activity (as measured by EEG using three re ference points computed from 14 electrodes). Although power in all frequency bands was examin ed, results were only significant for alpha power, which is consistent with mo st literature in this area. Findings showed participants with a histor y of depression demons trated asymmetrical activation in the direction of mo re alpha power, or less left front al and right posterior activation as compared to never-depressed control participants. Because the sample differed only in their history of depression (i.e., patie nts were carefully matched on several demographic variables, including age, gender, and socioeconomic status and there were no sign ificant differences in self-reported depression, emotional state, or medication history), these results suggest EEG is a reliable state-independent mark er of depression history, which they proposed had implications for the prediction of future psyc hopathology or vulnerability to aff ective 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, 1998).

PAGE 20

20 In another study, Henriques and Davidson (1991) sought to demonstrate differences in leftsided frontal activation among depr essed and never-depressed controls with specific attention to the midfrontal and parietal re gions. Following a similar proce dure as the 1990 investigation, a small sample (N = 28) was evaluated. Patients w ith a history of depression (all of whom also met research criteria for current depression) dem onstrated left frontal hyp oactivation (i.e., more left-side alpha power, or less front al activation) in the midfrontal region. Group differences were not detected in the parietal re gion. These findings support, at l east partially, the investigators contention that cortical activati on differs during approachand wit hdrawal-related behavior, such that depressed individuals, who are more likely to demonstrate withdrawal-related behaviors (e.g., loss of initiative, difficulty concentra ting, indecisiveness, hope lessness), will also demonstrate decreased left frontal activation. While many studies have replic ated findings demonstrating re duced left relative to right activation in depressed indi viduals (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 findings are variab le. For example, in addition to discussing the inconsistencies in the literature, Reid and colleagues (1998) fa iled to support their hypotheses that there would be region-specific group differences (here, mid-frontal and lateralfrontal regions) in regard to alpha activity (Study 1) or that this relationship would be appa rent in a range of depressed individuals (Study 2). In the first study, they hypothesized th at their depressed group would exhibit reduced left frontal activ ation relative to non-depressed cont rols. Results did not reveal group differences in those regions. They did, however, show diffe rences in the parietal region. Further, among a sample of depressed individua ls (Study 2), asymmetry was not related to

PAGE 21

21 depression severity. These findi ngs 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 th ese observations. One methodological difference, which appears to have had a significant influence on the findings, was the length of EEG recordings employed in the presen t study (8 min) comp ared to others (30 sec to 1 min). In fact, decomposition of intervals of EEG recordings into shorter blocks (2 min), revealed group differences commen surate to previous findings. In sum, research conducted within the la st 25 years has extensively illustrated the relationship between generalized sl owing in the prefrontal cortex (i.e., asymmetrical activation of frontal regions of the brain) a nd depression. Despite the complexi ty of this literature and the variable findings, these studies have advanced our understandi ng of the neurological basis of depression. Indeed, use of electro encephalography to make inferen ces about patterns of regional cortical activation in the brain has significant implications for mediation of various outcomes (e.g., identification of indi viduals at risk for future depressi on). Though the relationship between cortical activity and depression has been largely substantiated in the liter ature, no attention has been directed towards implications for medical outcomes. For example, one can surmise that depressed individuals (who are predisposed to re duced frontal activation) may be particularly sensitive to anesthesia, which has a suppressing effect on the frontal co rtex. 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 pa rticular importance in populations wher e depression is at least marginally prevalent.

PAGE 22

22 Depression in the Gyneco logic 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 circad ian rhythmicity of cortisol s ecretion (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 psychosoc ial stress and cancer progression, especially among patients with more advanced cancers (Sepht on & Spiegel, 2003; Touitou et al., 1996). Depression, the second psychological stressor indicated in mort ality, has also been linked to dysregulated cortisol (Cohen, de Moor, Devi ne, Baum, & Amato, 2001), as well as to fatigue (Bower, Ganz, Dickerson, Petersen, Aziz, & Fahe y, 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 individu als by increasing risk for or complicating the course of cancer and its treatment and even speeding the progressi on 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 li terature, little has been done in the way of addressing the impact of depression on indivi duals with imminent cancer diagnoses (i.e., those who ar e awaiting a conclusive diagnosis of cancer). In most cases, cancer diagnosis is preceded by a series of clinical te sts to identify or assess the nature of clinical signs (e.g., presence of a tumor) and to determin e the severity of pathology. This can be a potentially stressful process. As in the case of cancers etiologically rela ted to an overgrowth of cells, surgical intervention to extract the tumor(s ) is often necessary. Such cases warrant an

PAGE 23

23 adequate evaluation of the rela tionship between stressful life events (conceptualized 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 clinic al signs but awaiting c onclusive diagnoses), prevalence rates for individuals with comorbid depression and a variety of cancer types have been estimated. For example, depression occu rs in 12 to 23% of patients with gynecologic cancers (Massie, 2004). This means that a s ubgroup 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 so me proportion of these women will eventually receive a diagnosis of cancer, it is reasonable to expect the in cidence of depression among them to be less than the upper limit of the range estim ated for women with definitive cancer diagnoses (i.e., <23%). To be more specific, the prev alence of depression am ong 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 consideration. Earlier, it was implied that depressed indi viduals might be particularly sensitive to anesthesia. This was based namely on the know n 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 significa ntly over the last few decades (i.e., following the advent of more sophisticated intraoperative monitoring and

PAGE 24

24 anesthetic management techniques), they are not uncommon, particularly among individuals who are more susceptible to the effects of anesthes ia (e.g., depressed individua ls). Though statistics do not indicate an enormous incidence of de pression among gynecologi c oncology patients (both with and without conclusive diagnoses), the incidence is large enough to merit attention. Particularly among patients awaiti ng 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 co mplicate the course of treatm ent. Thus, examination of depression as a premorbid risk factor for anesth esiarelated complicati ons can be useful in understanding differences in response to anes thesia, which ultimately has implications for prevention and intervention. Purpose of the Present Study The present study purposed to dr aw a conceptual link betwee n depression, brain function (i.e., electrical activity in the frontal lobe), and depth of anesthesia. The former literature review sought to achieve the following objectives: (a) to define depth of anesthesia and explore how it has been quantified in previ ous research, (b) to examine and summarize the large body of literature linking depression to asy mmetrical activation of the frontal cortex, and (c) to provide a rationale for examining response to anesthesia in women undergoing surger y for the removal of gynecologic tumors. Despite the sufficient evidence available to propose a model linking the findings of the aforementioned areas, the impact of depression on a variety of in traoperative factors has been largely overlooked. In fact, the vast majority of research in areas of c linical interest, including postoperative cognitive dysfuncti on (POCD) and anesthetic awar eness, has only addressed the psychological impact of these complications (e.g., post-traumatic stress disorder following anesthetic awareness), often glazing over or negl ecting the preoperative pi ece (i.e., the impact of

PAGE 25

25 comorbid disorders, as well as latent psychosoc ial factors such as a pre-existing history of depression). So, although previ ous research has shown an incr eased incidence of postoperative depression attributable to pain, complications of anesthesia, and other underlying causes across a variety of patient populations (Elkins, Whitfield, Marcus, Sy mmonds, 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 be tween presurgical depression and anesthetic sensitivity. Introduction to Anesthetic Sensitivity No line of research has formerly or direc tly documented a relationship between depression and anesthetic sensitivity. This can be attribut ed 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 individuals cumulative response to anesthetic drugs (measured in the same way as depth of anesthesia usi ng digitized EEG derived from a patient state monitor) during the initial anesthetic induction pha se (refer to methods outlined in Chapter 3 for a more detailed explanatio n). To be clear, the present study represented the first attempt to examine the demographic, biological, and psycholog ical correlates of anesthetic sensitivity. Specifically, the purpose of the present study was to examine the relationship between presurgical depression and anesth etic sensitivity in an at-ris k 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.

PAGE 26

26 Participants were classified in to 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 preope rative, as well as intraoperative, interventions to minimize associated outcomes.

PAGE 27

27 Figure 1-1. Proposed model showing the major associ ations conceptualized in the present study. Asymmetrical Activation of the Frontal Cortex (EEG) Depression Anesthetic Sensitivity

PAGE 28

28 CHAPTER 2 STATEMENT OF PROBLEM The preceding review of literature provide d 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 anesthes ia (Bruhn et al., 2006), which may be a clinically important predictor of in creased incidence of intraoperative and postoperative complications. To the greatest extent, 1-year mortality among non-cardi ac 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 (Muravchick, 1998). As previously alluded, premorbid patie nt factors that are associated with suppressed frontal lobe activity, such as depressi on, may heighten risk for greater anes thetic depth (identified here as anesthetic sensitivity). Hence, the presen t study examined the impact of depression on anesthetic sensitivity in a sample of women und ergoing surgery for the removal of gynecologic tumors. No line of research has formerly or direc tly documented a relationship between depression and anesthetic sensit ivity. It is, indeed, a nove l concept. Here, anesthetic sensitivity was defined as an individuals cumulative response to anesth etic drugs during the init ial anesthetic induction phase. It was measured in much the same way as depth of anesthesia (using digitized EEG derived from a patient state mon itor) and was calculated with re spect to area over the curve (AOC) of BIS during the anesthetic induction ph ase (more on this in the following chapter). Further, this study assessed th e effect of history of depre ssion on anesthetic sensitivity by

PAGE 29

29 classifying participants into two depressed group s (depressed versus not depressed) based on an interview. The incidence of de pression among surgical patients (p articularly those who have or are at risk for cancer) is also t hought to be significant, and thus the effects of anesthesia on this sample was reasonably expected to be apparent. Although correla tional analyses do not provide causal evidence for the relationship between depr ession and anesthetic sensitivity, the current study might represent a significant movement towa rds identifying areas for clinical intervention at the preoperative, intraopera tive, and postoperative levels. The current study addressed the following specific aims: Specific Aim I To examine the relationship between presurgi cal 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 asso ciation between depression and reduced frontal activity (e.g. Davidson, 1998), it was hypothesized th at depression severity would be positively related to greater sensitivity to anesthesia. Specifically, it was predic ted that individuals who report more depressive symptoms prior to surger y would show greater responsiveness to initial anesthetic induction, measured as area over the curve (AOC). Specific Aim II To evaluate whether anesthet ic effects differ among indi viduals with and without a history of depressive symptomatology. It was hypothesized that there would be group differences in response to anesthesia. Specifi cally, it was predicted th at individuals with a history of depressive symptomato logy would demonstrate greater sens itivity to ini tial anesthetic induction, also measured as AOC.

PAGE 30

30 CHAPTER 3 METHODS Sample Characteristics 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 hys terectomy, bilateral salpingectomy/oophorectomy, exploratory laparoscopy, appe ndectomy, lymph node dissect ion/sampling, cytoreduction, appendectomy, omentectomy, and colectomy). Eleven of these women were identified as having a history of depressive sympto matology 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 depre ssion, and self-reported treatmen t for depressive symtomatology, including a history of an tidepressent use as determined by self -report and/or review of available medical records. The remaining ageand educ ation-matched participan ts were 15 women with no known history of depressive symtomatology. The following inclusion and exclusion criteria we re applied. Participants were required to be over the age of 40 and native En glish speakers. Also, participants were also required to score > 24 on the Mini-Mental State Exam (MMSE). Add itional exclusion criteria applied exclusively to fulfill research aims for the larger longitu dinal study included (a) severe cardiovascular compromise or an ejection fraction of < 20%, (b) need for regional anesthesia and/or emergency surgery, (c) malignant hyperthermia, (d) choline este rase deficiency, (e) po rphyria, (f) allergy to lidocaine, (g) inability to tolerate a normal dose of hypnotic during anesthetic induction (based on the clinical judgment of the attending anes thesiologist), and (h) conditions that would

PAGE 31

31 confound interpretation of neurocognitive tests such as blindness, severe hearing impairment, and brain metastases. Forty-three of the 76 women enrolled in th e larger longitudina l study consented to participate in additional psychol ogical and neurocognitive testing. Although all were eligible, 17 possible participants were excluded from the current analysis1. 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, includi ng 19 Caucasian participants, 4 African-American participants, one Hispanic participant, one Native-American participant, and one participant of Pacific Isla nd origin. There were no signif icant differences between the groups with and without a history of depressive symptomatology w ith respect to age [t (24) = 1.43, p = 0.17], intelligence (as measur ed by the Wechsler Abbrevia ted 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 vi a close collaboration w ith the scheduling staff of the UF-Shands Gynecologic Oncology Clinic a nd the principal investigators of the larger longitudinal study examining Anesthetic Depth a nd Mortality in this patient population. As part of this larger investiga tion, all patients were to have gyne cological surgery to identify, to 1 Thirteen participants were excluded because their Bisp ectral Index Scores (BIS) reco rds were invalid, inaccessible, or missing. Three participants did not complete psycholo gical measures. One participant did not meet the minimum criteria for MMSE score > 24.

PAGE 32

32 remove, and identify the pathology of gynecologi cal masses. During a routine examination and assessment for surgery, patients meeting study criter ia were identified and invited to participate in the study. Interested part icipants provided informed cons ent for participation following University of Florida Institutional Review Bo ard 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 pe rformed all procedures. See Figure 3-1 for an overview of the study design. Clinical Interview and Consensus Conference Participants underwent a presur gical 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, a nxiety, and other mood disorders was made. Participants endorsing a history of depression as defined by self-re port of current and/or past depressive symptomatology (but not exclusively current symptoma tology), diagnosis of clinical depression, and/or treatment for depressive symtomatology, including a history of antidepressent use or psychotherapy focused on addressing c linical depression were considered for classification in the history of depressive sy mptomatology group. In some cases, classification was made on the basis of findings from a revi ew of available medical records. Final determination of group classificat ion was made via consensus conf erence. Post hoc comparisons of groups were made on the basis of these classifications.

PAGE 33

33 Psychological Assessment Measures Several mood measures were administered to participants the day before surgery to assess baseline mood status, including the Beck De pression InventorySecond Edition (BDI-II) and the Millon Behavioral Medici ne Diagnostic (MBMD). Beck Depression InventorySecond 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 reliabili ty and validity (Beck et al., 1997). Millon Behavioral Medicine Diagnostic (M BMD; Millon, Antoni, Millon, Meagher, & Grossman, 2001): The MBMD is a 165-item, self-report, tr ue/false questionnaire used to assess the psychological factors that may influence the cour se of treatment of medically ill patients. It contains 38 scales that tap into the following dimensions: response patterns, negative health habits, psychiatric indications, c oping styles, and stress moderato rs. 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 de monstrated adequate reliability and validity ( Millon et al., 2001 ). The subscales of interest for th is study were the Depression Scale, the Dejected Scale, and the Future Pessimism Scale, the predomin ant psychiatric indicator, coping style, and stress moderator, re spectively, in this patient popul ation. Though these scales are highly correlated, they have been shown to tap into unique dimensions of behavior and will, therefore, be asse ssed independently. The Depression Scale is one of five psychiatric indicators of the MBMD. This scale focuses on the patients cognitive an d somatic state, as indicated by ch anges in appetite, feelings of hopelessness, social isolation, anhedonia, self-deprecation, and a number of other depressive

PAGE 34

34 symptoms. Examples of MBMD De pression Scale items include, Ive lost interest in things that I used to find pleasurable and I have b een 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 Associatio n, 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, an d disconsolation. Sample items on this scale include I spend much of my time brooding about things and M y 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 moderato r to influence several medical outcomes, including adherence to a nd confidence in medical recommendations, emotional response to medical diagnosis, as well as disease course. Unlike the Depression and Dejected Scales, the Future Pessimism Scale is a relatively less global assessment of patients response style, reflecting rather patients curre nt response to a curren t 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 im plications for assessment of patients prognosis in the context of health ma intenance (e.g., adherence to medical regimen) and healthcare delivery (e.g., improving communication be tween patients and healthcare providers).

PAGE 35

35 Other Questionnaires Charlson Comorbidity Index (CCI; Char lson, Pompei, Ales, & MacKenzie, 1987): The CCI is a 17-item questionnaire designed to id entify 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 (C AD), peripheral artery di sease, cerebrovascular disease, pulmonary disease, di abetes, and metastatic solid tumor, among others, which are assigned a score based on severity (e.g., m ild liver disease = 1; HIV/AIDS = 6). Neuropsychological Assessment Instruments In addition to psychological assessment measur es, participants were administered several neuropsychological tests to a ssess 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; Fo lstein, Folstein & McHugh, 1975): The MMSE provides a structured approach to mental stat us testing and screening for general cognitive decline. It is comprised of 11 simple ques tions, yielding a maximum score of 30. The MMSE was used to characterize general, global cha nges in cognitive function relative to temporal orientation, verbal memory, atte ntion, language, and visuoconstruc tion ability. Individuals with MMSE score < 24 were excluded from the study. Wechsler Abbreviated Scale of Intelligen ce (WASI; Psychological Corporation, 1999): The WASI is a short (approximately 30 mi nutes) and reliable measure of general intelligence. It has four subtests: Vocabulary, Block Design, Similarities, and Matrix

PAGE 36

36 Reasoning. Like other widely us ed Wechsler scales, the WASI is nationally standardized and provides summary scores for Verbal IQ, Performa nce 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 VariableAnesthetic Sensitivity The current investigation involved the measuremen t of anesthetic sensitivity, defined as an individuals initial responsiveness to anesthesia from presurgical baseline to the intraoperative anesthetic maintenance phase. Anesthetic sensitivity was measured intraoperatively using a Bispectral Index Score (BIS ) monitor (Aspect Medical Syst ems Inc., MA), a digitally processed electroencephalograph (EEG) paramete r used to quantitati vely measure hypnotic depth of anesthesia (i.e., the di rect effects of anesthetics on th e brain cortex) during surgical procedures. BIS is represented as a value be tween 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 individu als and falls variably as frontal wave activity declines (i.e., in response to anesthetic induc tion). Standardized placement of the unilateral BIS sensor for this protocol was across the part icipants left frontal lobe. Baseline BIS was recorded immediately after the BIS sensor was mounted onto patien ts (i.e., before surgery) and subsequent BIS were digitally recorded through the duration of the surg ical intervention using the 30-second smoothing rate (as opposed to the 15-second smoothing rate), which decreases variability. Data was abstracted from the BIS monitor and downloaded to a database for use in the current analysis. For the purpose of the primary ai m of this investigation, BIS was quantified as area over the curve (AOC), or the difference between the tota l area and area under the curve

PAGE 37

37 (AUC) as conceptualized by Pruessner, Kirsc hbaum, 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 res ponsiveness to anesthesia is examined during the critical period defined as base line to anesthetic maintenance, designated as 6.5 minutes post-anesthetic induction. Because va riability in intraopera tive factors increases greatly during anesthetic maintenance, this cuto ff was determined to be an acceptable threshold to observe the effects of initial anesthetic induction as illustrated in Figure 3-2. Statistical Analyses The psychological assessment measures used to assess mood in the current study (i.e., the BDI-II and the MBMD) were hand-scored followi ng scoring instructions provided in the respective administration and scor ing manuals. Raw scores for bot h 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 curv e in respect to ground (AUCG) was used to estimate area over th e curve (AOC) (see Figure 3-3). The statistical software package SPSS 14.0 fo r Windows (SPSS Inc., IL) was used to conduct the statistica l analysis for this research study. Specific Aim To examine the relationship between presurgical depression and anesthetic sensitivity in an at-risk population (i.e., gyn-oncol ogy) regardless of gr oup assignment, Pearsons correlations were used. Given the known ne urological component of depressi on and the expansive research on the impact of compromised cognitive/physiologic reserve on anesthetic responsiveness in vulnerable populations, it was hypot hesized that depression severity would be positively related to greater sensitivity to anesthesia, as determined by AOC estimates.

PAGE 38

38 Specific Aim II To evaluate magnitude of anesthetic effect s (i.e., responsiveness to initial anesthetic induction) among individuals with and without a history of de pressive symptomatology, group comparisons were made using an independent samples t-test.

PAGE 39

39 Table 3-1. Participant charac teristics by groupMeans and st andard deviations shown. IQ, Wechsler Abbreviated Scal e of Intelligence (WASI; Psychological Corporation, 1999) CCI, Charlson Comorbidity In dex (Charlson et al., 1987) 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 in valid, inaccessible, or missing. Three participants did not complete psychological measures. One participant di d not meet the minimum criteria for MMSE score > 24. No History of Depressive Symptomatology (N=15) History of Depressive Symptomatology (N=11) Significance Age 61.4 (9.9) 55.4 (11.6) ns Years of Education 13.6 (1.9) 11.3 (2.2) p = 0.016 IQ 109.2 (16.9) 95.1 (19.7) ns CCI Total 5.1 (2.3) 4.0 (2.8) ns Subject Recruitment and Screening (N = 76) Informed Consent Admission to GCRC Psychological/Neurocognitive Testing (N = 43)2 Clinical Interview and Consensus Conference (N = 26) No History of Depressive Symptomatology Group ( N = 15 ) History of Depressive Symptomatology Group ( N = 11 ) Surgical Intervention (N = 76) Abstraction of BIS Records (N = 26)

PAGE 40

40 Figure 3-2. Illustration of area under the curve w ith respect to ground (AUCG) and area over the curve (AOC). 1 1 ) 1 (2 ) (n i i i i Gt m m AUC GAUC area total AOC Pruessner et al., 2003. Figure 3-3. Formulas for area under th e curve with respect to ground (AUCG) and area over the curve (AOC) 001234 567 TIME (in minutes) 100 AOC AUCG BIS

PAGE 41

41 CHAPTER 4 RESULTS Independent samples t-tests confirmed group di fferences in mood in some, but not all of the administered questionnaires. Table 4-1 show s the results of these independent samples ttests. Consistent with expect ations, there were significant di fferences between the groups for depression as measured by the Beck Depr ession InventorySecond Edition (BDI-II), [ t (24) = 2.89, p < 0.05; r = .51], as well as the Millon Beha vioral 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 repres ent moderate effects. There we re 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 ( p s < 0.05) and are reported in Ta ble 4-1. It is notewort hy that for al l significant differences, the group with a hi story of depressive symptoma tology 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 Pearsons correlational analyses were conduc ted to assess the relationship between depression and anesthetic sensitivity. Anesthet ic sensitivity was measured with respect to calculations of area over the curve (AOC), wh ich 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 th at depression severity would be positively related to greater sensitivity to anesthesia, as determined by AOC estimates. De pression severity was

PAGE 42

42 operationalized using scores on the BDI-II, as well as three scales of the MBMD (i.e., Depression, Dejected, and Future Pessimism). Re sults indicated an asso ciation between two of the MDMD scales, the Depression and Future Pessimism Scales ( p s < 0.05). Specifically, higher reports of baseline presurgical depression (a s measured by the MBMD Depression Scale; r = 0.443, p = 0.02) and greater pessimism towa rds current medical diagnosis ( r = 0.474, p = 0.02) were correlated with greater anesthetic sens itivity. See Figures 4-1 and 4-2 for visual illustrations of these trends. There were no si gnificant relationships found between depression severity as measured by the BDI-II ( r = 0.122, p = 0.551) or the Dejected 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 cl assification and anesthetic se nsitivity was assessed using an independent samples t-test with group classi fication as the independe nt variable and AOC estimate for anesthetic sensitivity as the dependent variable. Greater AOC estimates were predicted for the group with history of depressi ve symptomatology; stat ed differently, greater responsiveness to the in itial effects of anesthetic inducti on would be seen in the group with history of depressive symptomatology. The indepe ndent samples t-test fa iled to reject the null hypothesis. Explicitly, ther e was no significant group differe nce in AOC estimates. AOC estimates were no different for participants w ithout a history of depr essive symptomatology ( M = 527.26, SD = 105.42) as compared to those with a hi story 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 examin e possible factors that might contribute to the effect of depression on anesthetic respons e. Age, comorbid illness (as measured by the Charelson Comorbidity Index, CCI), and patholo gy status (i.e., whether tumor was benign or malignant) were examined. None of these possi ble covariates were related to the outcome

PAGE 43

43 variable of AOC (all p s > 0.05; see Table 4-2). These results, therefore, preclude examination of the impact of patient factors such as age, the pr esence of comorbid illne ss, and pathology status on group differences in AOC at this time. Possibl e reasons for the lack of group differences in anesthetic sensitivity are addressed in the next chapter. Table 4-1. Means and standard deviati ons for psychological assessment measures. Table 4-2. Correlation matrix for AOC and hypothesized covariates. **Correlation is significant at the 0.01 level (2-tailed). CCI, Charlson Comorbidity In dex (Charlson et al., 1987) Psychological Assessment Measures No History of Depressive Symptomatology (N=15) History of Depressive Symptomatology (N=11) Significance 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 Dejected 10.5 (18.9) 43.6 (37.6) p = .018 MBMD Future Pessimism 48.3 (25.4) 56.9 (22.8) ns Age CCI Total Pathology Status 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

PAGE 44

44 700.00 600.00 500.00 400.00 Anesthetic Sensitivity (AOC) 100.00 80.00 60.00 40.00 20.00 0.00 MBMD Depression Scale Fit line for Total yes no History of Depression r = 0.443, p = 0.02 Figure 4-1. Relationship between MBMD depression scores and an esthetic sensitivity (AOC).

PAGE 45

45 80.00 60.00 40.00 20.00 MBMD Future Pessimism Scale 700.00 600.00 500.00 400.00 AOC=total area minus AUC Fit line for Total yes no History of Depression r = 0.474, p = 0.02 Figure 4-2. Relationship between MBMD future pessimism scores and anesthetic sensitivity (AOC).

PAGE 46

46 yes no History of Depressive Symptomatology 650.00 600.00 550.00 500.00 450.00 Anesthetic Sensitivity (AOC) Figure 4-3. Relationship between group classifi cation and anesthetic sensitivity (AOC). Note: Error bars represent standard error of the mean. 527.26 + 105.42 569.47 + 127.73

PAGE 47

47 CHAPTER 5 DISCUSSION The present study examined two aims. The fi rst aim examined the relationship between presurgical depression severity and anesthe tic 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 presurgi cal depression would be more sensitiv e to anesthesia. That is, these individuals would demonstrate a greater decline in their fr ontal lobe EEG frequency, as measured by a Bispectral Index monitor (BIS; Aspect Medial Systems Inc., MA) immediately following anesthetic induction. For the present inves tigation, an area ove r the curve (AOC) algorithm was used to quantify EEG change from a pre-anesthesia baseline to 6.5 minutes postanesthetic induction. The second aim examined whether anesthetic effects would differ am ong individuals with and without a history of depres sive symptomatology. It was hypot hesized that there would be group differences in response to anesthesia such that individuals with a history of depressive symptomatology would demonstrate greater se nsitivity to initial anesthetic induction. Summary and Interpretation of the Results Specific Aim I The hypothesized positive relatio nship between presurgical se verity of depression and anesthetic sensitivity was supporte d by two of the four depressi on scales. Depression severity was operationalized using scores on three sc ales of the MBMD (Millon et al., 2001)the Depression, Dejected, and Future Pessimism Scal esas well as the BDI-II (Beck et al., 1997). Results provided some support for an association between depression and anesthetic sensitivity.

PAGE 48

48 Specifically, participants self-reports on the De pression 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 Dejected Sc ale of the MBMD. Thus, although all four measures were highly correlated, only data fr om the Depression and Future Pessimism Scales (MBMD) supported the proposed hypothesis. There is some indication that findings 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 gl obal picture of a patients mood (Millon et al., 2001); and, unlike the BDI-II, it represents a pe rsonality 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 pa tients mood state (e.g., decreased appetite, discouragement, anhedonia), with particular sens itivity to characteristic signs of depression. Examples of MBMD Depression Scale items include, Ive lost interest in things that I used to find pleasurable and I have been having serious thoughts about suic ide. Similarly, the Future Pessimism Scale of the MBMD also provides an assessment of patients outlook towards current medical diagnosis. In fact, previous research has shown this stress moderator to influence several medical outcomes, includ ing 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 findings are very promising. Though re sults were measure-specific, 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

PAGE 49

49 sensitivity could be explained by anatomical differ ences (i.e., of the front al cortex) in those who report greater severity of de pressive symptomatology. Ind eed, those who report greater depression may likely evidence increased vulnerabil ity to anesthesia, which acts on the frontal lobe. This may have important implications fo r future research in the area of anesthetic sensitivity, which will be addr essed in the following section. Specific Aim II Results of the secondary an alysis did not provide ev idence to support the second hypothesis of the current study. Individuals w ho 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 limitati ons. As Figure 4-3 illust rates, 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 indi ces of depression and anesthetic sensitivity would suggest that individuals with a history of depressive symptoms would demonstrate greater sensitivity to anesthesia. In deed, a number of factors could contribute to the aforementioned relationship (i.e., between depressi on severity and anesthe tic sensitivity). The cerebral reserve lite rature (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 evaluati ng 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 gr oup having a history of depression being less educated), the lack of a relati onship between education and the out come variable suggests that it

PAGE 50

50 is not a significant contributor to the observe d 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 anesthe tic sensitivity warrants a ttention. Specifically, the variability in AOC estimates in the group with a history of depressive symptoms needs to be addressed. One possible explana tion is the composition and size of the group with a history of depression. Few of the participants classified in this group actually reported clinic ally significant depression (i.e., scale scores >75) when the MBMD was administered3. The criteria applied in the consensus conference to classify participants into the groups with and without a history of depression were very sensitive. Consideratio ns included current symptomatology, previous history of depressive symptoms and formal diagnosis and/or treatment (i.e., therapy and /or medication) for clinical depression. Despite th e 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 difference s 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 diffe rences in anesthetic sensitivity. Though participants in each group endorsed le vels of current depr essive symptomatology on the depression measures commensurate with their classification, the consensus conference method was imperfect. Classifica tion 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 depr ession, 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 th e Future Pessimism Scale.

PAGE 51

51 subgroup of participants who did not complete a fu ll clinical interview (i.e ., with detailed query of psychological history), the consensus was base d 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 st udy evaluating the predictive va lue of presurgical depression on anesthetic sensitivity have gr eat implications and relevance to the current literature. Anxiety and depression have been previously shown to affect anesthetic res ponsiveness. One study showed that patients with higher preoperative anxiety required more intraope rative anesthetic than patients with lower baseline preopera tive anxiety (Maranets & Kain, 1999). A metaanalysis (Dickens, McGowan & Dale, 2003) exam ining the impact of patient depression on experimental pain perception suggests that depr essed 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 inde pendent 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 activ ation of the frontal cortex. To resummarize, general anesthesia results in su ppression of frontal lobe activity, a process that has been referred to as depth of anesthesia (Br uhn 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

PAGE 52

52 that patients who have less physiologic reserve may be more susceptible to the depressant effects of anesthesia (Muravchick, 1998), and may theref ore experience greater anesthetic depth and possibly greater anesthesia-related outcomes. Prem orbid patient factors that are associated with suppressed frontal lobe activity ma y heighten risk for greater an esthetic depth. Depression, for example, has previously been associated with reduced frontal activity (e.g., Davidson, 1998). Therefore, depression may compromise reserve a nd heighten risk for gr eater anesthetic depth among individuals undergoing surgery; hen ce, 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 ar e 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 th e relationship between stressful lif e events (conceptualized as the combination of physical, environmental, emotional, and psychosocial variables), physiologic/cognitive reserve, and prognosis, depr ession should be routin ely considered as a marker of increased vulnerability. Consideri ng the prevalence and impact of depression on patients with gynecologic tumors, including those w ith 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 k nowledge and practice. Sp ecifically, it has vast implications for interventions that consider de pressive 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

PAGE 53

53 mathematically derived from the formula for a rea under the curve (AUC), which is commonly used to measure physiological or endocrinologi cal 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 an esthetic sensitivity in re lation to depression is relatively novel. In this case, area over th e curve (AOC) was calcula ted by subtracting area under the curve with respect to ground (AUCG) from 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 interpretati on. 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 rela ted to lower AUC estimates). Another issue in relation to using AOC estimates to measure anesthetic sensitivity is the limited number of events (i.e., reco rds of Bispectral Index scores) us ed to calculate AOC; stated differently, the duration of time considered in th e 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 base line to anesthetic maintenance, designated as 6.5 minutes post-anesthetic induction. While extending this period would provide a more accurate picture of anesthetic sensit ivity, issues with variability in intraoperative factors, such as medications administered, patient homeostatic st atus, procedures perfor med, and complications, would likely confound our AOC estimates.

PAGE 54

54 Also, use of BIS as an indicator of anesthetic depth has not been validat ed or established as the gold standard measure of anesthetic de pth (Bruhn et al., 2006). Recall, BIS is a dimensionless EEG-derived value that utilizes a unilateral sensor (int egrated from 3 or 4 electrodes) to obtain an electroe ncephalographic signal from the fo rehead (Bruhn et al., 2006). It differs from the traditional EEG in that it provide s a single variable that is derived from several disparate descriptors of EEG (Bruhn et al., 2006). Though BI S is highly correlated with behavioral assessments of depth of anesthesia (e.g., anesthetic awareness), caution should be used when drawing conclusions about the ability of BIS to a ssess EEG waves. Specifically, caution should be used when using BIS to di scriminate between depressed and non-depressed individuals on the basis of a co rrelation between depression and reduced frontal activity in the frontal cortex. This is part icularly 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 exampl es, Reid, Duke and Allen (1998), Bruder and colleagues (1997), and Henriques and Davidson (1991) used 27, 30, and 14 electrode sites, respectively. Whether depression increases risk for anes thesia-related complications by increasing sensitivity to anesthetic induction is still unknown. Though the relationship between depression and anesthetic sensitiv ity was partially supported, we are unable to assume causality from a correlational design. Further evaluation of this re lationship is warranted. Indeed, a longitudinal design may help clarify the long-term impact of depression on surgical outcomes. Also, consideration of other potential co variates 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

PAGE 55

55 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 te sting (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 da ta. Some systematic factors that may have contributed to the loss of this da ta are being considered. Indee d, the current analyses may have been enhanced by a larger sample. However, th e current findings still highlight the need to identify patients at-risk for adverse intraopera tive and postoperative out comes, which may have vast implications for improving patient care be fore, 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 out comes. Some possibilities may include patients with reduced presurgical front al function (e.g., as measured by neuropsychological assessment), dementia, mental retardation, or ne urological damage (i.e., to the pref rontal cortex of the brain). In fact, there has been some research to suggest that reduced frontal-speci fic abilities, such as working memory and higher order problem solving, is associated with general cognitive slowing in these populations (Devenny et al., 2000; Je lic et al., 2000; Lindal, 1990; Numminen et al., 2001; and Sinanovic et al., 2005). Similar to st udies linking depression to reduced frontal activity, these studies ha ve, 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 brai n in the present population. Confirming that depressed individuals are more sus ceptible to anesthetic effects because of their predisposition to

PAGE 56

56 reduced frontal activity is an important addition to the current literature and a likely next step. This can be achieved by obtaining presurgica l 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 inve stigate the rela tionship between cortisol levels and depression, among other possible physiological or psychologi cal 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 popul ations, including cance r (Cohen et al., 2001; Sephton et al., 2000). Thus, examining the relations hip 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 re lationship between depr ession and anesthetic sensitivity in a group of women, age 40 and ol der, undergoing surgery for the removal of gynecologic tumors. The first aim tested the hyp othesis that depression severity, as assessed by four independent measures of depressed mood, w ould 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 de pressive symptomatology group demonstrating relatively more anesthetic sens itivity. Results provide d some evidence for a relationship between depression severity and anesthetic sensitivity; however, the group difference hypothesis was not supported. One possi ble explanation for this discrepancy is that the depression-anesthetic sensitivity link is meas ure-specific. Specifically, the measures that were correlated with anesthetic sensitivity seem to be more sensitive to the assessment of current depressive symptomatology.

PAGE 57

57 The present study is an important first step in examining premor bid factors that may influence anesthetic response, and thereby, contribute to advers e intraoperative and postoperative outcomes. From the literature, it is clear that examination of risk factor s such as depression may be useful in identifying indivi duals who are at increased risk for negative outcomes associated with anesthesia. Although correlational anal yses will not provide causal evidence for the relationship between depression and anesthetic sensitivity, the current study represents a significant movement towards iden tifying areas for clinical inte rvention at the preoperative, intraoperative, and postoperative levels. For ex ample, the MBMD Depression Scale, one of the measures that demonstrated sensitivity to identi fying individuals at increased risk for negative anesthetic response, is an inva luable assessment tool that has vast implications for moderating factors that may complicate or undermine treatment efforts. N eedless to say, the current study emphasizes the need for interdisciplinary efforts in prevention and intervention in this patient population.

PAGE 58

58 LIST OF REFERENCES American Psychiatric Association Task Force on DSM-IV (2000). Diagno stic and statistical manual of mental disorders: DSM-IV-TR. Washington, DC: American Psychiatric Association. Arbous, M.S., Grobbee, D.E., van Kleef, J.W., de Lange, J.J., Spoormans, H.H., Touw, P., et al. (2001). Mortality associated with anesthesia: A qualitative analys is to identify risk factors. Anaesthesia, 56 1141-1153. Beck, A.T., Steer, R.A., & Brown, G. (1997). Be ck Depression Inventory II. San Antonio, TX: The Psychological Corporation. Bell, I.R., Schwartz, G.E., Hardin, E.E., Baldwin, C.M., & Kline, J.P. ( 1998). Differential resting quantitative electroencephalographic alpha patterns in women with environmental chemical intolerance, depressives, and normals. Biological Psychiatry,43 376. Bernstein, G.M. & Offenbartl, S.K. (1991). A dverse surgical outcomes among patients with cognitive impairments. The American Surgeon, 57 682-690. Black, F.W. (1975). Unilateral brain lesions and MMPI performance: A preliminary study. Perceptual and Motor Skills, 40, 87-93. Bower, J.E., Ganz, P.A., Dickerson, S.S., Peters en, L., Aziz, N., & Fahey J.L. (2005). Diurnal cortisol rhythm and fatigue in breast cancer survivors. Psychoneuroendocrinology, 30 92100. Bruder, G.E., Fong, R., Tenke, C.E., Leite, P., Towe y, J.P., Stewart, J.E., et al. (1997). Regional brain asymmetries in major depression with or without anxiety disorder: A quantitative electroencephalographic study. Biological Psychiatry, 41, 939. Bruhn, J., Myles, P.S., Sneyd, R., & Struys, M.M. R.F. (2006). Depth of An aesthesia monitoring: whats available, whats validated and whats next? British Journal of Anaesthesia, 97, 8594. Cohen, L., de Moor, C., Devine, D., Baum, A., & Amato, R.J. (2001). Endocrine levels at the start of treatment are associated with subs equent psychological adjustment in cancer patients with metastatic disease. Psychosomatic Medicine, 63, 951-958. Charlson, M.E., Pompei, P., Ales, K.L. & MacKen zie, C.R. (1987). A new method of classifying prognostic comorbidity in longitudinal studies: Development and validation. Journal of Chronic Diseases, 40, 373-383. Davidson, R.J. (1988). EEG measures of cerebra l asymmetry: Conceptual and methodological issues. International Journal of Neuroscience, 39, 71-89. Davidson, R.J. (1998). Anterior electrophysiologi cal asymmetries, emotion and depression: conceptual and methodological conundrums. Psychophysiology, 35, 607.

PAGE 59

59 Davidson, R.J., Abercrombie, H.C., Nitschke, J.B., & Putnam, K. (1999). Regional brain function, emotion, and disorders of emotion. Current Opinion in Neurobiology, 9, 228234. Davidson, R.J., Chapman, J.P., & Chapman, L.J. (1987). Task-dependent EEG asymmetry discriminates between depresse d and non-depressed subjects. Psychophysiology, 24, 585. Davidson, R.J., Pizzagalli, D., Nitschke, J.B., & Putnam, K. (2002). Depression: Perspectives from Affective Neuroscience. Annual Review of Psychology, 53, 545-574. Davidson, R.J., Schaffer, C.E., & Saron, C. (1985). Effects of lateralized presentations of faces on self-reports of emotion and EEG asymmetr y in depressed and nondepressed subjects. Psychophysiology, 22, 353-364. Debener, S., Beauducel, A., Nessl er, D., Brocke, B., Heilemann, H., Kayser, J. (2000). Is resting anterior EEG alpha asymmetry a trait marker for depression? Findings for healthy adults and clinically depressed patients. Neuropsychobiology, 41, 31. dElia, G. & Perris, C. (1973). Cerebr al functional dominance and depression. Acta Psychiatrica Scandanavica, 49, 191-197. dElia, G. & Perris, C. (1974). Cerebral functional dominance and memory functions. Acta Psychiatrica Scandanavica [Supplement], 255, 143-157. Devenny, D.A., Krinsky-McHale, S.J., Sersen, G., & Silverman, W.P. (2000). Sequence of cognitive decline in dementia in adults with Down's syndrome. Journal of Intellectual Disability Research, 44 (6), 654-665. Dickens, C., McGowan, L., & Dale, S. (2003). Impact of depression on experimental pain perception: A systematic review of the literature with meta-analysis. Psychosomatic Medicine, 65, 369-375. Drevets, W.C. (1998). Functional neuroimagi ng studies in depressi on: the anatomy of melancholia. Annual Review of Medicine, 49, 341-361. Drover, D. & Ortega, H.R. (2006). Patient state index. Best Practice Research in Clinical Anaesthesiology, 20, 121-128. Elkins, G., Whitfield P., Marcus, J., Symm onds, R., Rodriguez, J., & Cook, T. (2005). Noncompliance with behavioral reco mmendations following bariatric surgery. Obesity Surgery, 15, 546-551. Folstein, M.F., Folstein, S.E., McHugh, P.R. (1975) "Mini-mental state": A practical method for grading the cognitive state of patients for the clinician. Journal of Psychiatric Research, 12, 189-198. Gainotti, G. (1972). Emoti onal behavior and hemisphe ric side of the lesion. Cortex, 8, 41-55.

PAGE 60

60 Gasparrini, W.G., Satz, P., Heilman, K.M., & C oolidge, F.L. (1978). He mispheric asymmetries of affective processing as determined by the Minnesota Multiphasi Personality Inventory. Journal of Neurology, Neurosurgery, and Psychiatry, 41, 470-473. George, M.S., Ketter, T.A., & Post, R.M. (1994) Prefrontal cortex dys function in clinical depression. Depression, 2, 59-72. Glass, P.S. (1998). Anesthetic drug interactio ns: An insight into general anesthesiaits mechanism and dosing strategies [Editorial]. Anesthesiology, 88 5. Gotlib, I.H., Ranganath, C., & Rosenfeld, P. ( 1998). Frontal EEG alpha asymmetry, depression and cognitive functioning. Cognition and Emotion, 12, 449. Grasshoff, C., Rudolph, U., & Antkowiak, B. ( 2005). Molecular and systemic mechanisms of general anaesthesia: The multi-site and multiple mechanisms concept. Current Opinion in Anaesthesiology, 18 386-391. Henriques, J.B. & Davidson, R.J. (1990). Regional brain electrical asymmetries discriminate between previously depressed and healthy control subjects. Journal of Abnormal Psychology, 99, 22-31. Henriques, J.B. & Davidson, R.J. (1991). Left frontal hypoactivation in depression. Journal of Abnormal Psychology, 100, 535. Jelic, V., Johansson, S.E., Almkvist, O., Shiget a, M., Julin, P., Nordberg, A., et al. (2000). Quantitative electroencephalography in mild cognitive impairment: Longitudinal changes and possible prediction of Alzheimer's disease Neurobiology of Aging, 21, 533-540. Johansen, J.W., Sebel, P.S., Sigl, J.C. (2000). Clinical impact of hypno tic-titration guidelines based on EEG bispectral index (BIS) mon itoring during routine anesthetic care. Journal of Clinical Anesthesiology, 12, 433-443. Katon, W., & Sullivan, M.D. (1990). Depr ession and chronic mental illness. Journal of Clinical Psychiatry, 51 3-14. Kelley, S.D. (2003). Monitoring level of consciou sness during anesthesia and sedation. Newton, MA: Aspect Medical Systems, Inc. Kentgen, L.M., Tenke, C.E., Pine, D.S., F ong, R., Klein, R.G., & Bruder, G.E. (2000). Electroencephalographic asymmetries in adoles cents with major depression: Influence of comorbidity with anxiety disorders. Journal of Abnormal Psychology, 109, 797. Le Grande, M.R., Elliott, P.C., Murphy, B.M., Wor cester, M.U., Higgins, R.O., Ernest, C.S., et al. (2006). Health related quality of life traj ectories and predictors following coronary artery bypass surgery. Health and Quality of Life Outcomes, 13, 49. Lindal, E. (1990). Post-operative depr ession and coronary bypass surgery. International Disability Study, 12, 70-74.

PAGE 61

61 Lindsey, D.B. & Wicke, J.D. (1974). The electroe ncephalogram: Autonomous electrical activity in man and animals. In R. Thompson & M.N. Patterson (Eds.), Bioelectric recording techniques (pp. 3-83). New York: Academic Press. Luecken, L.J., Dausch, B., Gulla, V., Hong, R., Compas, B.E. (2004). A lterations in morning cortisol associated with PTSD in women with breast cancer. Journal of Psychosomatic Research, 56, 13-15. Maranets, I. & Kain, Z.N. (1999). Preoperati ve anxiety and intra operative anesthetic requirements. Anesth Analg, 89, 1346-1351. Massie, M.J. (2004). Prevalence of depression in patients with cancer. Journal of the National Cancer Institute Monographs 2004 57-71. McKechnie, B. (1992). Manipula tion under anesthesia: Neurologica l effects of different modes of anesthesia. Dynamic Chropractic, 10 n.p. Messner, M., Beese, U., Romstock, J., Dinkel, M., & Tschaikowsky, K. (2003). The bispectral index declines during neuromuscular block in fully awake persons. Anesth Analg, 97, 488491. Miller, S.L., Jones, L.E., Carney, C.P. (2005) Psychiatric sequelae following breast cancer chemotherapy: A pilot study using claims data. Psychosomatics, 46 517-522. Millon, T., Antoni, M.H., Millon, C., Meagher, S ., Grossman, S. (2001). Test Manual for the Millon Behavioral Medicine Diagnostic (MBMD). Minneapo lis, MN: National Computer Services. Monk, T.G., Saini, V., Weldon, B.C., & Sigl, J.C. (2005). Anesthetic management and one-year mortality after noncardiac surgery. Anesth Analg, 100, 4-10. Mormont, M.C. & Levi, F. (1997). Circadian-syst em alterations during cancer processes: A review. International Jour nal of Cancer, 70, 241-247. Munro, A.J., Potter, S. (1996). A quantitative ap proach to the distress caused by symptoms in patients treated with radical radiotherapy. British Journal of Cancer, 74, 640-647. Muravchick, S (1998). The aging process: Anesthetic impli cations [Review]. Acta Anaesthesiol Belg, 49, 85-90. Newman, M.F., Croughwell, N.D., Blumenthal, J. A., Lowry, E., White, W.D., Spillane, W., et al. (1995). Predictors of cognitive decline after cardiac operation. The Annals of Thoracic Surgery, 59 1326-1330. Numminen, H., Service, E., Ahonen, T., & Ruopp ila, I. (2001). Working memory and everyday cognition in adults with Down's syndrome. Journal of Intellectual Disability Research, 45, 157-168.

PAGE 62

62 Ockenfels, M.C., Porter, L., Smyth, J., Kirs chbaum, C., Hellhammer, D.H., & Stone, A.A. (1995). Effect of chronic stress associated with unemployment on salivary cortisol: Overall cortisol levels, diurnal rhyt hm, and acute stress reactivity. Psychosomatic Medicine, 57, 460-467. Perini, G. & Mendus, R. (1984) Depression and anxiety in complex partial seizures. The Journal of Nervous and Mental Disease, 172, 287-290. Pruessner, J.C., Kirschbaum, C., Meinlschmid, G., & Hellhammer, D.H. (2003). Two formulas for computation of the area under the curv e represent measures of total hormone concentration versus time-dependent change. Psychoneuroendocrinology, 28, 916-931. Psychological Corporation. (1999). Wechsler Abbreviated Scale of Intelligence San Antonio, TX: Author. Ransom, E.S, & Mueller, R.A. (1997). Safety co nsiderations in the use of drug combinations during general anaesthesia. Drug Safety: An International J ournal of Medical Toxicology and Drug Experience,16, 88-103. Rasmussen, L.S., Johnson, T., Kuiprs, H.M., Kriste nsen, D., Siersma, V.D., Vila, P., et al. (2003). Does anesthesia cause postoperative cognitive dysfunction? A randomized study of regional versus general anesth esia in 438 elderly patients. Acta Anaesthesiol Scand, 47, 260-266. Reid, S.A., Duke L.M., & Allen, J.J.B. (1998) Resting frontal electroencephalographic asymmetry in depression: Inconsistencies sugge st the need to identify mediating factors? Psychophysiology, 35, 389. Renna, M. & Venturi, R. (2000). Bispectral index and anaesthesia in the elderly. Minerva Anesthesiol, 66, 398-402. Robinson, R.G., Kubos, K.L., Starr, L.B., Rao, K., & Price, T.R. (1984). Mood disorders in stroke patients Stroke, 13, 635-641. Rochford, J.M., Swartzburg, M., Chowdhrey, S.M ., & Goldstein, L. (1976). Some quantitative EEG correlates of psychopathology. Research Communications in Psychology, Psychiatry, and Behavior, 1, 211-226. Satz, P. (1993). Brain reserve capacity on sympto m onset after brain injury: A formulation and review of evidence fo r threshold theory. Neuropsychology, 7, 273-295. Schaffer, C.E., Davidson, R.J., & Saron, C. (1983) Frontal and parietal electroencephalogram asymmetry in depressed and nondepressed subjects. Biological Psychiatry, 18, 753-762. Sephton, S.E., Sapolsky, R.M., Kraemer, H.C., & Spie gel, D. (2000). Diurnal cortisol rhythm as a predictor of breast cancer survival. Journal of the National Cancer Institute, 92, 9941000.

PAGE 63

63 Sephton, S. & Spiegel, D. (2003). Circadian di sruption in cancer: A neuroendocrine-immune pathway from stress to disease? Brain, Behavior and Immunology, 17, 321-328. Sigurdsson, G.H. & McAteer, E. (1996). Morbidity and mortality associat ed with anaesthesia. Acta Anaesthesiol Scand, 40, 1057-1063. Sinanovic, O., Kapidzic, A., Kovacevic, L., Hudi c., J, & Smajlovic, D. (2005). EEG frequency and cognitive dysfunction in pati ents with Parkinson's disease Med Arh., 59, 286-287. Song, D., Joshi, G.P., & White, P.F. (1997). Titr ation of volatile anesthetics using bispectral index facilitates recovery after ambulatory anesthesia. Anesthesiology, 87, 842-848. Spiegel, D. (1997). Psychosocial asp ects of breast cancer treatment. Seminar on Oncology, 24 S1. Stern, Y. (2002). What is cognitive reserve? Th eory and research application of the reserve concept. Journal of the International Neuropsychological Society, 8, 448-460. Touitou, Y., Bogdan, A., Levi, F., Benavides, M., & Auzeby, A. (1996). Disruption of the circadian patterns of serum cortisol in breas t and ovarian cancer patients: Relationships with tumour marker antigens. British Journal of Cancer, 74, 1248-1252. Vess, J.D., Moreland, J.R., Schwebel, A.I., & Kr aut, E. (1988). Psychosocial needs of cancer patients: Learning from pa tients and their spouses. Journal of Psychosocial Oncology, 6, 31-51. Watson, M., Haviland, J.S., Greer, S., Davidson, J., & Bliss, J.M. (1999). Influence of psychological response on survival in br east cancer: A population-based cohort study. The Lancet, 354, 1331-1336.

PAGE 64

64 BIOGRAPHICAL SKETCH Rachel Andr was born and raised in Miami, FL. She is a Phi Beta Kappa graduate of Howard University in Washington, D.C., where sh e earned a Bachelor of Science in psychology. Her minor area of concentration was chemistry. Ms. Andr is currently pu rsuing her doctorate in clinical psychology at the Univer sity of Florida, specializing in health psychology. Current clinical and research interests are in the area of obesity resear ch and treatment, culture and body image, as well as the psychosocial impact of h ealth problems at the individual and community levels. Areas of particular in terest to Ms. Andr are those that have vast public health implications (e.g., sexually transmitted diseases such as HIV/AIDS and HPV; obesity).


Permanent Link: http://ufdc.ufl.edu/UFE0020953/00001

Material Information

Title: Presurgical Depression and Anesthetic Sensitivity in Women Undergoing Surgery for the Removal of Gynecological Tumors
Physical Description: Mixed Material
Copyright Date: 2008

Record Information

Source Institution: University of Florida
Holding Location: University of Florida
Rights Management: All rights reserved by the source institution and holding location.
System ID: UFE0020953:00001

Permanent Link: http://ufdc.ufl.edu/UFE0020953/00001

Material Information

Title: Presurgical Depression and Anesthetic Sensitivity in Women Undergoing Surgery for the Removal of Gynecological Tumors
Physical Description: Mixed Material
Copyright Date: 2008

Record Information

Source Institution: University of Florida
Holding Location: University of Florida
Rights Management: All rights reserved by the source institution and holding location.
System ID: UFE0020953:00001


This item has the following downloads:


Full Text





PRESURGICAL DEPRESSION AND ANESTHETIC SENSITIVITY
IN WOMEN UNDERGOING SURGERY
FOR THE REMOVAL OF GYNECOLOGICAL TUMORS





















By

RACHEL ANDRE


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

2007



































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









ACKNOWLEDGMENTS

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


page

ACKNOWLEDGMENTS .............. ...............4.....


LIST OF TABLES ............ ...... ._._ ...............7....

LIST OF FIGURES .............. ...............8.....


AB S TRAC T ......_ ................. ............_........9


CHAPTER


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 page

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 page

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

By

Rachel Andre

May 2007

Chair: Catherine Price
Major: Psychology

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

anesthetic induction.

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

anesthesia).









CHAPTER 1
INTTRODUCTION

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

gynecologic tumors.

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,

1998).









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

prevalent.










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

consideration.

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

gynecologic tumors.

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

sensitivity'

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

anesthetic sensitivity.

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.





Anesthetic


Asymmetrical Activation of
the Frontal Cortex (EEG)


Figure 1-1. Proposed model showing the major associations conceptualized in the present study.









CHAPTER 2
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

tumors .

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.









CHAPTER 3
METHOD S

Sample Characteristics

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).









Other Questionnaires

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

variability.

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.

Statistical Analyses

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.

Specific Aim

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.












































Surgical Intervention
(N 7 6)


Abstraction of
BIS Records (N 26)


Table 3-1. Participant characteristics by group--Means and standard deviations shown.
No History of History of Depressive Significance


Depressive
Symptomatology
(N= 15)
61.4 (9.9)
13.6 (1.9)
109.2 (16.9)
5.1 (2.3)


Symptomatology
(N=11)

55.4 (11.6)
11.3 (2.2)
95.1 (19.7)
4.0 (2.8)


Age
Years of Education
IQ
CCI Total


0.016


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)


Informed Consent
Admission to GCRC
Psychological/Neurocognitive Testing
(N = 43)2


Clinical Interview and
Consensus Conference (N 26)


History of Depressive
Symptomatology Group
(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
Symptomatology Group
(N = 151











100 - - -

AOC




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)









CHAPTER 4
RESULTS

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
Symptomatology Symptomatology
(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
Status
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
O no
o
X yes
Too~oo Fit line for Total
x x
OX
O
O

S600.00 O

> O

o) OO

o

bg O x
O x


o

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
x Ono

o X yes,,,


Ox x


.5O

OO

5 00.00-1 O
C) x

O oO

OO



1 I I


200040006000800




MBMD Future Pessimism Scale


Figure 4-2. Relationship between MBMD future pessimism scores and anesthetic sensitivity
(AOC).





650.00


527.26 + 105.42

O 600.00-





S550.00-





500.00-




569.47 + 127.73
450.00-


no yes
History of Depressive Symptomatology

Figure 4-3. Relationship between group classification and anesthetic sensitivity (AOC).

Note: Error bars represent standard error of the mean.










CHAPTER 5
DISCUSSION

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-

anesthetic induction.

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

sensitivity .

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,

respectively .

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

depressive symptomatology.










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

population.










LIST OF REFERENCES


American Psychiatric Association Task Force on DSM-IV (2000). Diagnostic and statistical
manual of mental disorders: DSM-IV-TR. Washington, DC: American Psychiatric
Association.

Arbous, M.S., Grobbee, D.E., van Kleef, J.W., de Lange, J.J., Spoormans, H.H., Touw, P., et al.
(2001). Mortality associated with anesthesia: A qualitative analysis to identify risk factors.


Beck, A.T., Steer, R.A., & Brown, G. (1997). Beck Depression Inventory II. San Antonio, TX:
The Psychological Corporation.

Bell, I.R., Schwartz, G.E., Hardin, E.E., Baldwin, C.M., & Kline, J.P. (1998). Differential resting
quantitative electroencephalographic alpha patterns in women with environmental
chemical intolerance, depressives, and normals. Biological Psychiatry,43, 376-388.

Bemstein, G.M. & Offenbartl, S.K. (1991). Adverse surgical outcomes among patients with
cognitive impairments. Th2e American Surgeon, 57, 682-690.

Black, F.W. (1975). Unilateral brain lesions and MMPI performance: A preliminary study.
Perceptual and Motor .\d1/\, 40, 87-93.

Bower, J.E., Ganz, P.A., Dickerson, S.S., Petersen, L., Aziz, N., & Fahey J.L. (2005). Diumal
cortisol rhythm and fatigue in breast cancer survivors. Psychoneuroendocrinology, 30, 92-
100.

Bruder, G.E., Fong, R., Tenke, C.E., Leite, P., Towey, J.P., Stewart, J.E., et al. (1997). Regional
brain asymmetries in maj or depression with or without anxiety disorder: A quantitative
electroencephalographic study. BiologicalPsychiatry, 41, 939-948.

Bruhn, J., Myles, P.S., Sneyd, R., & Struys, M.M.R.F. (2006). Depth of Anaesthesia monitoring:
what' s available, what' s validated and what' s next? British Journal ofA aII(Ir \lr ia 9 7, 85-
94.

Cohen, L., de Moor, C., Devine, D., Baum, A., & Amato, R.J. (2001). Endocrine levels at the
start of treatment are associated with subsequent psychological adjustment in cancer
patients with metastatic disease. Psychosomatic M~edicine, 63, 951-958.

Charlson, M.E., Pompei, P., Ales, K.L. & MacKenzie, C.R. (1987). A new method of classifying
prognostic comorbidity in longitudinal studies: Development and validation. Journal of
Chronic Diseases, 40, 373-383.

Davidson, R.J. (1988). EEG measures of cerebral asymmetry: Conceptual and methodological
issues. International Journal ofNeuroscience, 39, 71-89.

Davidson, R.J. (1998). Anterior electrophysiological asymmetries, emotion and depression:
conceptual and methodological conundrums. Psychophysiology, 35, 607-614.










Davidson, R.J., Abercrombie, H.C., Nitschke, J.B., & Putnam, K. (1999). Regional brain
function, emotion, and disorders of emotion. Current Opinion in Neurobiology, 9, 228-
234.

Davidson, R.J., Chapman, J.P., & Chapman, L.J. (1987). Task-dependent EEG asymmetry
discriminates between depressed and non-depressed subjects. Psychophysiology, 24, 585.

Davidson, R.J., Pizzagalli, D., Nitschke, J.B., & Putnam, K. (2002). Depression: Perspectives
from Affective Neuroscience. Annual Review ofPsychology, 53, 545-574.

Davidson, R.J., Schaffer, C.E., & Saron, C. (1985). Effects of lateralized presentations of faces
on self-reports of emotion and EEG asymmetry in depressed and non-depressed subj ects.
Psychophysiology, 22, 353-364.

Debener, S., Beauducel, A., Nessler, D., Brocke, B., Heilemann, H., Kayser, J. (2000). Is resting
anterior EEG alpha asymmetry a trait marker for depression? Findings for healthy adults
and clinically depressed patients. Neuropsychobiology, 41, 31-37.

d'Elia, G. & Perris, C. (1973). Cerebral functional dominance and depression. Acta Psychiatrica
Scandanavica,~~~ddd~~ddd~~ 49, 191-197.

d'Elia, G. & Perris, C. (1974). Cerebral functional dominance and memory functions. Acta
Psychiatrica Scandanavicad~~~ddd~~ddd~~ [Supplement], 255, 143-157.

Devenny, D.A., Krinsky-McHale, S.J., Sersen, G., & Silverman, W.P. (2000). Sequence of
cognitive decline in dementia in adults with Down's syndrome. Journal oflntellectual
Disability Research, 44 (6), 654-665.

Dickens, C., McGowan, L., & Dale, S. (2003). Impact of depression on experimental pain
perception: A systematic review of the literature with meta-analysis. Psychosomatic
Medicine, 65, 369-375.

Drevets, W.C. (1998). Functional neuroimaging studies in depression: the anatomy of
melancholia. Annual Review of2~edicine, 49, 341-361.

Drover, D. & Ortega, H.R. (2006). Patient state index. Best Practice Research in Clinical


Elkins, G., Whitfield P., Marcus, J., Symmonds, R., Rodriguez, J., & Cook, T. (2005).
Noncompliance with behavioral recommendations following bariatric surgery. Obesity
Surgery, 15, 546-551.

Folstein, M.F., Folstein, S.E., McHugh, P.R. (1975). "Mini-mental state": A practical method for
grading the cognitive state of patients for the clinician. Journal of Psychiatric Research,
12, 189-198.

Gainotti, G. (1972). Emotional behavior and hemispheric side of the lesion. Cortex, 8, 41-55.










Gasparrini, W.G., Satz, P., Heilman, K.M., & Coolidge, F.L. (1978). Hemispheric asymmetries
of affective processing as determined by the Minnesota Multiphasi Personality Inventory.
Journal ofNeurology, Neurosurgery, and Psychiatry, 41, 470-473.

George, M.S., Ketter, T.A., & Post, R.M. (1994). Prefrontal cortex dysfunction in clinical
depression. Depression, 2, 59-72.

Glass, P.S. (1998). Anesthetic drug interactions: An insight into general anesthesia--its
mechanism and dosing strategies [Editorial]. Awathel~ill~ Sithoy 88, 5-6.

Gotlib, I.H., Ranganath, C., & Rosenfeld, P. (1998). Frontal EEG alpha asymmetry, depression
and cognitive functioning. Cognition and Emotion, 12, 449-478.

Grasshoff, C., Rudolph, U., & Antkowiak, B. (2005). Molecular and systemic mechanisms of
general anaesthesia: The 'multi-site and multiple mechanisms' concept. Current Opinion


Henriques, J.B. & Davidson, R.J. (1990). Regional brain electrical asymmetries discriminate
between previously depressed and healthy control subj ects. Journal ofAbnormal
Psychology, 99, 22-3 1.

Henriques, J.B. & Davidson, R.J. (1991). Left frontal hypoactivation in depression. Journal of
Abnormal Psychology, 100, 535-545.

Jelic, V., Johansson, S.E., Almkvist, O., Shigeta, M., Julin, P., Nordberg, A., et al. (2000).
Quantitative electroencephalography in mild cognitive impairment: Longitudinal changes
and possible prediction of Alzheimer's disease. Neurobiology ofdging. 21, 533-540.

Johansen, J.W., Sebel, P.S., Sigl, J.C. (2000). Clinical impact of hypnotic-titration guidelines
based on EEG bispectral index (BIS) monitoring during routine anesthetic care. Journal of
Clinical wlbll\r l i1,hog,5~ 12, 433-443.

Katon, W., & Sullivan, M.D. (1990). Depression and chronic mental illness. Journal of Clinical
Psychiatry, 51, 3-14.

Kelley, S.D. (2003). Monitoring level of consciousness during anesthesia and sedation. Newton,
MA: Aspect Medical Systems, Inc.

Kentgen, L.M., Tenke, C.E., Pine, D.S., Fong, R., Klein, R.G., & Bruder, G.E. (2000).
Electroencephalographic asymmetries in adolescents with major depression: Influence of
comorbidity with anxiety disorders. Journal ofAbnormalPsychology, 109, 797-802.

Le Grande, M.R., Elliott, P.C., Murphy, B.M., Worcester, M.U., Higgins, R.O., Ernest, C.S., et
al. (2006). Health related quality of life traj ectories and predictors following coronary
artery bypass surgery. Health and Quality of Life Outcomes, 13, 49.

Lindal, E. (1990). Post-operative depression and coronary bypass surgery. International
Disability Study, 12, 70-74.










Lindsey, D.B. & Wicke, J.D. (1974). The electroencephalogram : Autonomous electrical activity
in man and animals. In R. Thompson & M.N. Patterson (Eds.), Bioelectric recording
techniques (pp. 3-83). New York: Academic Press.

Luecken, L.J., Dausch, B., Gulla, V., Hong, R., Compas, B.E. (2004). Alterations in morning
cortisol associated with PTSD in women with breast cancer. Journal ofPsychosomatic
Research, 56, 13-15.

Maranets, I. & Kain, Z.N. (1999). Preoperative anxiety and intraoperative anesthetic
requirements. Anesth Anlytl. 89, 1346-1351.

Massie, M.J. (2004). Prevalence of depression in patients with cancer. Journal of the National
Cancer Institute M~onoguraphs 2004, 57-71.

McKechnie, B. (1992). Manipulation under anesthesia: Neurological effects of different modes
of anesthesia. Dynamic Chropractic, 10, n.p.

Messner, M., Beese, U., Romstock, J., Dinkel, M., & Tschaikowsky, K. (2003). The bispectral
index declines during neuromuscular block in fully awake persons. Anesth Anlytl. 97, 488-
491.

Miller, S.L., Jones, L.E., Carney, C.P. (2005). Psychiatric sequelae following breast cancer
chemotherapy: A pilot study using claims data. Psychosomatics, 46, 517-522.

Million, T., Antoni, M.H., Millon, C., Meagher, S., Grossman, S. (2001). Test Manual for the
Million Behavioral Medicine Diagnostic (MBMD). Minneapolis, MN: National Computer
Services.

Monk, T.G., Saini, V., Weldon, B.C., & Sigl, J.C. (2005). Anesthetic management and one-year
mortality after noncardiac surgery. Alllthl Anlytl. 100, 4-10.

Mormont, M.C. & Levi, F. (1997). Circadian-system alterations during cancer processes: A
review. International Journal of Cancer, 70, 241-247.

Munro, A.J., Potter, S. (1996). A quantitative approach to the distress caused by symptoms in
patients treated with radical radiotherapy. British Journal of Cancer, 74, 640-647.

Muravehick, S (1998). The aging process: Anesthetic implications [Review]. Acta A nacr rlr itheti
Belg, 49, 85-90.

Newman, M.F., Croughwell, N.D., Blumenthal, J.A., Lowry, E., White, W.D., Spillane, W., et
al. (1995). Predictors of cognitive decline after cardiac operation. The Annals of Thoracic
Surgery, 59, 1326-1330.

Numminen, H., Service, E., Ahonen, T., & Ruoppila, I. (2001). Working memory and everyday
cognition in adults with Down's syndrome. Journal oflntellectual Disability Research, 45,
157-168.










Ockenfels, M.C., Porter, L., Smyth, J., Kirschbaum, C., Hellhammer, D.H., & Stone, A.A.
(1995). Effect of chronic stress associated with unemployment on salivary cortisol: Overall
cortisol levels, diurnal rhythm, and acute stress reactivity. Psychosomatic M~edicine, 57,
460-467.

Perini, G. & Mendus, R. (1984). Depression and anxiety in complex partial seizures. The Journal
ofNervous and M~ent~sal Dsease 172, 287-290.

Pruessner, J.C., Kirschbaum, C., Meinlschmid, G., & Hellhammer, D.H. (2003). Two formulas
for computation of the area under the curve represent measures of total hormone
concentration versus time-dependent change. Psychoneuroendocrinology, 28, 916-931.

Psychological Corporation. (1999). Wechsler Abbreviated Scale oflntelligence. San Antonio,
TX: Author.

Ransom, E. S, & Mueller, R.A. (1997). Safety considerations in the use of drug combinations
during general anaesthesia. Drug Safety: An International Journal of2~edical Toxicology
and Drug Experience, 16, 88-103.

Rasmussen, L.S., Johnson, T., Kuiprs, H.M., Kristensen, D., Siersma, V.D., Vila, P., et al.
(2003). Does anesthesia cause postoperative cognitive dysfunction? A randomized study of
regional versus general anesthesia in 438 elderly patients. Acta Anactl'lr\ibled Scand, 47,
260-266.

Reid, S.A., Duke L.M., & Allen, J.J.B. (1998). Resting frontal electroencephalographic
asymmetry in depression: Inconsistencies suggest the need to identify mediating factors?
Psychophysiology, 35, 389-404.

Renna, M. & Venturi, R. (2000). Bispectral index and anaesthesia in the elderly. M~inerva
Anesthesiol, 66, 398-402.

Robinson, R.G., Kubos, K.L., Starr, L.B., Rao, K., & Price, T.R. (1984). Mood disorders in
stroke patients. Stroke, 13, 635-641.

Rochford, J.M., Swartzburg, M., Chowdhrey, S.M., & Goldstein, L. (1976). Some quantitative
EEG correlates of psychopathology. Research Communications in Psychology, Psychiatry,
and Behavior, 1, 211-226.

Satz, P. (1993). Brain reserve capacity on symptom onset after brain injury: A formulation and
review of evidence for threshold theory. Neuropsychology, 7, 273-295.

Schaffer, C.E., Davidson, R.J., & Saron, C. (1983). Frontal and parietal electroencephalogram
asymmetry in depressed and nondepressed subjects. BiologicalPsychiatry, 18, 753-762.

Sephton, S.E., Sapolsky, R.M., Kraemer, H.C., & Spiegel, D. (2000). Diurnal cortisol rhythm as
a predictor of breast cancer survival. Journal of the National Cancer Institute, 92, 994-
1000.










Sephton, S. & Spiegel, D. (2003). Circadian disruption in cancer: A neuroendocrine-immune
pathway from stress to disease? Brain, Behavior andl~mmunology, 17, 321-328.

Sigurdsson, G.H. & McAteer, E. (1996). Morbidity and mortality associated with anaesthesia.
Acta A na I \lrtibled Scand', 40, 1057-1063.

Sinanovic, O., Kapidzic, A., Kovacevic, L., Hudic., J, & Smajlovic, D. (2005). EEG frequency
and cognitive dysfunction in patients with Parkinson's disease. M~edArh., 59, 286-287.

Song, D., Joshi, G.P., & White, P.F. (1997). Titration of volatile anesthetics using bispectral
index facilitates recovery after ambulatory anesthesia. Anesthesiology, 87, 842-848.

Spiegel, D. (1997). Psychosocial aspects of breast cancer treatment. Seminar on Oncology, 24,
Sl.

Stern, Y. (2002). What is cognitive reserve? Theory and research application of the reserve
concept. Journal of the International Neuropsychological Society, 8, 448-460.

Touitou, Y., Bogdan, A., Levi, F., Benavides, M., & Auzeby, A. (1996). Disruption of the
circadian patterns of serum cortisol in breast and ovarian cancer patients: Relationships
with tumour marker antigens. British Journal of Cancer, 74, 1248-1252.

Vess, J.D., Moreland, J.R., Schwebel, A.I., & Kraut, E. (1988). Psychosocial needs of cancer
patients: Learning from patients and their spouses. Journal ofPsychosocial Oncology, 6,
31-51.

Watson, M., Haviland, J.S., Greer, S., Davidson, J., & Bliss, J.M. (1999). Influence of
psychological response on survival in breast cancer: A population-based cohort study. The
Lancet, 354, 1331-1336.









BIOGRAPHICAL SKETCH

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).