methods to improve anesthetic drug management by sandeep …

METHODS TO IMPROVE ANESTHETIC

DRUG MANAGEMENT

by

Sandeep Choudary Manyam

A dissertation submitted to the faculty of

The University of Utah

in partial fulfillment of the requirements for the degree of

Doctor of Philosophy

Department of Bioengineering

The University of Utah

December 2006

ABSTRACT

Modern day anesthesia involves the use of multiple drugs simultaneously to

maintain insensitivity to pain or analgesia, lack of awareness of the surgical procedure

and suppression of autonomic responses. The sedative component of anesthesia is

primarily provided by using a hypnotic drug (volatile or intravenously administered) and

the analgesic component is provided by an opioid (primarily intravenously administered).

The level of anesthetic effect produced by these drugs is assessed by the use of a

multitude of physiologic responses such as heart rate, blood pressure, movement etc. The

response dynamics of these indicators are typically non linear and change with the

combination of anesthetics being used. The potency of drugs also vary among patients

and across age groups. These factors make the accurate titration of anesthetic drugs

challenging. Accurate titration of anesthetic drug such that the effect is just enough to

cause unconsciousness and immobility in the patient helps to avoid adverse effects such

as delayed emergence, awareness during the procedure, hyper variable cardiovascular

state and memory loss that is thought to be associated with under or overdose.

This work aims to improve anesthetic drug management through efficient drug

delivery and real time monitoring. The first goal is to improve drug delivery and clinical

outcomes for the average patient by identifying combinations of sedative and analgesic

drugs that ensure fast recovery from anesthesia. Although the combinations are suitable

to be applied in clinical practice they may not be effective when applied to individual

v

patients that are outliers (such as those who use chronic pain medication). The drug dose

in such patients can be titrated by assessing the depth of anesthesia in real time. The

second goal is to test the ability of emerging depth of anesthesia monitoring technologies

to assess each patient’s anesthetic state. Real time monitors of anesthetic effect can help

the clinicians refine their dosing strategy and predict adverse events such as intra-

operative awareness or patient responses to pain.

CONTENTS

ABSTRACT...................................................................................................................... iv

ACKNOWLEDGEMENTS .......................................................................................... viii

1. INTRODUCTION......................................................................................................... 1

1.1 Goals ................................................................................................................. 6

1.2 References......................................................................................................... 8

2. OPIOID-VOLATILE ANESTHETIC SYNERGY AND CONTEXT

SENSITIVE TARGETS ............................................................................................. 13

2.1 Abstract ........................................................................................................... 13

2.2 Introduction..................................................................................................... 15

2.3 Materials and Methods.................................................................................... 17

2.4 Results............................................................................................................. 25

2.5 Discussion ....................................................................................................... 36

2.6 Appendix A: The Logit Model for Pharmacodynamics ................................. 52

2.7 Appendix B: Pharmacokinetic and Pharmacodynamic Simulations .............. 54

2.8 References....................................................................................................... 56

3. CONTEXT SENSITIVE TARGETS FOR OPIOIDS AND

INTRAVENOUS ANESTHETICS ........................................................................... 62

3.1 Abstract ........................................................................................................... 62

3.2 Introduction..................................................................................................... 64


3.4 Results............................................................................................................. 70

3.5 Discussion ....................................................................................................... 84

3.6 References....................................................................................................... 90

4. PROCESSED EEG TARGETS REQUIRED FOR

ADEQUATE ANESTHESIA ..................................................................................... 93

4.1 Abstract ........................................................................................................... 93

4.2 Introduction..................................................................................................... 94


4.4 Results........................................................................................................... 102

vii

4.5 Discussion ..................................................................................................... 115

4.6 References..................................................................................................... 126

5. PROCESSED EEG SIGNALS AS INDICATORS OF

INADEQUATE ANESTHESIA............................................................................... 132

5.1 Abstract ......................................................................................................... 132

5.2 Introduction................................................................................................... 134

5.3 Materials and Methods.................................................................................. 136

5.4 Results........................................................................................................... 140

5.5 Discussion ..................................................................................................... 147

5.6 References..................................................................................................... 149

6. SUMMARY AND CONCLUSIONS ....................................................................... 151

6.1 Summary....................................................................................................... 151

6.2 Conclusions................................................................................................... 153

6.3 Impact ........................................................................................................... 154

6.4 Future Work .................................................................................................. 155

ACKNOWLEDGEMENTS

I would like to acknowledge a number of people for their help and support during

my doctoral work.

Foremost, of course is my advisor, Dr. Dwayne Westenskow. Throughout my

doctoral work he encouraged me to work on ideas that had practical applications in

clinical anesthesia. Ever since I entered his laboratory he placed extreme confidence in

me and provided me with limitless opportunities. He greatly assisted me with developing

my scientific communication skills and translating my ideas into viable research grants.

I am also grateful to a number of anesthesiologists who taught me all I know

about clinical anesthesia and conducting clinical research. Dr. Talmage Egan, for

spending countless hours in reviewing and helping me interpret my results and providing

the direction to make my work clinically relevant and innovative. For the creative

freedom he gave me while simultaneously insisting on the highest standards for both

form and content. His simple words of encouragement -- “keep up the good work” when I

had not shown him results for ages were an additional incentive for me to work harder. I

am thankful for his efforts in providing me with the broad perspective with which I could

relate any specific problem I was working on, to anesthesiology and patient care as a

whole. I was deeply touched by his kindness and generosity with ideas and financial

support.

x

Dr. Dhanesh Gupta, for his enthusiastic guidance and his step by step involvement

in translating a “paper napkin” idea in to an exciting “high-impact” manuscript. Without

his energy and emphasis on completion, I would be forever lost in refining my data

analysis. For the numerous operating room breaks and weekends he decided to spend

with me to ensure my simulations were meaningful.

Dr. Ken Johnson, for his insightful comments on my results and constant

encouragement. For the number of hours he spent helping me understand anesthetic

dosing.

Dr. Nathan Pace, for insisting on the right statistical methods at every stage. For

allowing me to “pick his brain” at will and always helping me with a solution or pointing

me in a direction in which I could find one.

I am greatly appreciative of the committee members for their time and effort in

not only clarifying my research ideas but also in ensuring that I receive a well rounded

education. Dr. Richard Normann, who was incidentally the first professor whom I met in

Utah, helped me continually in identifying my interests and helping me define my long

term career goals. I am still in awe of his child-like enthusiasm when conducting

laboratory research and hope that I am able to bring that level of energy in to my own

experiments some day. I have always relied on his frank opinion and guidance throughout

my graduate education and hope this will continue for years to come.

Dr. Steve Kern, for his support with any engineering dilemmas and his

stimulating discussions on pharmacodynamic models and methods. For the confidence he

gave me by just “being there” for I knew that if I was stuck with a problem Dr. Kern

could probably bail me out.

xi

Dr. Rob Macleod who, along with Dr. Patrick Tresco, taught a set of classes that

formed the foundation of my graduate education. What made these two classes stand out

was not just the content that was taught but their emphasis on the manner in which an

engineer-scientist ought to approach a biological system. As I set out on my academic

career their teaching style will always be the standard that I would try to achieve. I

would also like to thank Rob for offering me a teaching assistantship. This enabled me to

observe his teaching methods at close hand and also came at a time when I was faced

with funding problems.

Dr. Srikantan Nagarajan, who taught me so much about the basic principles of

conducting research. So many of the concepts I learnt in his laboratory extend far beyond.

Sri insisted in making the most out of any experiment. He would insist that every

experiment whether a failure or a success needs to “count” toward my own as well as the

society’s learning process. His words “focus on the science” still ring in my ears and

motivate me when I am frustrated with a research problem.

Drs. Gregory Clark and Kenneth Horch, for their advice during the crucial days

when I was faced with major decisions during my graduate studies. Dr. Clark for his

particular emphasis on rigorous experimental techniques and personal attention to my

experimental skills and writing techniques in the neural interfaces laboratory.

Julia White, our research nurse, who was involved in all steps of planning the

study, volunteer recruitment and data collection. Without her attention to detail these

studies would have been monumentally difficult.

xii

Noah Syroid and Jim Agutter at MedVis, for their continued support over the

years. I will miss the informal discussions with Noah and his active participation in

research conferences.

The administrative staff at the anesthesiology and bioengineering departments.

Specifically, Jeff Mann, Vicki Larsen, Karen Terry, Paul Dryden and Linda Twitchell

among many for others their logistical and technical help. My past and present lab mates

at the anesthesia bioengineering laboratory for their friendship and their insightful

discussions.

Finally, I deeply appreciate the unwavering support and encouragement my

family and friends. My mother, Deepa Choudary, for her encouragement to explore and

young age. For her numerous personal sacrifices to keep us oblivious to other problems.

My sister, Kinnera Krishna, who has always encouraged me to pursue my interests no

matter what the costs. My fiancée, Nirupama Ramkumar, who endured frustrating and

good times with me through the various stages of graduate school. With great pleasure

and gratitude I dedicate my work to them

CHAPTER 1

INTRODUCTION

In modern clinical practice, anesthesia comprises of three main components-

insensitivity to pain or analgesia, lack of awareness of the surgical procedure and

suppression of autonomic responses. This is achieved by using different classes of drugs

simultaneously. The analgesic component is most commonly provided by opioids which

are primarily delivered intravenously. Lack of awareness or sedation is achieved by a

hypnotic drug. The hypnotic agent may be administered through a vaporizer for volatile

agents (ex. sevoflurane) or by using an infusion pump for intravenous drugs (ex.

propofol). In addition to sedation and analgesia, muscle relaxants are used to suppress

somatic motor responses.

Certain hypnotic drugs alone can often produce surgically adequate anesthesia

albeit at higher concentrations.13

This approach, which was common in the past, is often

associated with excessive hemodynamic depression14

and other undesirable side effects

of administration of high doses of the hypnotic drug for a long period of time (e.g.,

prolonged time to awakening from anesthesia, etc.).15

Thus, for practical purposes, the

current state of the art is to produce anesthesia with an opioid and a sedative in

combination.1

Interaction is observed among many drugs used in anesthetic practice. The

addition of opioid reduces the concentration of the hypnotic drug required to produce

2

sedation.3,16-29

Similarly the presence of a hypnotic drug enhances analgesia and reduces

the opioid requirements. Although, anesthetic drug interactions were widely studied in

the past,17,22,27,28,30,31

it is only more recently that they have been quantified by the use of

a mathematical model.2,3,23,25,32-36

The pharmacodynamic interaction models relate the

concentration of the two drugs to the level of effect they produce. These models can help

clinicians determine if a certain dose combination of hypnotic and opioid will provide

adequate sedation and analgesia. Response surface models allow the complete

characterization of pharmacodynamic interactions over the entire spectrum of possible

concentration pairs32,33

instead of just a single level of drug effect such as a 50%

probability of nonresponsiveness to surgical incision (e.g., Minimum Alveolar

Concentration, MAC). Short, et al. describe a crisscross sampling method37

which can be

used to sample drug concentration pairs needed to construct a response surface. Response

surface pharmacodynamic interaction methods provide a framework to define and

explore these issues. However such methods have not been used to study the interaction

between volatile anesthetics and opioids. These models can also form the basis for the

development of a real-time pharmacokinetic-pharmacodynamic display system.38

The choice of anesthetic drugs and their clinically effective concentrations is

based on a number of factors. The opioid is selected based on a combination of the

potency and the speed of decay of the drug at the effect site.39

For shorter procedures a

drug with rapid induction and a very short half life, such as remifentanil (t1/2 = 0.9 min.)

is ideal. For longer procedure a long acting drug such as, fentanyl (t1/2 = 4.7 min.) or

sufentanil (t1/2 = 5.9 min.) may be preferred. The hypnotic drug is selected based on the

patient’s preexisting clinical conditions, the intensity and duration of procedure. The drug

3

dosage is computed using the patient parameters that influence uptake and delivery, such

as age, weight, height, etc. The drug dose regimen that is determined based on knowledge

of clinical testing of the drug is adapted intraoperatively to suit the patient.

The accurate titration of drugs such that the level of drug is just enough to block

responses is highly desirable. This enables the clinician to provide an adequate level of

anesthesia within the operating room and facilitate rapid recovery once the procedure has

ended. Several factors such as interpatient pharmacokinetic and pharmacodynamic

variability make this task challenging. Pharmacokinetic variability can be described as

the variation in the uptake and distribution of drug between patients. It is on the order of

70% (i.e. with an infusion rate of 10 mg/kg/hr of propofol the blood concentration may

vary from 3 to 5 mg/L in patients). Differences in cardiac output, hepatic perfusion,

enzyme activity and protein binding contribute to this variability.6,40-46

Pharmacodynamic

variability can be described as the variation of the potency of the drug in each patient.

Several investigators have quantified this variation to be on the order of 300-400%47-54

(i.e., some patients may lose consciousness at a blood concentration of 1 mg/L while

other’s may need as much as 5 mg/L before they are sedated). The factors responsible for

pharmacodynamic variability are still unclear although some investigators suspect the

variability arises from genetic differences in receptor pharmacology.55

Clinicians cope

with this combined variability by adjusting the drug dose to suppress patient responses.

These limitations necessitate the development of methodical schemes to determine the

dose for combinations of anesthetic drugs that will work in all types of patients.

To determine the level of anesthesia clinicians often depend on unreliable,

nonspecific measures56

such as hemodynamics, reflexes to stimuli, spontaneous

4

respiration rate, etc. to determine the level of anesthetic effect. To use these methods, the

clinician is dependent on a number of factors such as training, experience and availability

of intraoperative monitoring methods. It is difficult to monitor some measures such as

blood pressure as a continuous signal intraoperatively. Hemodynamic responses are often

affected by the presence of vasoactive and ionotropic drugs.57

The lack of definite

indicators for sedation and analgesia make the precise delivery of anesthetics drugs

challenging. The use of patient responses to accurately titrate anesthetic drugs

intraoperatively is not viable ethically, as eliciting patient responses may cause patient

discomfort. Thus, many clinicians often chose to operate with a more than adequate level

of drug to prevent patient awareness and responses. Even though there are no direct

adverse effects with using this range of concentrations they may result in delayed

emergence and higher operating costs. A real-time monitoring system may address may

address many of these concerns.

It is well understood that patterns within the electroencephalogram (EEG) are

good correlates to clinical endpoints such as loss of consciousness.58, 59

Despite this, EEG

monitors have not been widely accepted intraoperatively by anesthesiologists. The

primary reasons are (1) EEG is a data intensive signal and analysis in real time is tedious,

(2) Large inherent variability in the signal (3) lack of clear guidelines to assess changing

levels of sedation and (4) Not all drugs produce a similar effect on the EEG at a given

clinical endpoint (loss of consciousness). These limitations are somewhat addressed by

CNS effect monitors that extract salient features of the EEG waveform that correlate well

with depth of anesthesia and quantify them in to a index.

5

The processed EEG has emerged as an important surrogate measure of CNS drug

effect.11, 12

Surrogate measures are employed when the clinical drug effect of interest is

difficult or impossible to measure. The processed EEG has many characteristics of the

ideal surrogate. In contrast to more clinically oriented measures of drug effect, it is an

objective, continuous, reproducible, noninvasive, high resolution signal. It can also be

used as an effect measure when an experimental subject is unconscious or apneic,

whereas many of the more clinically oriented measurements require awake, cooperative

subjects. The processed EEG signal has been commercialized by number of

manufacturers. Preliminary studies validating the bispectral index (BIS), reported the

concentration-BIS relationship and examined the ability of the BIS monitor to track

sedation.12

A major limitation of several such studies is that they report the predictive

performance of the BIS monitor when drugs are used in isolation. Since modern

anesthesia calls for a balanced sedation and analgesia, opioids are almost ubiquitous in

pain management. A study that evaluates such monitoring technologies must replicate the

clinical environment in which they are intended for use. Although the ability of processed

EEG monitors to track the sedative state has been extensively studied, the ability of these

monitors to detect pain in patients who are undergoing a surgical procedure has not been

reported. If processed EEG monitors correlate with patient responses to pain, they will be

an invaluable tool to identify inadequate anesthesia in patients when traditional markers

such as movement and heart rate are obscured by the presence of other drugs.

Recent advances in drugs, monitoring technology and combined pharmacologic

knowledge have shown that drugs can be improved in clinical anesthetic practice.

Accurate knowledge of the drug disposition and a method of feedback of the analgesic

6

and sedative drug effect may eventually lead to the development of a closed loop

computer controlled anesthesia system.60, 61

1.1 Goals

This dissertation aims to improve anesthetic drug management in two steps.

Pharmacokinetic and pharmacodynamic models can be used to predict the level of

sedation and analgesia in a patient. The first step is to construct pharmacodynamic

models for a commonly used opioid (remifentanil) and volatile hypnotic drug

(sevoflurane). We can then use simulations based on these models, to identify certain

factors which when applied to anesthetic practice will improve clinical outcomes.

Specifically simulations will be used to identify a combination of opioid and hypnotic

that will provide adequate anesthesia and enable the patient to regain sensation quickly

after the procedure. Further, these models will help understand the combined effects of

volatile anesthetics and opioids. Our second goal is to provide the clinician with a means

for feedback of the patient’s anesthetic state within the operating room. To achieve this

we will test emerging technologies in their ability to monitor adequate anesthesia and

their ability to detect patient responses. Understanding the operating characteristics of

such monitors will improve intraoperative monitoring and enable more accurate drug

administration.

Chapters 2 and 3 of this dissertation describe pharmacodynamic models that

estimate the interaction between commonly used hypnotic and opioid drugs. Chapter 2 in

specific describes the interaction between a volatile agent and an opioid drug. Chapter 2

fills in an important void in our understanding of volatile anesthetic and opioid

interactions. The quantitative description of analgesic and sedative effect caused by the

7

combinations of drugs can be extended to other volatile anesthetics and opioids. Chapter

2 also introduces an optimization technique used to estimate context sensitive optimal

combinations that ensure adequate anesthesia by targeting drug doses that produce

sedation and analgesia in a wide patient population and speed up emergence. After

further validation, the clinical application of these results will lead to accurate anesthetic

dosing in the general patient population. Chapter 3 extends the methods described in

Chapter 2 to estimate optimal combinations of an intravenous hypnotic drug and an

opioid. Chapter 3 introduces methods by which number of clinical endpoints (adequate

sedation, analgesia and rapid emergence) can be ensured simultaneously through drug

optimization. These techniques can be extended to wide range of anesthetic procedures

that require a particular level of sedation and analgesia (e.g., outpatient procedures that

are common in a gastroenterology clinic have specific sedation and analgesia

requirements that differ from the typical surgical procedure). This technique can also be

used to ensure other desirable clinical outcomes such as minimizing cost of anesthetics,

minimal respiratory depression or preventing side effects such as nausea that are

associated with a specific drug concentration.

Chapters 4 and 5 examine the performance of two emerging processed

electroencephalographic (EEG) monitors that can be used to determine the depth of

anesthesia in real-time. In Chapter 4, the ability to monitor depth of sedation is studied.

Processed EEG monitor targets that coincide with adequate analgesia and sedation are

described. The manufacturers of processed EEG monitors recommend certain monitor

indices that are associated with adequate sedation, the results presented in this chapter

prove that the monitor index associated with adequate sedation varies as function of the

8

combination of drugs used to provide anesthesia. These limitations are addressed by the

suggesting processed EEG monitor targets associated with adequate anesthesia. Chapter 5

examines the changes in processed EEG monitor indices in response to stimulation. The

results of this exploratory study highlight the need for further algorithm development in

the processed EEG monitors. Finally, Chapter 6 summarizes important conclusions from

this work and suggests future work in this area of research.

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Intraindividual variability in male hepatic CYP3A4 activity assessed by alfentanil and

midazolam clearance. J Clin Pharmacol 1999; 39: 664-9

48. Schneider G, Sebel PS: Monitoring depth of anaesthesia. Eur J

Anaesthesiol Suppl 1997; 15: 21-8

49. Berne RM, Levy MN: Physiology. Fourth Edition, Mosby 1998

50. Rampil IJ, Lockhart SH, Eger EI, 2nd, Yasuda N, Weiskopf RB, Cahalan

MK: The electroencephalographic effects of desflurane in humans. Anesthesiology 1991;

74: 434-9

51. Rampil IJ: A primer for EEG signal processing in anesthesia.


52. Gan TJ, Glass PS, Windsor A, Payne F, Rosow C, Sebel P, Manberg P:

Bispectral index monitoring allows faster emergence and improved recovery from

propofol, alfentanil, and nitrous oxide anesthesia. BIS Utility Study Group.


53. Glass PS, Bloom M, Kearse L, Rosow C, Sebel P, Manberg P: Bispectral

analysis measures sedation and memory effects of propofol, midazolam, isoflurane, and

alfentanil in healthy volunteers. Anesthesiology 1997; 86: 836-47

54. Locher S, Stadler KS, Boehlen T, Bouillon T, Leibundgut D, Schumacher

PM, Wymann R, Zbinden AM: A new closed-loop control system for isoflurane using

bispectral index outperforms manual control. Anesthesiology 2004; 101: 591-602

55. Glass PS, Rampil IJ: Automated anesthesia: fact or fantasy?


CHAPTER 2

OPIOID-VOLATILE ANESTHETIC SYNERGY AND CONTEXT

SENSITIVE TARGETS §

2.1 Abstract

2.1.1 Background

Combining a hypnotic and an analgesic to produce sedation, analgesia, and

surgical immobility required for clinical anesthesia is more common than administration

of a volatile anesthetic alone. The aim of this study was to apply response surface

methods to characterize the interactions between remifentanil and sevoflurane.

2.1.2 Methods

Sixteen adult volunteers received a target controlled infusion of remifentanil (0-

15 ng•mL-1

) and inhaled sevoflurane (0-6 vol %) at various target concentration pairs.

After reaching pseudo-steady-state drug levels, the Observer's Assessment of

Alertness/Sedation score and response to a series of randomly applied experimental pain

stimuli (pressure algometry, electrical tetany, and thermal stimulation) were observed for

each target concentration pair. Response surface pharmacodynamic interaction models

were built using the pooled data for sedation and analgesic endpoints. Using computer

§ Accepted for publication in Anesthesiology, February 2006. Reprinted with permission

from Anesthesiology. Copyright 2006, American Society of Anesthesiologists. Original

article titled: “Opioid-volatile anesthetic synergy: A response surface model with

remifentanil and sevoflurane as prototypes.”

14

simulation, the pharmacodynamic interaction models were combined with previously

reported pharmacokinetic models to identify the combination of remifentanil and

sevoflurane that yielded the fastest recovery (Observer’s Assessment of

Alertness/Sedation score ≥ 4) for anesthetics lasting 30-900 minutes.

2.1.3 Results

Remifentanil synergistically decreased the amount of sevoflurane necessary to

produce sedation and analgesia. Simulations revealed that as the length of the procedure

increased, faster recovery was produced by concentration target pairs containing higher

amounts of remifentanil. This trend plateaued at a combination of 0.75 vol % sevoflurane

and 6.2 ng•mL-1

remifentanil.

2.1.4 Conclusion

Response surface analyses demonstrate a synergistic interaction between

remifentanil and sevoflurane for sedation and all analgesic endpoints.

2.1.5 Acknowledgements

Supported in part by a research grant from Alaris Medical Systems, Inc., San

Diego, CA, U.S.A. (TDE) and by the National Institute of Biomedical Imaging and

Bioengineering of the National Institutes of Health 8 RO1 EB00294 (SCM and DRW).

Portions of this work have been presented at the 79th

Annual Clinical and

Scientific Congress of the International Anesthesia Research Society in Honolulu, HI,

March 15, 2005, (Poster S-405) and the 80th

Annual Clinical and Scientific Congress of

the International Society of Anesthesia Research in San Francisco, CA, March 27, 2006.

15

The authors would like to thank Steve E. Kern, Ph. D. (Associate Professor,

Departments of Pharmaceutics and Anesthesiology, University of Utah), for his insightful

comments and feedback in the preparation of this manuscript.

2.2 Introduction

In the modern era, anesthesia is at least a two drug process consisting of an opioid

and a sedative. The sedative component is typically provided by a volatile anesthetic or

the intravenous sedative propofol. The opioid component is most commonly provided by

fentanyl or one of its congeners. Although it is possible to achieve anesthesia with high

doses of the sedative alone (i.e., a volatile anesthetic or propofol), this approach is often

associated with excessive hemodynamic depression1 and other adverse effects such as

prolonged time to awakening from anesthesia.2 Thus, for practical purposes, the current

state of the art is to produce anesthesia with an opioid and a sedative in combination.

Opioid-hypnotic drug interaction studies have traditionally evaluated the effects

of adding one or two fixed doses or concentrations of a drug to several defined

concentrations of the second drug.3-7

Analysis of this interaction data is most commonly

performed utilizing an isobologram or demonstrating the shift of parallel dose-response

curves. Studies designed to characterize the interaction between sedatives and opioids

using these traditional methods confirm the synergistic nature of the pharmacodynamic

interactions.8-10

A significant drawback of the isobologram technique is that it describes

the interaction at a single level of drug effect (e.g., the Minimum Alveolar Concentration,

MAC- the end-tidal concentration of volatile anesthetic where there is a 50% probability

of moving to a skin incision-among others). Recently, response surface methodology has

been applied to the study of anesthetic drug interactions.11-14

Response surface models

16

allow the complete characterization of pharmacodynamic interactions over the entire

spectrum of possible concentration pairs.12,15

Isobolograms represent just a single

“slice” through the response-surface, whereas the response surface approach provides

information over the entire spectrum of drug effect.

Response surface pharmacodynamic interaction methods provide a framework to

define and explore opioid-hypnotic interactions. Information about whether the

interaction between two drugs is supradditive (synergistic), additive, or antagonistic is

easily determined by the morphology of the surface. Furthermore, through computer

simulation, it is possible to combine these response surface pharmacodynamic models

with pharmacokinetic models to identify combinations of drugs that produce the same

probability of producing a therapeutic effect while optimizing some other desirable

outcome, such as the speed of awakening from anesthesia.8

Prior work in our laboratory created response surface pharmacodynamic models

for remifentanil and propofol in combination.13

The current study is intended to extend

this work to the interaction between volatile anesthetics and opioids using sevoflurane

and remifentanil as prototypes of their respective drug classes. The principle aim of this

study was to characterize the pharmacodynamic interactions of remifentanil and

sevoflurane in producing sedation and analgesia using response surface models. We

hypothesized that sevoflurane and remifentanil would demonstrate synergistic

interactions for all the analgesic and sedative endpoints. By quantitatively describing

these interactions and utilizing previously described pharmacokinetic models, we

hypothesized that we could determine, through simulation, those combinations of

17

sevoflurane and remifentanil that would provide clinically adequate anesthesia and result

in the most rapid emergence from anesthetics of varying durations.

2.3 Materials and Methods

2.3.1 Volunteer Recruitment and Instrumentation

After approval by the Human Institutional Review Board at the University of

Utah Health Sciences Center (Salt Lake City, Utah, U.S.A.), informed written consent

was obtained from 16 healthy adult male and female volunteers. Eligible subjects had an

American Society of Anesthesiologists Physical Status of I, were nonsmokers, were 18–

45 years of age, and deviated by no more than 25% from their ideal body weight.

Volunteers who had a history of significant alcohol or drug abuse, a history of allergy to

opioids, a family history of malignant hyperthermia , or a history of chronic drug use or

medical illness that is known to alter the pharmacokinetics or pharmacodynamics of

opioids or inhalation anesthetics were not eligible.

After a period of overnight fasting, volunteers had an intravenous catheter placed

for fluid and drug administration, and electrocardiogram, pulse oximetry, non-invasive

blood pressure, expired carbon dioxide and expired anesthetic gas monitoring were

applied. To measure the response to electrical tentanic stimulation, surface electrodes

were placed at the posterior tibial nerve. Prior to administration of the study drugs,

volunteers were treated with 0.2 mg glycopyrrolate to prevent bradycardia, and 1 mg

pancuronium to prevent muscle rigidity due to the opioid infusion. Each volunteer

received 30 mL of sodium citrate by mouth.

18

2.3.2 Study Design

The study was an open-label, randomized, parallel group study using a crisscross

design as advocated by Short, et al.16

to assess drug interactions. Similar methodology

was used in our earlier report describing the interactions between propofol and

remifentanil.13

Each volunteer was randomized into one of two study groups. The

primary drug for the first group was remifentanil (0.5-15 ng•mL-1

) and for the second

group the primary drug was sevoflurane (0.3-6 vol %). The primary agent was

administered from a low to a high concentration in random steps determined a priori to

allow characterization of the entire concentration range when all data were pooled

(Figure 2.1). After obtaining pharmacodynamic measurements at the highest

concentration of the primary agent, a washout period was observed during which time the

primary agent decayed to predicted concentrations below the initial target concentrations.

This was followed by the administration of the secondary drug at a stable background

level. The primary agent was administered from low to high concentration in the same

steps as in the initial period. Following another washout period, a higher background

level of the secondary drug was administered before the primary agent was administered

from low to high concentration in the same steps. Upon completion of this third set of

data collection, all of the drugs were discontinued and the volunteer was allowed to

recover.

2.3.3 Drug Delivery

Remifentanil was administered to specific predicted effect site concentration

targets using a computer assisted infusion pump (Pump 22, Harvard Apparatus, Limited,

Holliston, MA ) utilizing the pharmacokinetic parameters described by Minto, et al.,17

19

Figure 2.1: A schematic summary of the infusion scheme. During each of the three study

periods the primary drug is administered in a stepwise fashion (solid black line), while in

the second and third study periods, the second drug (grey filled area) is held at a constant

predicted effect site concentration or measured alveolar concentration. In between each

study period there is a washout phase, during which the primary and secondary drugs are

allowed to decay to predicted concentrations below that of the subsequent target

concentration pair.

20

and controlled by STANPUMP software.§ Sevoflurane was administered in 2-10 L•min

-1

of oxygen by a tight fitting mask connected to a standard circle anesthesia circuit attached

to an anesthesia machine (Drager Medical, Inc., Telford, PA ).

2.3.4 Effect Measurements

Five minutes after achieving the targeted effect-site concentration (or stable end-

tidal concentration) for a primary drug “step,” a battery of pharmacodynamic assessments

were made. Effect measures included the Observer’s Assessment of Alertness/Sedation

score (OAA/S)18

and three surrogates for surgical stimulus- pressure algometry and

tetanic electrical stimulation, similar to those previously described by Kern, et al.,13

and

thermal stimulation. All stimuli were applied until reaching supra-maximal levels-50 mA,

50 PSI, and 50 °C for 5 seconds. The maximum intensity of the stimulation was

decreased from those utilized by Kern, et al.,13

because intensity levels of 60 mA and 60

PSI were found to be well above the supra-maximal stimulus intensity. Sedation was

measured first and then the experimental pain stimuli were measured in random order. In

terms of sedation, volunteers were considered nonresponsive if the OAA/S was ≤ 1 (loss

of response to “shake and shout,” Table 2.1). Once the volunteer became nonresponsive

(OAAS ≤ 1), direct laryngoscopy was performed with a Macintosh #3 blade to achieve a

Cormack Grade I view19

at each target concentration pair. The volunteer was considered

responsive to the noxious stimuli when the volunteer exhibited painful verbalization,

withdrawal movement, or an increase in heart rate of 20% over the prestimulus level.

With the exception of laryngoscopy, baseline measurements of the subject response to

§ Available from Steven L. Shafer, M.D., at http://anesthesia.stanford.edu/pkpd/. Posted

April 29, 1998. Accessed October 18, 2005.

21

Table 2.1: Observer’s Assessment of Alertness/Sedation Score (OAA/S)*

Responsiveness Score

Responds readily to name spoken in normal tone 5

Lethargic response to name spoken in normal tone 4

Responds only after name is called loudly and/or repeatedly 3

Responds only after mild prodding or shaking 2

Does not respond to mild prodding or shaking 1

Does not respond to noxious stimulus 0

1 For the purposed of this study, an OAA/S ≤ 1 was considered nonresponsive, whereas

an OAA/S ≥ 4 was considered “awake.”

22

each surrogate effect were made at the start of the study day in the absence of drugs. Two

kinds of data were recorded as surrogate measurements to surgical stimulus- the level of

tolerated stimulus (a continuous data variable) and a quantal response of whether the

volunteer could tolerate the maximal stimulus level (e.g., no withdrawal, no increase in

heart rate or blood pressure)20

. By convention, the maximum stimulation levels for the

surrogate pain measures were 5 seconds of 50 mA for tetanic electrical pain, 50 PSI for

pressure algometry, and 50 °C for thermal stimulation.

2.3.5 Data Analysis

Demographic data for the volunteers in each group were compared utilizing an

unpaired, two-sided t-test using StatView version 5.0.1 (SAS Institute, Inc., Cary, NC)

with P < 0.05 considered significant. All demographic data were reported as means with

standard deviations.

Data points that revealed a hyperalgesic response to a noxious stimulation at low

sevoflurane concentrations 21

were discarded in order to allow modeling of the drug

response as a monotonic function.

2.3.6 Response Surface Models

Response surface models were constructed for each pharmacodynamic response

using the Logit model as shown below: 22

)( 32101

1CrCsCrCs

eEffect

⋅⋅−⋅−⋅−+=

ββββ

where Cs and Cr are the concentration of sevoflurane (alveolar end-tidal concentration,

vol %) and remifentanil (effect site concentration, ng•mL-1

, as predicted by Stanpump

),

23

respectively, and ßi are the parameters describing the response surface. Additional details

of the Logit model are provided in Appendix A.

For each pharmacodynamic response, the data were combined and used to fit the

three-dimensional response surface using a naïve pooled technique. Model coefficients

and standard errors were estimated using MATLAB (MathWorks Inc., Natick, MA).

Models were built by an iterative process in which the log likelihood (LL) between the

observations and the model predictions was maximized. The contribution of each

coefficient was evaluated by excluding it from the model and determining whether the

model deteriorated significantly using the likelihood ratio test (∆ Likelihood Ratio ≥

30%). The standard error of the model parameters was estimated using the bootstrap

method for 5000 iterations.23

Model performance was evaluated by assessment of Error Prediction (observed vs.

predicted probability of effect for each dose combination) and the correlation coefficient.

The Error Prediction is defined as the following:

ObservededictedObservedXError ediction /Pr100Pr −=

The correlation coefficient of the regression parameter estimates was used to

evaluate how well the nonlinear regression models described the observed data. A large

value of the correlation coefficient (≥ 0.7) indicates that the responses predicted from the

surface described the observed data well.24

2.3.7 Determination of Synergy

Using the response surfaces for surrogate surgical stimuli and sedation, it is

possible to simulate two-dimensional concentration-effect relationship curves for

sevoflurane at a variety of remifentanil concentrations.9 Each of these curves represents

24

a vertical slice from the respective response surface. The synergistic effects of combining

remifentanil and sevoflurane in producing sedation and analgesia are demonstrated by

examining the change in the slope and the leftward shift of the sevoflurane concentration-

effect curves.

2.3.8 Combined Pharmacokinetic and Pharmacodynamic Simulations

The time to regaining responsiveness from a single drug anesthetic is determined

by the pharmacokinetics of the individual drug, the concentration-effect relationship, and

the duration of administration of the drug.2,25

For two-drug anesthetics, the time to

awakening is not only dependent on the individual drug pharmacokinetics and the

duration of the anesthetics, but also on the target concentrations achieved for each of the

drugs administered.8 To provide a clinically useful context for applying the response

surface models to everyday anesthesia practice, the pharmacodynamic response surface

models from this study were combined with pharmacokinetic models17,26

using computer

simulation as described by Vuyk, et al.,8 to identify target concentration pairs of

remifentanil and sevoflurane that provided a high probability of nonresponsiveness to

noxious stimulation and the most rapid emergence after cessation of anesthetic

administration. Additional details are provided in Appendix B.

The sevoflurane model described by Lerou, et al., 26

and the remifentanil model

reported by Minto, et al.,17

were utilized to simulate a range of alveolar concentrations

and effect site concentrations of the sevoflurane and remifentanil, respectively, that

produced a 95% probability of nonresponsiveness to the maximal tetanic stimulus of 50

mA, as determined by the response surface. Electrical tetanic stimulation is a surrogate

noxious stimulus that is thought to be similar to a skin incision.27

These alveolar and

25

effect site concentrations were maintained at these levels for one hour, after which time

the drugs were discontinued and the “washout” of the anesthetics was simulated. The

shortest time during the washout until the drug interaction model predicted a 95%

probability that OAA/S was ≥ 4 was found through iterative simulation utilizing a binary

search algorithm.28

The initial concentration pair was randomly picked from those target

concentration pairs located along the EC95 isobole for tetanic stimulation. After

calculating the recovery time (OAA/S ≥ 4) for this initial target concentration pair, a

fixed “step” of a 25% change in either the remifentanil concentration or the sevoflurane

concentration in a random direction along the isobole was made and the time to

awakening was calculated. If this time was higher than that of the previous concentration

pair, the next concentration pair was picked half-way between the previous point and this

point; otherwise, the next concentration pair was a picked to be the same size step change

in concentration away from the previous point. This step-wise search was continued until

a point was reached where recovery time was within 5% of the previously calculated

recovery time at the previous concentration pair. The combination of sevoflurane and

remifentanil that resulted in the quickest recovery (OAA/S ≥ 4) was determined for

anesthetics of 30-900 minutes in duration.

2.4 Results

All 16 volunteers completed the study. The demographics of the two groups are

shown in Table 2.2. There were no differences between the groups except that the

remifentanil group was predominately male volunteers, whereas the sevoflurane group

contained equal numbers of male and female volunteers.

26

Table 2.2: Demographics of Study Volunteers*

Group 1

Sevoflurane

Group 2

Remifentanil

Age [years] 25.0 ± 4.2 23.1 ± 2.7

Weight [kg] 70.8 ± 13.0 74.5 ± 9.3

Height [cm] 174.3 ± 9.0 177.8 ± 8.4

Sex [M:F] 4:4 7:1

1 All values are given as mean ± standard deviation, except for the ratio of males to

females.

27

2.4.1 Response Surface Models and Determination of Synergy

The parameters for all the response surface models were identifiable. The Logit

model parameters estimated through nonlinear regression are shown in Table 2.3. The

estimates of “goodness of fit” (e.g., Log Likelihood, Standard Errors, and Correlation

Coefficient) suggest that the models describe the data well. Based on the drug

concentrations required to achieve nonresponsiveness, thermal stimulation was the

mildest and tetanic stimulation was the most noxious stimulus. All of the simulated

concentration-effect relationship curves from the response surface models showed

synergy for both analgesia and sedation.

The response surface for sedation (OAA/S ≤ 1) of the unstimulated volunteers is

shown in Figure 2.2. The response surface for tetanic stimulation is shown in Figure 2.3.

The other pain stimuli surfaces (not shown) were of very similar shape. The raw data

used to create these surfaces are shaded based on the residual error between the measured

response and model prediction. Throughout most of the clinically relevant range of

concentrations (sevoflurane 0- 3 vol % and remifentanil 0- 7.5 ng•mL-1

) the residual error

is below 10%. The OAA/S score and the tolerance to electrical tetanic stimulation are

shown topographically in Figure 2.2b and Figure 2.3b, respectively. Figures 2.4a and

2.4b are two-dimensional concentration-response curves for sevoflurane at a variety of

remifentanil concentrations that are based on the response surfaces for surrogate surgical

stimuli and sedation. Each of these concentration-response curves was determined by

taking a vertical slice through the respective response surface (Figure 2.2a and 2.3a,

Table 2.4).

28

Table 2.3: Mean Model Parameters for the Logit Response Surface*

ß0 ß1 ß2 ß3 Log

Likelihood

Correlation

Coefficient

Pressure algometry 3.82 2.43 0.54 1.27 -78.90 0.78

Tetanic Stimulation 3.27 0.97 0.088 1.09 -84.06 0.72

Thermal stimulation 3.38 1.32 0.55 3.47 -103.99 0.73

Laryngoscopy 3.70 2.36 0.54 1.22 -82.48 0.78

OAA/S 7.30 7.84 0.23 3.94 - 24.12 0.89

* Model parameters are listed for all values. Standard errors for all parameters were <

0.01, as determined by the bootstrap method. OAA/S = Observer assessment of alertness

and sedation score.

29

Figure 2.2: The remifentanil-sevoflurane interaction for sedation. The Logit response

surface model prediction for sedation for unstimulated volunteers is presented in the top

panel (Figure 2.2a). An Observer’s Assessment of Alertness/Sedation (OAA/S) score ≤ 1

represents a sedated volunteer. A 0 indicates an OAA/S ≥ 2 and a 1 indicates an OAA/S ≤

1. The symbols show measured responses and the surface predicted by the model is

represented by the grid-lined surface. The raw data used to create this model is shaded

based on the residual error. A topographic view of the 50% and 95% effect isoboles for

probability of being sedated is presented in the bottom panel (Figure 2.2b). The OAA/S

score at each target concentration pair is overlaid.

30

Figure 2.2

a)

b)

31

Figure 2.3: The remifentanil-sevoflurane interaction for electrical tetanic stimulation.

The top panel (Figure 2.3a) shows the Logit response surface model prediction for tetanic

stimulation of 50 mA. A 0 indicates a response (movement or a 10% increase in blood

pressure or heart rate) to a 50 mA stimulus current and a 1 indicates no response to 50

mA stimulus current. The symbols show measured volunteer responses to 50 mA of

stimulus current and the surface predicted by the model is represented by the grid-lined

surface. The raw data used to create this model is shaded based on the residual error. The

bottom panel (Figure 2.3b) shows a topographic view of the 50% and 95% effect isoboles

for probability of tolerating a 50 mA stimulus current. The percentage of tolerated

stimulus current at each target concentration pair is overlaid.

32

Figure 2.3

a)

b)

33

Figure 2.4: The effect of adding remifentanil on the concentration-effect relationships of

sevoflurane for sedation (Figure 2.4a) and analgesia (Figure2.4b). Each curve represents

the concentration-effect relationship for sevoflurane with a fixed effect site concentration

of remifentanil simulated from the corresponding response surface model. The shift in the

curves toward the left indicates that much less sevoflurane is needed when remifentanil is

added, demonstrating the significant pharmacodynamic synergy between the sedative and

the opioid. Note that the magnitude of the leftward shift decreases as the remifentanil

concentration increases (i.e., there is a ceiling effect).

34

Figure 2.4

a)

b)

35

Table 2.4: Reduction in Sevoflurane Requirements by Remifentanil*

Remifentanil

Ce

[ng••••mL-1

]

Remifentanil

Infusion Rate

[mcg••••kg-1••••min

-1]

Sevoflurane

EC95% OAA/S ≤≤≤≤ 1

[vol %]

Sevoflurane

EC95% Tetanic

Stimulation

[vol %]

0 0 1.30 6.48

1.25 0.05 0.78 2.63

5 0.18 0.33 0.90

7.5 0.27 0.23 0.61

* The reduction in the alveolar concentration of sevoflurane that produces a 95%

probability (EC95%) of an OAA/S score ≤ 1 or no movement or hemodynamic response to

a 50 mA tetanic stimulation by the addition of remifentanil in doses ranging from 0-0.27

mcg•kg-1•min

-1 (Effect Site Concentration, Ce , 0-7.5 ng•mL

-1) are reported. All infusion

rates were calculated for a hypothetical 30 year old male who weighed 80 kg and was 183

cm tall utilizing Stanpump (http://anesthesia.stanford.edu/pkpd/).

36


For shorter procedures the target concentration pairs that resulted in the most

rapid return to responsiveness approached the maximally synergistic combination-a

combination that lies on the point of the response surface where the surface curves

maximally towards the origin. (Figure 2.5a) At this combination, the plasma

concentrations of the drugs are both relatively low and therefore the plasma

concentrations of the drugs decline to sub-clinical levels quickly (Figure 2.5b). As the

duration of the anesthetic increases, the target concentration pairs with the shortest

recovery time must be adjusted to be weighted towards the drug with the shorter acting

kinetic profile, in this case remifentanil. By avoiding a large increase in the accumulation

of sevoflurane in the body, the kinetics of washout of these combinations would allow

rapid emergence from anesthesia. This trend plateaued at 0.75 vol % sevoflurane and 6.2

ng•mL-1

remifentanil (Figure 2.6, Table 2.5).

2.5 Discussion

In this study we utilized response surface models to characterize the

pharmacodynamic interactions between a potent volatile agent, sevoflurane, and a

synthetic opioid, remifentanil, across a wide range of concentration pairs. With these

pharmacodynamic models, we determined that the addition of remifentanil to sevoflurane

anesthesia not only synergistically decreases the response to painful stimulation but also

synergistically potentiates the sedative effects of the volatile anesthetic. Furthermore,

utilizing these pharmacodynamic models and previously described pharmacokinetic

models, 17,26

we performed simulations to identify the target concentration pairs of

remifentanil and sevoflurane that produced clinically adequate anesthesia (e.g., ≥ 95%

37

Figure 2.5: The results of computer simulations designed to identify optimal target

concentration pairs of remifentanil- and sevoflurane that minimize the time to

responsiveness. The top panel (Figure 2.5a) shows the predicted decline in effect site and

alveolar concentrations for remifentanil and sevoflurane after stopping drug

administration regimens targeted to reach the EC95 isobole for tetanic stimulation for one

hour. The EC95 isobole is on the “floor” of the cube; the vertical axis represents time

elapsed since stopping the administration of the drugs. The isobole representing a 95%

probability of the return of responsiveness (Observer’s Assessment of Alertness/Sedation

score ≥ 4) is shown by a dotted line that is superimposed on the concentration decay

curves. The highlighted curve is the sevoflurane and remifentanil target concentration

pair that resulted in the fastest return of responsiveness. The bottom panel (Figure 2.5b)

shows the time in minutes to the return of responsiveness after a 1 hour procedure in

which sevoflurane and remifentanil were administered to target concentration pairs on the

EC95 isobole for tetanic stimulation. The highlighted trace on the panel on the left is

shown topographically. The minimum time to regain responsiveness represents the target

concentration pairs for a 1 hour procedure.

38

Figure 2.5

a)

b)

39

Figure 2.6: The optimal combinations of remifentanil and sevoflurane to maintain

adequate anesthesia and promote rapid emergence. The combinations that produced the

quickest time to regain responsiveness (Observer’s Assessment of Alertness/Sedation

score ≥ 4) at various durations (in hrs) are shown. For example: In a 1 hour procedure

target concentrations of 1.05 vol % of sevoflurane and 4.3 ng•mL-1

of remifentanil result

in the fastest return of responsiveness. The simulations show that optimal combination

changes as a function of length of procedure. Although a target concentration pair with

higher remifentanil concentrations provides a faster recovery in longer cases,

remifentanil-sevoflurane mixtures in which sevoflurane is less than 0.75 vol % show no

significant advantage.

40

Table 2.5: Simulation Results for Anesthetics 30-900 Minutes in Length*

Length of

Anesthetic

[hr]

Shortest

Recovery Time

[min]

Remifentanil

Ce

[ng••••mL-1

]

Remifentanil

Infusion Rate

[mcg••••kg-1••••min

-1]

Sevoflurane

Alveolar

vol %

0.5 4.5 4.1 0.15 1.10

1 5.0 4.3 0.16 1.05

2 5.8 4.9 0.18 0.93

4 6.7 5.2 0.19 0.88

7 7.2 6.1 0.22 0.75

10 7.4 6.1 0.22 0.75

15 7.5 6.2 0.23 0.74

20 7.6 6.1 0.22 0.75

24 7.7 6.1 0.22 0.75

* The effect site concentration (Ce) and infusion rate for remifentanil and the alveolar end

tidal concentration of sevoflurane that produced the shortest recovery times are reported

for anesthetics lasting 0.5-24 hours. All simulations were performed for a hypothetical 30

year old male who weighed 80 kg and was 183 cm tall.

41

probability of no response to painful stimulation) while allowing the quickest time to

awakening (e.g., ≤ 5% probability of OAAS ≤ 4) for surgical procedures of increasing

duration.

These simulations demonstrated that there was a plateau in the utility of

remifentanil to decrease the amount of sevoflurane necessary to produce clinically

adequate anesthesia (sedation and nonresponsiveness to noxious stimulation).


Response surface methods have been utilized to model the interactions between a

variety of combinations of anesthetics, the most common being that of propofol and

remifentanil.8,13,14,29-31

Our results are similar to the findings with propofol and

remifentanil, in that our data demonstrate that the addition of remifentanil to sevoflurane

results in a synergistic effect for both analgesia and sedation. Our results do not agree

with the study by Dahan who found that alfentanil produced no synergistic effect on

sevoflurane induced sedation.32

Dahan used Bispectral Index rather than OAA/S to

measure sedation and used a relatively lower concentration of alfentanil. Our data

evaluated the contribution of higher levels of opioid effect (remifentanil) relative to the

alfentanil concentration range studied by these investigators. Furthermore, we

specifically evaluated the effects of combinations of sevoflurane and remifentanil on

clinical sedation, as measured by the OAA/S, as opposed to the surrogate marker of the

Bispectral Index. Perhaps the limitations of the Bispectral Index algorithm, specifically

its insensitivity to the effect of an opioid on sedation,33

may explain differences in our

results. Alternatively, the fact that we utilized the Logit model for our response surface

data whereas Dahan utilized the Minto response surface models, may have resulted in a

42

“forced fit” of our data to the relatively constrained model. However, the response

surface generally predicted the observed data extremely well (Figure 2.2a and 2.2b and

Table 2.3), and therefore is most likely not a “forced fit.”

Over the past few years, several investigators have utilized response surface

models to determine the interactions between propofol and remifentanil,8,11,13,30

propofol

and alfentanil,34,35

and sevoflurane and alfentanil.32

Each of these authors utilized a

single type of pharmacodynamic model to develop their response surface models. The

pharmacodynamic model described by Greco,12

and utilized by Kern,13

differs from the

pharmacodynamic model developed by Minto,15

and utilized by Dahan,32

in that it

requires the exponent of the response to be fixed, therefore limiting the flexibility of the

model to fit optimally the response data. However, the Greco form of this model provides

a specific parameter that examines the interaction between the two drugs. The models

proposed by Bouillon,11

Bol,30,36

and the Logit model also differ in their mathematical

complexity and physiologic plausibility. Choosing the right model to describe the data is

an empirical process in which the error statistics of each model are used to determine if

increasing the level of complexity allows a better fit of the measured response data.23

However, if a model that has many degrees of freedom is chosen, it is possible to fit a

surface to data from poorly designed trials or studies with inadequate response

sampling.15

For the analysis of our data, we chose the Logit model because it easily allowed

the analysis of data from volunteers with different baseline and maximal responses to the

surrogate pain stimuli and the clinical assessment of sedation. Given the diversity of

different response surfaces models published in the anesthesia literature, the fact that we

43

were able to characterize adequately our data set with the Logit model, which is a

moderately constrained model compared to those proposed by Greco,12

Minto14

or Bol,36

may indicate that the synergism observed by these surfaces is accurate. Minto, et al., have

proposed that there are several criteria necessary for an “Ideal Pharmacodynamic

Interaction Model.”14

The Logit model is able to predict additive, synergistic, and

antagonistic interactions. Simulations of the isoboles that result with changes in the Logit

model’s ß3 coefficient-the coefficient that controls the interaction between the two drugs-

produce isoboles consistent with those of Berenbaum37

(Figure 2.7). The response

surfaces derived from the Logit model were easily derived from a relatively small

number of volunteers from predicted effect-site remifentanil concentrations and measured

alveolar end-tidal sevoflurane concentrations covering the entire clinical range of

concentration pairs.

In addition, the response surface reduces to single drug concentration-response

curves that are similar to those that would be derived by single drug analysis 17,38

as

shown in Figures 2.4a and 2.4b. However, the mathematics of logarithms dictates that

when there is no drug present (i.e., sevoflurane-remifentanil target concentration pair of 0

vol % and 0 ng•mL-1

) there is still a slight effect (approximately 0.0007 probability of no

response). Therefore, the Logit model that we have chosen as the basis of our response

surface analysis meets all but one of the criteria proposed by Minto, et al.,15

albeit that the

predictions made when there are no drugs present is close to no drug effect.

44

Figure 2.7: The isoboles derived from simulated Logit model of the sedation response

surface (Observer’s Assessment of Alertness/Sedation score ≤ 1) to demonstrate additive,

synergistic, and antagonistic interactions, by only modifying the ß3 coefficient. In the

Logit model, the ß3 coefficient controls the interaction between the two drugs- ß3 = 0, ß3

> 0, and ß3 < 0, producing additive, synergistic, and antagonistic interactions. The dotted

line represents the isobole predicted by the Logit model when the drug interaction is

simply additive (ß3 = 0), while the solid line and the dotted line represent the predicted

isoboles when there is a synergistic (ß3 = 3.94) or antagonistic (ß3 = -0.22) drug

interaction.

45


The simulations utilizing pharmacokinetic models and our pharmacodynamic

response surfaces to determine the combination of sevoflurane and remifentanil that

would produce the fastest return of responsiveness for anesthetics of varying durations

provided interesting insight into the role of pharmacokinetics and pharmacodynamics in

optimizing clinical anesthetics. As shown in Figure 2.5a, for a 1 hour duration anesthetic,

the “optimum” combination of sevoflurane and remifentanil is at the point in the center of

the “edge” of the plateau of maximum response-on the isobole that defines 95%

probability of not responding to electrical tetanic stimulation. As the duration of the

anesthetic increases, the optimal combinations shifted toward higher remifentanil

concentrations due to the rapid elimination of remifentanil.

Despite the synergistic interactions between remifentanil and sevoflurane in

providing analgesia and sedation, there was a discrete plateau in the sevoflurane-

remifentanil combinations for the longest of procedures (Figure 2.6). This plateau occurs

at a sevoflurane concentration of 0.75 vol % which correlates with an approximately

66% reduction in the Mean Alveolar Concentration (MAC) of sevoflurane (2.2 vol % for

adult males and females between the ages of 20-50 years).38

The 66% reduction in

sevoflurane requirements coincidentally is between the amount of reduction of MAC

(61%) and MACBAR (Blocks Autonomic Responses, 83%) expected when high doses of

opioids are combined with the modern, potent volatile anesthetics.3,7,38,39

Furthermore,

this value is similar to the MACAWAKE of sevoflurane (0.35 MAC, approximately 0.75

vol %),40

thereby demonstrating that these response-surface models may account for the

fact that opioids themselves cannot provide complete anesthesia.41-43

The major factor

46

preventing a further decrease in the sevoflurane requirement may be the limited reduction

of the MACAWAKE observed with opioids.44

That these sevoflurane-remifentanil response

surface pharmacodynamic models predict interactions that are consistent with clinical

practice further demonstrates that these response surfaces may be useful tools for

understanding anesthetic interactions in the clinical realm.45

2.5.3 Clinical Implications

These response surface models allow the creation of two-dimensional

concentration-effect curves that demonstrate an approximately 6-fold decrease in the

EC95 for sedation and an approximately 10-fold decrease in the EC95 for tolerance of

tetanic stimulation with the addition of 7.5 ng•mL-1

remifentanil (0.27 µg•kg-1•min

-1

infusion) to a sevoflurane anesthetic (Figure 2.4a and 2.4b and Table 2.4).

Based on the synergistic interaction between sevoflurane and remifentanil in

preventing a response to the surrogate surgical stimuli and in producing sedation, the

response surfaces from this study confirm the utility of administrating “balanced”

anesthetics with a combination of a volatile anesthetic and an opioid. The

pharmacokinetic-pharmacodynamic simulations illustrate the benefit of minimizing the

administered dose of even a low solubility volatile anesthetic to near 0.5 MAC in the

presence of remifentanil, an opioid with rapid elimination. This is especially true for

anesthetics with duration of over 5 hours. Whether this results in a pharmacoeconomic

advantage of combining a low dose of sevoflurane with a higher dose of remifentanil will

require prospective studies, because the pharmacoeconomic advantages of a drug are

certainly not limited to just minimizing the time until awakening or the drug acquisition

costs.46

47

2.5.4 Limitations

One of the limitations of our study design is that the response surface model for

sedation was determined in unstimulated volunteers. Because the level of stimulation can

change the depth of sedation, it is possible that our unstimulated volunteer response

surface analysis for sedation may not accurately predict the sedation response of patients

undergoing surgical procedures. In particular, the lack of an endotracheal tube in the

volunteers may have resulted in our measuring deeper levels of sedation than would be

apparent if the endotracheal tube was stimulating a patient or volunteer receiving the

same target concentration pairs of sevoflurane and remifentanil. However, the difficulty

in measuring the level of sedation during stimulation in a volunteer setting (e.g.,

confounding sedation score by stimulation response, etc.) prevented us from collecting

the data that would be needed to estimate a surface with continual stimulation.

A further limitation of our study design was that the surrogate pain stimuli used to

measure the analgesic response in volunteers is only a surrogate of intraoperative surgical

pain. By including a range of experimental pain stimuli to cover the range expected

during a surgical procedure, it is probable that the most stimulating intraoperative events-

surgical incision and laryngoscopy-have been recreated in the volunteer laboratory.

However, since key surgical stimuli can only be applied once (e.g., skin incision, etc.),

and since surgical patients cannot ethically be provided with subtherapeutic combinations

of anesthetics or serve as their own pharmacologic control, volunteer studies are essential

to allow the collection of the high resolution data needed to achieve the goal of mapping

the interaction surface between two agents over the entire concentration spectrum.

48

Another limitation in this study is that we used a pharmacokinetic model to

predict remifentanil effect site concentrations rather than drawing blood samples during

pseudo-steady state to measure remifentanil plasma concentrations. This limitation may

explain the variability found in the single drug dose-response data for remifentanil.47

Mertens, et al., determined that remifentanil can be delivered accurately by target

controlled infusions.48

However, they found that the most accurate and least biased

delivery was achieved when the pharmacokinetic set(s) determined by Egan, et al. 49-51

were utilized. Given the fact that the pharmacokinetic set utilized (by Minto, et al.17

) was

determined in a population very similar to that being studied here, the accuracy and bias

of the target controlled infusion should be at least as accurate as employing the

pharmacokinetic sets of Egan, et al.48

Although we had an unequal number of males and

females in our groups, it is unlikely that this accounted for the pharmacodynamic

variability given that sex has little influence on the pharmacokinetics or

pharmacodynamics of remifentanil17

or sevoflurane.52

Other sources of pharmacokinetic

variability (e.g., age, body weight, cardiac output, etc.) most likely did not contribute

much to the pharmacodynamic variability, given the similarities between groups in the

important covariates.

For the analgesic response measurements we were forced to both limit the

maximum stimulus applied and discard those responses that were below the respective

baseline values. We limited the maximum stimulus applied in order to prevent

irreversible tissue damage in the volunteers. In a previous investigation in our

laboratory,13

we found levels of the pressure, temperature, and electrical current that

could be tolerated without any evidence of long lasting damage. However, this approach

49

may result in censored data that can result in pharmacodynamic response curves that

predict potency lower than the true values. Therefore, extending the application of these

response surfaces beyond the range of concentrations examined by these response

surfaces may result in erroneous conclusions.

Just as difficult of a statistical problem is how to deal with those analgesic

responses that were below the baseline values. This hyperalgesic response has been

observed when low doses of volatile anesthetics are administered to animals and

humans.21

Unfortunately, the models utilized to construct response surfaces require a

monotonic function, and therefore are unable to characterize this phenomenon. Other

investigators often do not observe this hyperalgesic response because the step change in

inhaled anesthetic concentration is either so large that the hyperalgesic concentrations are

“jumped over” or the variability in the analgesic response measurement is so large that

this small hyperalgesic effect is unidentifiable.

The hyperalgesia associated with the presence of low concentrations of volatile

anesthetics21

is different from the hyperalgesia phenomenon occasionally observed after

the administration of remifentanil.53-55

The hyperalgesia observed by some investigators

after remifentanil administration is associated with a rightward shift in the subsequent

analgesic concentration-response curves. Although we did not design this study to

specifically address the presence or absence of remifentanil induced hyperalgesia, we did

not find any difference between the baseline levels of tolerated stimuli (e.g., prior to

remifentanil administration) and the levels of stimuli tolerated at the lowest level of

remifentanil with the first doses of sevoflurane (Study Period II, Remifentanil Group,

One-sided paired t-test, P > 0.05 for all three stimuli). This is consistent with the

50

observations of Lotsch and Angst where hyperalgesia to pressure and electrical

stimulation was not induced by remifentanil.55

The Logit model offered the advantage of being able to easily compensate for

data from volunteers with different baseline and maximal responses to the surrogate pain

stimuli and the clinical assessment of sedation. However, the mathematics of logarithms

dictates that when there is no drug present (i.e., sevoflurane-remifentanil target

concentration pair of 0 vol % and 0 ng•mL-1

) there is still a very slight effect

(approximately 0.0007 probability of no response). Furthermore, the Logit model

requires a dichotomous response-“response” vs. “no response” to a single stimulus

intensity. For the surrogates for surgical stimulus, this was the equivalent of having no

movement and no hemodynamic change when a volunteer received the maximum

possible intensity of the pain surrogate. However, the OAA/S is an ordinal scale that

consists of five different scores (Table 2.1). The Logit model mandated that we choose

which OAA/S scores defined patients who were “awake” and those who were “asleep.”

In order to represent the state most consistent with adequate sedation for surgery, the

response surface model for “general anesthesia” was based on an OAA/S ≤ 1 (“no

response to shake and shout”). On the other hand, to most accurately represent the

emergence from general anesthesia (i.e., suitable for extubation), we chose an OAA/S ≥ 4

(“responds to normal voice”) as the basis of the response surface for awakening from

anesthesia. Although this dichotomous view of general anesthesia is not reflected by the

OAA/S score, it is more consistent with “adequate” general anesthesia-for any given

stimulus at any given time point, anesthesia can be either considered adequate or not.20

The models described by Greco,12

Minto,14

and Bouillon11

would have avoided this

51

complexity because all of these models easily handled continuous response variables.

However, each of these alternative model architectures would have had difficulty

resolving the intersubject variability that naturally exists in the baseline and maximal

tolerated stimulus.

2.5.5 Future Work

Our response surface models for sevoflurane and remifentanil interactions were

developed in volunteers exposed to a variety of surrogate pain stimuli. These models will

need to be validated in a variety of surgical patients receiving these two drugs as the only

anesthetic agents. Further work will need to be done to determine if the surrogate pain

stimuli accurately predict the responses to different surgical stimuli (e.g., skin incision,

abdominal insufflation, placement of Mayfield head fixation, etc.). In addition, there are

conceivably 15 different sedative-opioid combinations that could be generated when one

considers the pharmacodynamic and pharmacokinetic differences between the clinically

available volatile anesthetics (desflurane, sevoflurane, and isoflurane) and commonly

utilized opioids (morphine, fentanyl, alfentanil, sufentanil, and remifentanil), not to

mention the alternative of a propofol based anesthetic. Response surface models of these

combinations would be necessary to develop a comprehensive library of models for use

in everyday anesthesia practice that would not constrain the clinician to a single pair of

anesthetics (i.e., sevoflurane and remifentanil only).

2.5.6 Conclusion

In summary, the sevoflurane-remifentanil response surfaces estimated in this

study demonstrate clear and profound synergism for both analgesia and sedation.

Furthermore, combined with pharmacokinetic models, the response surfaces provide the

52

scientific foundation to identify the “optimal” combinations of sevoflurane and

remifentanil required to produce the fastest return to alertness (OAA/S ≥ 4) after

anesthetics varying in duration from 30-900 minutes. The reduction in sevoflurane

requirements predicted by these simulations plateaus at a value (0.75 vol %, 0.34 MAC)

comparable to that of MACAWAKE (0.35 MAC) of sevoflurane and in the range of the

maximum reduction in MAC (61%) and MACBAR (85%) that results from co-

administration of high doses of remifentanil with sevoflurane, acting as indirect

validation of the response surfaces. These response surfaces may potentially be used to

clinical advantage, such as their incorporation into real-time, pharmacokinetic-

pharmacodynamic display systems.45,56

2.6 Appendix A: The Logit Model For Pharmacodynamics

The pharmacodynamic response to a single drug can be described by the logistic

regression model. In the logistic regression model, the natural logarithm of the odds ratio

of drug effect (the Logit) is described as a function of drug concentration (C):

CP

PoddsratioLogit ⋅+=

−== 10

1ln)ln( ββ (1)

where P is the probability of the desired effect, and ß0 and ß1 are estimated parameters.

The Logit model can be generalized to multiple drugs, using the linear function of

the concentrations of the two drugs sevoflurane (Cs) and remifentanil (Cr)22

:

rsrs CCCCP

PoddsratioLogit ⋅⋅+⋅+⋅+=

−== 3210

1ln)ln( ββββ (2)

where P is the probability of the desired effect, and ß0, ß1, ß2, ß3 are estimated

coefficients of the linear function.

53

Rearranging equation (2) to solve for the probability of effect, P, results in

equation (3):

)( 32101

1CrCsCrCs

eP

⋅⋅−⋅−⋅−+=

ββββ (3)

Equation (3) can be rearranged to compute the 50% (equation (4a)) and 95%

(equation (4b)) probability isoboles for sevoflurane:

Cr

CrEC S

⋅+

⋅−=

31

20,50

ββ

ββ (4a)

Cr

Cr

EC S⋅+

⋅−+−=

31

20

,95

)195.0

1ln(

ββ

ββ (4b)

The Logit model reduces to a simpler form that allows calculation of the

concentration-effect relationship for sevoflurane or remifentanil when administered

alone. By substituting into equation (3) a value of 0 for remifentanil or sevoflurane,

respectively, the concentration of each drug needed to produce 50% probability of effect

(EC50) when each of the drugs is used individually, can be calculated by equations (5a)

and (5b).

1

0,50

β

β=SEC

(5a)

54

2

0,50

β

β=REC

(5b)

2.7 Appendix B: Pharmacokinetic-Pharmacodynamic Simulations

2.7.1 Pharmacodynamic End Points

Examining the response surface models generated for adequate sedation (95%

probability of OAA/S ≤ 1) and adequate analgesia (95% probability of having no

movement or hemodynamic response to a 50 mA electrical stimulus), it is clear that there

are many target concentration pairs of sevoflurane and remifentanil that would provide

adequate surgical anesthesia. The concentration pairs on the EC95% isobole for no-

response to a 50 mA electrical stimulation (Figure 2.3b) is consistently greater than the

concentration pairs on the EC95% isobole for adequate sedation (Figure 2.2b). Therefore,

providing combinations of sevoflurane and remifentanil that are on the electrical

stimulation EC95% isobole will provide adequate surgical anesthesia. Clinical recovery

from surgical anesthesia is characterized by the ability to follow simple commands (e.g.,

eye opening, squeezing hands, etc.) upon discontinuing drug administration. The state of

clinical recovery from anesthesia corresponds to an OAA/S ≥ 4 (Table 1). Therefore, in

order to model the response surface for clinical recovery from administration of

combinations of sevoflurane and remifentanil, a Logit model can be constructed with

OAA/S ≥ 4 defined as adequate recovery and an OAA/S < 4 defined as “asleep.” This

model has a correlation coefficient of 0.83 and the model coefficients, ß0, ß1, ß2, ß3 are

estimated as 2.97, 4.98, 0.33, and 3.15, respectively. Because the Logit model has the

limitation that a small effect remains when there is no drug administered, the EC80%

isobole for OAA/S ≥ 4 was used to determine the sevoflurane-remifentanil concentration

55

pairs that resulted in “clinical recovery” after discontinuing administration of sevoflurane

and remifentanil.

2.7.2 Pharmacokinetic Models

As detailed above, the time until “clinical recovery” after the discontinuation of

the administration of sevoflurane and remifentanil can be defined as the time that it takes

for the sevoflurane and remifentanil concentrations to reach a combination on the EC80%

isobole for OAA/S ≥ 4. In order to simulate the elimination of sevoflurane and

remifentanil, it is necessary to know the concentrations in all of the pharmacokinetic

compartments prior to the cessation of drug administration. Administration and

elimination of sevoflurane was simulated utilizing the 14 compartment physiologic model

described by Lerou, et al., 26

with the volumes and blood flows reported by Lowe and

Ernst,57

and partition coefficients reported by Kennedy, et al.58

Simulation of the

administration of propofol required the use of the target controlled infusion algorithm

described by Van Puocke, et al.,59

employing the remifentanil pharmacokinetic model

described by Minto, et al.,17

to maintain a remifentanil effect site concentration on the

EC95% isobole for no-response to 50 mA electrical stimulus.

2.7.3 Determination of the Shortest Time to Awakening

The EC95% isobole for no-response to a 50 mA electrical stimulus provides a

large number of concentration pairs of sevoflurane and remifentanil. An initial

concentration pair was randomly picked from those concentration pairs located on the

EC95% isobole for tetanic stimulation. The alveolar concentration of sevoflurane and the

effect site concentration of remifentanil were maintained constant for the predetermined

duration (30-900 minutes). For example, to simulate the administration of 1.05 vol %

56

sevoflurane and 4.53 ng•ml-1

of remifentanil, the uptake and distribution of sevoflurane

throughout the body was simulated to maintain an alveolar concentration of 1.05% and

the uptake and distribution of remifentanil was simulated for utilizing the target

controlled infusion algorithm to maintain a constant value of 4.53 ng•ml-1

at the effect

site. At the end of the predetermined length of drug administration, the decay of the effect

site concentration of remifentanil and alveolar concentration of sevoflurane were

observed. The time at which these combinations fell below levels on the EC80% isobole

for OAA/S ≥ 4 were noted. For this example, the recovery time was 5 minutes (see

Figure 2.5b). This procedure was repeated with a binary search algorithm to determine

the combination of sevoflurane and remifentanil that started on the EC95% isobole for

tetanic stimulation and had the fastest recovery time for the predetermined duration of

drug administration. Using the same methods the ratio that had the fastest recovery time

was determined for each procedure length (0.5, 1, 2, 4, 7, 10, 15, 20 and 24 hrs).

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41. Hug CC, Jr.: Does opioid "anesthesia" exist? Anesthesiology 1990; 73: 1-

4

42. Philbin DM, Rosow CE, Schneider RC, Koski G, D'Ambra MN: Fentanyl

and sufentanil anesthesia revisited: how much is enough? Anesthesiology 1990; 73: 5-11

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43. Wong KC: Narcotics are not expected to produce unconsciousness and

amnesia. Anesth Analg 1983; 62: 625-6

44. Katoh T, Ikeda K: The effects of fentanyl on sevoflurane requirements for

loss of consciousness and skin incision. Anesthesiology 1998; 88: 18-24




46. Miller DR, Tierney M: Observational studies and "real world" anesthesia

pharmacoeconomics/Les etudes par observation et la realite pharmacoeconomique de

l'anesthesie. Can J Anesth 2002; 49: 329-334

47. Avram MJ, Krejcie TC: Using front-end kinetics to optimize target-

controlled drug infusions. Anesthesiology 2003; 99: 1078-86

48. Mertens MJ, Engbers FH, Burm AG, Vuyk J: Predictive performance of

computer-controlled infusion of remifentanil during propofol/remifentanil anaesthesia. Br

J Anaesth 2003; 90: 132-41

49. Egan TD, Huizinga B, Gupta SK, Jaarsma RL, Sperry RJ, Yee JB, Muir

KT: Remifentanil pharmacokinetics in obese versus lean patients. Anesthesiology 1998;

89: 562-73

50. Egan TD, Lemmens HJ, Fiset P, Hermann DJ, Muir KT, Stanski DR,

Shafer SL: The pharmacokinetics of the new short-acting opioid remifentanil (GI87084B)

in healthy adult male volunteers. Anesthesiology 1993; 79: 881-92

51. Egan TD, Minto CF, Hermann DJ, Barr J, Muir KT, Shafer SL:

Remifentanil versus alfentanil: comparative pharmacokinetics and pharmacodynamics in

healthy adult male volunteers. Anesthesiology 1996; 84: 821-33

52. Eger EI, 2nd, Laster MJ, Gregory GA, Katoh T, Sonner JM: Women

appear to have the same minimum alveolar concentration as men: a retrospective study.


53. Angst MS, Koppert W, Pahl I, Clark DJ, Schmelz M: Short-term infusion

of the mu-opioid agonist remifentanil in humans causes hyperalgesia during withdrawal.

Pain 2003; 106: 49-57

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Fletcher D, Chauvin M: Acute opioid tolerance: intraoperative remifentanil increases

postoperative pain and morphine requirement. Anesthesiology 2000; 93: 409-17

61

55. Lotsch J, Angst MS: The mu-opioid agonist remifentanil attenuates

hyperalgesia evoked by blunt and punctuated stimuli with different potency: a

pharmacological evaluation of the freeze lesion in humans. Pain 2003; 102: 151-61

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and Interaction Pharmacodynamic Relationship during Simultaneous Administration of

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targeting the effect site while limiting peak plasma concentration. IEEE Trans Biomed

Eng 2004; 51: 1869-75

CHAPTER 3

CONTEXT SENSITIVE TARGETS FOR OPIOIDS AND

INTRAVENOUS ANESTHETICS §

3.1 Abstract

3.1.1 Background

Anesthesia is most often achieved by a combination of a hypnotic and an opioid.

Utilizing pharmacokinetic models and pharmacodynamic response surface models, it

should be possible to determine the combination of propofol and remifentanil that would

result in the shortest time to awakening for anesthetics with different durations.

3.1.2 Methods

Response surface models that described the interaction between propofol and

remifentanil in providing adequate sedation and surgical analgesia generated from

volunteer data. Pharmacokinetic models were used to simulate dosing regimens that

maintained constant effect site target concentration pairs on the 95% isobole for adequate

anesthesia and the opioid/sedative mixture that yielded the fastest recovery (Observer’s

Alertness and Assessment Scale, OAA/S 4) from anesthetics with durations varying from

§ Submitted for review in Anesthesiology, June 2006. Original article titled: “Does the

ideal combination of remifentanil and propfol change with the duration of surgery?” The

text of Chapter 3 of this dissertation is primarily authored by Dhanesh K. Gupta M.D.,

Assistant Professor, Department of Anesthesiology, University of Utah. Sandeep C

Manyam conducted research, performed data analysis, and generated figures and tables

that form the basis of this manuscript.

63

0.5 to 24 hours were calculated.

3.1.3 Results

Logit response surface models were able to characterize all the pharmacodynamic

endpoints well. The pharmacokinetic-pharmacodynamic simulations revealed that as the

length of the procedure increased, faster recovery was produced by mixtures containing

higher amounts of remifentanil. This trend plateaued for anesthetics lasting two or more

hours at effect site concentrations of 1 µg•mL-1

propofol and 15 ng•mL-1

remifentanil.

3.1.4 Conclusions

For longer duration anesthetics, the pharmacokinetic advantage of remifentanil

becomes more apparent. Therefore, it appears that the optimal target concentration pairs

of propofol-remifentanil anesthetics only changes during the first two hours of anesthesia,

before the optimal concentration pairs plateau at their final values.



Diego, CA, (TDE) and by the National Institute of Biomedical Imaging and


Portions of this work have been presented at the 79th

Annual Clinical and

Scientific Congress of the International Anesthesia Research Society in Honolulu, HI,

March 15, 2005, (Poster S-405) and the 80th

Annual Clinical and Scientific Congress of

the International Society of Anesthesia Research in San Francisco, CA, March 27, 2006.

64

3.2 Introduction

The time until a patient regains responsiveness from a single drug anesthetic is

determined by the pharmacokinetics of the individual drug, the concentration-effect

relationship, and the duration of administration of the drug.1,2

For two-drug anesthetics,

the time to awakening is not only dependent on the individual drug pharmacokinetics and

the duration of administration of the anesthetics, but it is also dependent on the target

concentrations achieved for each of the drugs administered.3 Attempting to run a “lean”

anesthetic can result in an increased chance of intraoperative awareness,4 while

attempting to run a “deep” anesthetic can result in intraoperative hemodynamic

instability5 and possibly even an increase in one year mortality.

6 To provide a clinically

useful context for applying the response surface models to everyday anesthesia practice,

pharmacodynamic response surface models can be combined with pharmacokinetic

models7,8

using computer simulation as described by Vuyk, et al.,3 to identify target

concentration pairs of the sedative/hypnotic and the opioid that provide a high probability

of clinical sedation and nonresponsiveness to noxious stimulation and the most rapid

emergence after cessation of anesthetic administration.9

Several authors have developed response surface pharmacologic interaction

models of the prototypic intravenous sedative/hypnotic, propofol and the prototypic

synthetic opioid, remifentanil.10-12

However, only the work by Mertens, et al.,10

applied

these models to predict possible optimum target concentration pairs of propofol and

remifentanil that would result in the fastest return of consciousness. Surprisingly,

simulations based on these response surface models, and those based of isobologram data

extrapolated form propofol-alfentanil interaction data3 have both determined that with

65

increasing duration of anesthesia there was no change in the “optimal” propofol-

remifentanil target concentrations. This is in direct contrast to what would be predicted

based on the complex pharmacokinetics of propofol and remifentanil2 and the synergistic

interactions that occur for a variety of pharmacologic end points.3,11-14

In addition,

increasing the duration of anesthesia changes the “optimal” sevoflurane-remifentanil

target concentration pairs to contain higher concentrations of remifentanil. 9

The aim of this study was to utilize previously collected pharmacodynamic data

and apply the generated propofol-remifentanil response surface models to determine if

the “optimal” propofol-remifentanil concentrations changes as the anesthetic duration

increased. We hypothesized that by combining Logit response surface models developed

from volunteer data with published pharmacokinetic models, we could predict the target

concentrations of propofol and remifentanil that resulted in the fastest time to awakening

from anesthesia. We also hypothesized that the pharmacokinetic advantages of

remifentanil over propofol would result in higher remifentanil concentrations being

targeted as the duration of the anesthetic increased-the “optimal” propofol-remifentanil

concentration would increase as the duration of anesthesia increased.


Data from 32 of the 40 subjects included in this manuscript were acquired from

the datasets reported in two manuscripts from our laboratory that examined the

synergistic interaction between sedative/hypnotics and remifentanil in producing clinical

sedation and analgesia to experimental painful stimuli that are surrogates for

intraoperative painful stimuli.9,11

The data from all 24 subjects reported by Kern, et al.,11

were included in the current analyses, while only the data acquired from the eight

66

subjects who received remifentanil alone during the initial phase of the study reported by

Manyam, et al.,9 were included in the current analyses. The data from an additional eight

subjects who received propofol as the initial anesthetic drug followed by two fixed doses

of remifentanil were included in these analyses; these subjects were collected as part of

the volunteer study conducted by Manyam, et al.,9 but have not been reported elsewhere.

A written informed consent document that was approved by the Human

Institutional Review Board at the University of Utah Health Sciences Center (Salt Lake

City, Utah) was obtained from each of 40 volunteers in this open-label, randomized,

parallel group crisscross designed study to asses drug interactions (Figure 3.1).15

Each

volunteer was randomized to receive a target controlled infusion of propofol (predicted

effect site concentrations of 0.5 -12 µg•mL-1

) or remifentanil (predicted effect site

concentrations of 0.5 -80 ng•mL-1

) as the primary agent with the other drug acting as the

secondary agent (Figure 3.1). The reader is referred to the previous manuscripts by Kern,

et al.,11

and Manyam, et al.,9 for complete details regarding the methods of volunteer

preparation, drug administration, data collection, and data analysis). Only those portions

of the data analysis that have substantial differences from the previous manuscripts are

provided in detail below.

All of the effect measurements utilized by Manyam, et al.,9 had maximum

intensities that were decreased from those utilized by Kern, et al.,11

because intensity

levels of 60 mA and 60 PSI were found to be well above the supra-maximal stimulus

intensity. To adjust for the different supra-maximal stimulus levels applied between the

two studies as well as the intersubject variation in baseline tolerance of noxious

stimulation, the level of stimulus tolerated was normalized against each volunteer’s

67







concentration pair.

68

baseline value, such that 0 represented baseline and 1 represented the maximal stimulus

tolerated by the volunteer. This produced a quantal pharmacodynamic response of

whether the volunteer could tolerate the maximal stimulus level (e.g., no withdrawal, no

increase in heart rate or blood pressure). For each pharmacodynamic response, the data

were combined and used to fit the three-dimensional response surface based on the Logit

model.9 Simulated two-dimensional concentration-effect relationship curves for propofol

at a variety of remifentanil concentrations were utilized to determine the type of

pharmacologic interaction produced by the addition of remifentanil to a propofol

anesthetic.13

The pharmacodynamic response surface models from this study were combined

with previously published pharmacokinetic models7,16

using computer simulation as

described by Vuyk, et al.,3 to identify target concentration pairs of propofol and

remifentanil that provided a high probability of nonresponsiveness to noxious stimulation

and the most rapid emergence after cessation of anesthetic administration.9 Because of

the overlap between the propofol-remifentanil clinical sedation isobole (95% probability

of achieving an OAA/S ≤ 1) and surgical analgesia isobole (95% probability of no

movement response and no hemodynamic response to a 50 mA tetanic stimulation), the

composite “isobole” predicting adequate surgical anesthesia-adequate clinical sedation

and adequate surgical analgesia-was chosen to be the higher of the two isoboles at any

given concentration pair (Figure 3.2). The propofol model described by Tackely, et al.,17

and the remifentanil model reported by Minto, et al.,7 were utilized with the target

controlled infusion algorithm employed by STANPUMP18

to simulate a range of

propofol and remifentanil effect site concentrations that produced a 95% probability of

69

Figure 3.2: A representation of the isoboles predicting a 95% probability of clinical

sedation (OAA/S ≤ 1, dotted line), a 95% probability of surgical analgesia (no movement

and no hemodynamic response to a 50 mA tetanic stimulus, solid line), and an 80%

probability of clinical awakening form anesthesia (OAA/S ≥ 4, dashed line). Because the

isoboles for adequate sedation and adequate analgesia intersect and cross, the targets for

adequate clinical anesthesia (sedation and analgesia) is determined by the isobole that is

at the higher target concentration pairs (boundary of the hatched area).

70

surgical anesthesia, as determined by the composite clinical anesthesia isobole. These

effect site concentrations were maintained at these levels for one hour, after which time

the drugs were discontinued and the “washout” of the anesthetics was simulated. The

shortest time during the washout until the drug interaction model predicted a 95%

probability that OAA/S was ≥ 4 was found through iterative simulation utilizing a binary

search algorithm.19

The combination of propofol and remifentanil that resulted in the

quickest recovery (OAA/S ≥ 4) was determined for anesthetics of 30-900 minutes in

duration.

3.4 Results

All forty volunteers completed one of the two study protocols. The demographics

of the four groups of patients are shown in Table 3.1. There was no difference between

the groups except that the remifentanil patients from Manyam’s study were

predominately male, whereas the remainder of the groups contained equal numbers of

male and female volunteers.


The parameters for all the response surface models were identifiable. The Logit

model parameters estimated through the nonlinear regression are shown in Table 3.2. The

models described the pharmacodynamic data reasonably well (R2 > 0.5), with the models

for clinical sedation score and pressure algometry performing best. Figure 3.3a shows the

response surface for sedation (OAA/S ≤ 1) of the unstimulated volunteers and Figure

3.3b shows the data overlaid on the simulated isoboles that predict a 50% and 95%

probability of having an OAA/S ≤ 1. Figure 3.4a and 3.4b demonstrate the tetanic

stimulation response surface and the simulated isoboles that predict a 50% and 95%

71


Propofol

Kern

(n = 12)

Propofol

Manyam

(n = 8)

Remifentanil

Kern

(n = 12)

Remifentanil

Manyam

(n = 8)

Age [years] 29.0 ± 3.8 28.6 ± 7.9 31.6 ± 6.0 23.0 ± 3.0

Weight [kg] 69.0 ± 11.8 72.9 ± 13.2 73.0 ± 10.9 75.0 ± 9.0

Height [cm] 169.5 ± 8.9 172.7 ± 10.5 175.8 ± 12.8 178.0 ± 8.0

Sex [M:F] 5 : 7 8 : 3 7 : 5 7: 1

* All values are given as mean ± standard deviation, except for the ratio of males to

females.

72

Table 3.2: Mean Model Parameters for the Logit Response Surface*

ß0 ß1 ß2 ß3 Log

Likelihood

Correlation

Coefficient

Pressure algometry 3.02 0.63 0.28 0.57 -273.68 0.71

Tetanic Stimulation 3.73 0.56 0.09 0.48 -214.15 0.63

Laryngoscopy 3.37 0.51 0.04 0.20 -143.04 0.59

OAA/S 5.23 2.36 0.16 0.23 - 86.13 0.83

* Model parameters are listed for all values. Standard errors for all parameters were <

0.01, as determined by the bootstrap method. OAA/S = Observer assessment of Alertness

and sedation score.

73

Figure 3.3: The propofol-remifentanil interaction for sedation. The Logit response

surface model prediction for sedation for unstimulated volunteers is presented in the top

panel (Figure 3.3a). An Observer’s Assessment of Alertness/Sedation (OAA/S) score ≤ 1

represents a sedated volunteer. A 0 indicates an OAA/S ≥ 2 and a 1 indicates an OAA/S ≤

1. The symbols show measured responses and the surface predicted by the model is

represented by the grid-lined surface. The raw data used to create this model are shaded

based on the residual error. A topographic view of the 50% and 95% effect isoboles for

probability of being sedated is presented in the bottom panel (Figure 3.3b). The OAA/S

score at each target concentration pair is overlaid.

74

Figure 3.3

a)

b)

75

Figure 3.4: The remifentanil-sevoflurane interaction for electrical tetanic stimulation.

The top panel (Figure 3.4a) shows the Logit response surface model prediction for tetanic

stimulation of 50 mA. A 0 indicates a response (movement or a 10% increase in blood

pressure or heart rate) to a 50 mA stimulus current and a 1 indicates no response to 50

mA stimulus current. The symbols show measured volunteer responses to 50 mA of

stimulus current and the surface predicted by the model is represented by the grid-lined

surface. The raw data used to create this model is shaded based on the residual error. The

bottom panel (Figure 3.4b) shows a topographic view of the 50% and 95% effect isoboles

for probability of tolerating a 50 mA stimulus current. The percentage of tolerated

stimulus current at each target concentration pair is overlaid.

76

Figure 3.4

a)

b)

77

probability of not having a movement response or hemodynamic response to a 50 mA

tetanic stimulation.

All of the other pain stimuli surfaces (not shown) were of similar shape. The

residual errors for both clinical sedation and “surgical anesthesia” were less than 10%

throughout most of the clinically relevant range of concentrations (propofol 0-10 µg•mL-1

and remifentanil 0-15 ng•mL-1

). Simulated concentration-response curves for propofol at

a variety of remifentanil concentrations that are based on the response surface models for

clinical anesthesia and surrogate surgical anesthesia are shown in Figures 3.5a and 3.5b,

respectively.


The addition of a moderate dose of remifentanil (Ce 9.03 ng•mL-1

) to a thirty

minute anesthetic decreased the propofol effect site concentration 8 fold (Ce 11.95 �

1.51 µg•mL-1

) compared to the propofol effect site concentration required to produce

surgical analgesia without any remifentanil (Tables 3.3 and 3.4). The concentration of

propofol and remifentanil that resulted in the fastest emergence from surgical anesthesia

plateaued at 1 µg•mL-1

and 15 ng•mL-1

, respectively, for anesthetics lasting as short as

two hours (Figure 3.6 and Table 3.4). By administering combinations of anesthetics

containing higher amounts of remifentanil than propofol, it is possible to take advantage

of the more favorable pharmacokinetic properties of remifentanil and exploit the

synergistic pharmacodynamic effects.

78

Figure 3.5: The effect of adding remifentanil on the concentration-effect relationships of

propofol for sedation (Figure 3.5a) and analgesia (Figure 3.5b). Each curve represents the

concentration-effect relationship for propofol with a fixed effect site concentration of

remifentanil simulated from the corresponding response surface model. The shift in the

curves toward the left indicates that much less propofol is needed when remifentanil is

added, demonstrating the significant pharmacodynamic synergy between0 the sedative

and the opioid. Note that the magnitude of the leftward shift decreases as the remifentanil

concentration increases (i.e., there is a ceiling effect). The addition of small to moderate

amounts of remifentanil to a propofol anesthetic result in a large decrease in the amount

of propofol required to produce clinically adequate sedation and surgical anesthesia

(Figures 3.5a and 3.5b, and Table 3.3).

79

Figure 3.5

a)

b)

80

Table 3.3: Reduction in Propofol Requirements by Remifentanil *

Remifentanil

Ce

[ng•mL-1

]

Remifentanil

Infusion Rate

[µ•kg-1

•min-1

]

Propofol

C95% OAA/S ≤≤≤≤ 1

[µ•mL-1

]

Propofol

C95% Tetanic

Stimulation

[µ•mL-1

]

0 0 3.46 11.95

1.25 0.05 3.00 5.64

5 0.18 2.09 2.08

7.5 0.27 1.70 1.43

* The reduction in the effect site concentration (Ce) of propofol that produces a 95%

probability (C95%) of an OAA/S score ≤ 1 or no movement or hemodynamic response to a

50 mA tetanic stimulation by the addition of remifentanil in doses ranging from 0-0.27

mcg•kg-1•min

-1 are reported. All infusion rates were calculated for a hypothetical 30 year

old male who weighed 80 kg and was 183 cm tall utilizing Stanpump.

81

Table 3.4: Simulation Results for Anesthetics 30-900 Minutes in Length *

Length of

Anesthetic

[hr]

Shortest

Recovery Time

[min]

Remifentanil

Ce

[ng••••mL-1

]

Remifentanil

Infusion Rate

[µ••••kg-1••••min

-1]

Propofol

Ce

[µ••••mL-1

]

0.5 9.92 9.03 0.33 1.51

1 11.25 13.0 0.48 1.14

2 13.73 15.0 0.55 1.0

4 14.2 15.0 0.55 1.0

7 14.48 15.0 0.55 1.0

10 14.62 15.0 0.55 1.0

15 14.72 15.0 0.55 1.0

20 14.75 15.0 0.55 1.0

24 14.75 15.0 0.55 1.0

* The effect site concentration (Ce) and infusion rate for remifentanil and effect site

concentration (Ce) for propofol that produced the shortest recovery times are reported for

anesthetics lasting 0.5-24 hours. All simulations were performed for a hypothetical 30

year old male who weighed 80 kg and was 183 cm tall.

82

Figure 3.6: The results of computer simulations designed to identify optimal target

concentration pairs of remifentanil- and propofol that minimize the time to

responsiveness. The top panel (Figure 3.6a) shows the predicted decline in effect site

concentrations for remifentanil and propofol after stopping drug administration regimens

targeted to reach the EC95 for adequate clinical anesthesia isobole for one hour. The EC95

isobole is on the “floor” of the cube; the vertical axis represents time elapsed since

stopping the administration of the drugs. The isobole representing a 95% probability of

the return of responsiveness (Observer’s Assessment of Alertness/Sedation score ≥ 4) is

shown by a dotted line that is superimposed on the concentration decay curves. The

highlighted curve is the sevoflurane and remifentanil target concentration pair that

resulted in the fastest return of responsiveness. The bottom panel (Figure 2.5b) shows the

time in minutes to the return of responsiveness after a 1hr procedure in which propofol

and remifentanil were administered to target concentration pairs on the EC95 isobole for

adequate clinical anesthesia isobole. The highlighted trace on the panel on the left is

shown topographically. The minimum time to regain responsiveness represents the target

concentration pairs for a 1 hour procedure.

83

Figure 3.6

a)

b)

84

3.5 Discussion

Our simulations revealed that for short duration anesthetics, the pharmacokinetic

advantage of remifentanil becomes more apparent. Between 0.5 hours and 2 hours, the

propofol target effect site concentration decreased by 33% while the remifentanil target

effect site concentration increased by 66% (Table 3.5). However, with further increases

in the anesthetic duration, both the remifentanil and the propofol effect site

concentrations rapidly reached their plateau values-for all anesthetics lasting two or more

hours, the “optimal” target effect site concentration of propofol reached a nadir at 1.0

µg•mL-1

while the target effect site concentration of remifentanil plateaued at 15.0

ng•mL-1

. Therefore, our results are similar to those predicted by the Mertens, et al.,10

and

Vuyk, et al.,3 and different from our previous observations with sevoflurane-remifentanil

anesthesia.9


We chose to utilize a Logit model as the basis of our response surface analyses

because the Logit model is able to characterize quantal pharmacologic responses.

Transformation of the data from different data collection periods that have different

baseline and maximal pharmacologic response into quantal responses allows the analysis

of a larger data set derived from a variety of sources. With the Logit model we were able

to confirm the synergistic interaction of propofol and remifentanil in producing clinical

sedation and analgesia.10-12

As before, the Logit model fulfills all but one of the criteria

proposed by Minto, et al.,20

and the single unfulfilled criterion is that the Logit based

response surface model dictates that there is a slight effect (< 0.1% probability of a

response) when no drug is administered.9

85

Our predictions for the EC50, PROP for sedation and tetanic stimulation are very

close to those predicted by the previous analysis of portion of this dataset utilizing the

Greco form of the response surface model (2.2 vs 1.8 µg•mL-1

and 6.7 vs 4.6 ng•mL-1

,

sedation and tetanic stimulation, respectively).11

The estimates for the EC50, PROP for

sedation are also in agreement with those reported for surgical patients by other

investigators.10,21

The relative agreement between the EC50, PROP for tetanic stimulation

and the reported EC50, PROP for laryngoscopy in this manuscript ( 6.7 vs. 6.6 µg•mL-1

,

tetanic stimulation vs. laryngoscopy) and by others10,12

suggests that electric stimulation

may provide an stimulus of an intensity comparable to surgical incision or laryngoscopy.

Therefore, the volunteer paradigm utilized in this an other studies,9,11

is able to predict

results that are consistent with similar pharmacologic end points in surgical patients. The

volunteer paradigm offers several advantages over the surgical patient for studying

pharmacodynamic interactions between two anesthetic drugs. The main two advantages

are the ability to study subtherapeutic combinations of drugs without concern of

providing inadequate clinical effect and the ability to perform repeated measurement of

responses thereby allowing characterization of the entire spectrum of concentration pairs.

However, one of the remaining challenges in pharmacodynamics research is the

validation of the surrogates of surgical stimulation (e.g., electrical stimulation) by other

means than comparing the predicted concentration effect relationship to those reported

for the same drug in surgical patients.


There appears to be a very limited range of anesthetic lengths over which the

“optimal” propofol-remifentanil effect site concentrations change before reaching plateau

86

values. The minimum sedative concentration of propofol plateaus at 1.0 µg•mL-1

which

correlates with approximately a 71% reduction in the EC95, PROP for sedation.

Coincidently, various opioid and sedative/hypnotic combinations have revealed that even

“high” concentrations of opioid are unable to produce more than a 60-70% reduction in

pharmacologic requirements for a sedative/hypnotic.10,13,22-25

Examined another way, it

appears that the fundamental processes of anesthesia (amnesia) require a modest amount

of sedative/hypnotic even in the presence of extremely high opioid concentrations-the

ceiling effect.10,13,22-25

Accordingly, the EC95, REMI for sedation and EC95, REMI for surgical

analgesia were two to three times higher than the maximum remifentanil concentrations

simulated (32.7 and 41.4 ng•mL-1

, sedation and tetanic stimulation, respectively), which

is consistent with the findings of other investigations.3,9,10


The concentration-effect curves generated by these response surface models

demonstrate an approximately 2-fold reduction in the EC95, PROP for sedation and an

approximately 8-fold decrease in the EC95, PROP for surgical analgesia with the addition

of 7.5 ng•mL-1

of remifentanil (0.27 µg•kg-1

•min-1

infusion) to a propofol anesthetic

(Figure 3.5a and 3.5b and Table 3.3). Comparing this to the 6-fold reduction in the EC95,

SEVO for sedation and an approximately 10-fold decrease in the EC95, SEVO for surgical

analgesia under similar conditions,9 one can see that propofol is a more potent

sedative/hypnotic than sevoflurane and about equally as good of an immobilizer or

analgesic.

The propofol-remifentanil synergy in producing clinical sedation and surgical

analgesia supports the utility of administrating “balanced” anesthetics with a combination

87

of sedative/hypnotic and opioid. The pharmacokinetic-pharmacodynamic simulations

demonstrate the benefit of administrating a very low target effect site concentration of

propofol (1.0 µg•mL-1

) in the presence of remifentanil, an opioid with a very rapid

elimination clearance. This is especially true for anesthetics 2 hours or longer. Because

the pharmacoeconomic advantages of the drug are not limited to just minimizing the time

until awakening or the drug acquisition costs, it is unclear whether these high dose

remifentanil-low dose propofol anesthetics will be of a pharmacoeconomic advantage.26

3.5.4 Limitations

Although the volunteer study design affords the advantages of ethically allowing

the investigation of multiple concentration pairs spanning from subtherapeutic to supra-

therapeutic combinations, it is limited in that the sedation end point is determined in

unstimulated volunteers because of the lack of an endotracheal tube. Since every surgical

patient has at least the mild to moderate stimulation provided by the constant presence of

an endotracheal tube, there is a possibility that our predictions for the drug concentration

pairs that provide adequate sedation may underestimate the sedative requirements of an

intubated surgical patient. However, the robustness of the OAA/S criteria utilized to

define sedation (OAA/S ≤ 1-does not respond to shaking or shouting) may compensate

by providing intermittent stimulation. The similarities between the EC50, PROP for sedation

determined from this model and that determined in unintubated patients during

placement of an intracerebral stimulating electrode21

or intubated surgical patients

undergoing gynecological operations10

is reassuring that this limitation is not too large.

The application of surgical stimulus surrogates also has advantages and

disadvantages. Although it is possible to apply these surgical surrogates multiple times in

88

order to determine the pharmacologic response spanning the entire concentration range,

in order to assure that the volunteers were not injured, we had to limit the maximum

stimulus applied. This limitation may have resulted in censored data that could result in

pharmacodynamic response curves that predict a falsely low potency. Therefore, care

must be used if the data are extrapolated above the concentration range examined. The

other disadvantage of the surrogates to surgical stimulus is that it is unclear what the true

intraoperative correlates are for the tested stimuli just as it is unclear what the appropriate

surrogate stimulus is for many intraoperative stimuli (e.g., pneumoperitoneum,

subcutaneous tunneling, etc.). Based on our growing experience with these surgical

surrogate stimuli and observations of the pharmacodynamic and physiologic

responses,9,11

it is probably safe to state that the most stimulating intraoperative events-

surgical incision, placement of a Mayfield head fixation device, sternotomy, and

laryngoscopy-are mimicked by the most intense surrogate stimuli-tetanic stimulation and

laryngoscopy.

The use of intravenous anesthetics brought about two possible limitations. First,

the use of pharmacokinetic models to predict the propofol and remifentanil effect site

concentrations in lieu of measuring the actual blood drug concentration may compound

some of the variability in the opioid only, single drug data.27

However, as in our previous

study,9 there is convincing evidence to demonstrate that this may not be a major source of

pharmacokinetic variability. Second, continuous infusions of remifentanil has been

shown to induce hyperalgesia in patients28

and volunteers.29

As detailed in our prior

manuscript,9 we did not design the studies to detect the existence of or the presence of

remifentanil induced hyperalgesia. However, we did not find any differences between the

89

baseline levels of tolerated stimuli and the levels of stimuli tolerated at the lowest doses

of propofol. In addition, one could conjecture that any opioid hyperalgesia that developed

would not effect the clinical sedation scores (OAA/S) that were determined during quiet

periods prior to the determination of the analgesic response of each of the targeted

concentration pairs.

3.5.5 Future Work

Our response surface models for propofol and remifentanil interactions were

developed in volunteers exposed to a variety of surrogate pain stimuli. These models will

need to be validated in a variety of surgical patients receiving these two drugs as the only

anesthetic agents. Further work will need to be done to determine if the surrogate pain

stimuli accurately predict the responses to different surgical stimuli (e.g., skin incision,

abdominal insufflation, placement of Mayfield head fixation, etc.). In addition, there are

conceivably 20 different sedative-opioid combinations that could be generated when one

considers the pharmacodynamic and pharmacokinetic differences between the clinically

available sedative/hypnotics (propofol, desflurane, sevoflurane, and isoflurane) and

commonly utilized opioids (morphine, fentanyl, alfentanil, sufentanil, and remifentanil).

Simply utilizing the potency ratio of one drug compared to another member of it’s drug

class and performing computer simulations of the results based on previous developed

models may not yield correct results if the concentration-effect relationships are not

parallel and the appropriate dose ranges are not selected. But in order to develop a

comprehensive library of models for use in everyday anesthesia practice that would not

constrain the clinician to a single pair of anesthetics (i.e., sevoflurane and remifentanil

only) response surface models of these combinations would be necessary.

90

3.5.6 Conclusion

Several authors have used a variety of response surface models to characterize the

pharmacodynamic interactions of propofol and remifentanil for a variety of

pharmacologic end points in volunteers and surgical patients. We demonstrated that by

combining Logit response surface models developed from volunteer data with

pharmacokinetic models, we could identify target concentrations of propofol and

remifentanil that resulted in the fastest time to awakening from anesthesia. The

pharmacokinetic advantages of remifentanil over propofol resulted in higher remifentanil

concentrations being targeted as the duration of the anesthetic increased.

3.6 References


inhaled anesthetics. Anesth Analg 2005; 101: 688-96



3. Vuyk J, Mertens MJ, Olofsen E, Burm AG, Bovill JG: Propofol anesthesia

and rational opioid selection: determination of optimal EC50-EC95 propofol-opioid

concentrations that assure adequate anesthesia and a rapid return of consciousness.


4. Practice Advisory for Intraoperative Awareness and Brain Function

Monitoring: A Report by the American Society of Anesthesiologists Task Force on

Intraoperative Awareness. Anesthesiology 2006; 104: 847-64




6. Monk TG, Saini V, Weldon BC, Sigl JC: Anesthetic management and

one-year mortality after noncardiac surgery. Anesth Analg 2005; 100: 4-10





91

8. Schnider TW, Minto CF, Shafer SL, Gambus PL, Andresen C, Goodale

DB, Youngs EJ: The influence of age on propofol pharmacodynamics. Anesthesiology

1999; 90: 1502-16

9. Manyam SC, Gupta DK, Johnson KB, White JL, Pace NL, Westenskow

DR, Egan TD: Opiod-Volatile Anesthetic Synergy: A Response Surface Model with

Remifentanil and Sevoflurane as Prototypes. Anesthesiology 2006: in press







100: 1373-81





13. Vuyk J, Lim T, Engbers FH, Burm AG, Vletter AA, Bovill JG: The

pharmacodynamic interaction of propofol and alfentanil during lower abdominal surgery

in women. Anesthesiology 1995; 83: 8-22

14. Schraag S, Mohl U, Bothner U, Georgieff M: Interaction modeling of

propofol and sufentanil on loss of consciousness. J Clin Anesth 1999; 11: 391-6




2002; 96: 400-8

16. Lerou JG, Booij LH: Model-based administration of inhalation

anaesthesia. 1. Developing a system model. Br J Anaesth 2001; 86: 12-28

17. Tackley RM, Lewis GT, Prys-Roberts C, Boaden RW, Dixon J, Harvey

JT: Computer controlled infusion of propofol. Br J Anaesth 1989; 62: 46-53

18. Shafer SL, Gregg KM: Algorithms to rapidly achieve and maintain stable

drug concentrations at the site of drug effect with a computer-controlled infusion pump. J

Pharmacokinet Biopharm 1992; 20: 147-69

19. Knuth D: Sorting and Searching, The Art of Computer Programming, 3

Edition. Reading, Massachusetts, Addison-Wesley, 1997, pp 409-426

92



16

21. Fabregas N, Rapado J, Gambus PL, Valero R, Carrero E, Salvador L,

Nalda-Felipe MA, Troconiz IF: Modeling of the sedative and airway obstruction effects

of propofol in patients with Parkinson disease undergoing stereotactic surgery.




consciousness and skin incision. Anesthesiology 1994; 81: 820-8

23. Sebel PS, Glass PS, Fletcher JE, Murphy MR, Gallagher C, Quill T:

Reduction of the MAC of desflurane with fentanyl. Anesthesiology 1992; 76: 52-9

24. Hall RI, Szlam F, Hug CC, Jr.: The enflurane-sparing effect of alfentanil

in dogs. Anesth Analg 1987; 66: 1287-91

25. Brunner MD, Braithwaite P, Jhaveri R, McEwan AI, Goodman DK, Smith

LR, Glass PS: MAC reduction of isoflurane by sufentanil. Br J Anaesth 1994; 72: 42-6

26. Miller DR, Tierney M: Observational studies and "real world" anesthesia

pharmacoeconomics. Can J Anaesth 2002; 49: 329-34



28. Crawford MW, Hickey C, Zaarour C, Howard A, Naser B: Development

of Acute Opioid Tolerance During Infusion of Remifentanil for Pediatric Scoliosis

Surgery. Anesth Analg 2006; 102: 1662-1667



Pain 2003; 106: 49-57

30. Prys-Roberts C: Anaesthesia: a practical or impractical construct? Br J

Anaesth 1987; 59: 1341-5




Exp Med Biol 2003; 523: 35-43

CHAPTER 4

PROCESSED EEG TARGETS REQUIRED FOR

ADEQUATE ANESTHESIA §

4.1 Abstract

4.1.1 Background

Opioids are commonly used in conjunction with sedative drugs to provide

anesthesia. Previous studies have shown that opioids reduce the clinical requirements of

sedative drug needed to provide adequate anesthesia. Processed EEG parameters, such as

the Bispectral Index (BIS, Aspect Medical Systems, Newton, MA) and Auditory Evoked

Potential Index (AAI, Alaris Medical Systems, ), can be used intra-operatively to assess

the depth of sedation. The aim of this study was to characterize how the addition of

opioids sufficient to change the clinical level of sedation, influenced the BIS and AAI.

4.1.2 Methods

Twenty four adult volunteers received a target controlled infusion of

remifentanil (0-15 ng•mL-1

) and inhaled sevoflurane (0-6 vol %) at various target

concentration pairs. After reaching pseudo-steady-state drug levels, the Observer's

Assessment of Alertness/Sedation (OAAS) score, BIS, and AAI were measured at each

§ Accepted for publication in Anesthesiology, August 2006. Copyright 2006, American

Society of Anesthesiologists. Original article titled: “When is a bispectral index of 60 too

low? Rational processed EEG targets are dependent on the sedative-opioid ratio.”

94

target concentration pair. Response surface pharmacodynamic interaction models were

built using the pooled data for each pharmacodynamic end point.

4.1.3 Results

Response surface models adequately characterized all pharmacodynamic end

points. Despite the fact that sevoflurane-remifentanil interactions were strongly

synergistic for clinical sedation, BIS and AAI were minimally affected by the addition of

remifentanil to sevoflurane anesthetics.

4.1.4 Conclusion

Although clinical sedation increases significantly with the addition of a small to

moderate dose of remifentanil to a sevoflurane anesthetic, the BIS and AAI are

insensitive to this change in clinical state. Therefore, during sevoflurane-remifentanil

anesthesia, targeting a BIS < 60 or an AAI <30 may result in an unnecessarily deep

anesthetic state.



Diego, CA, (TDE) and by the National Institute of Biomedical Imaging and


4.1 Introduction

Explicit recall of intraoperative events (intraoperative awareness) is a major

concern of both patients undergoing anesthetics and health-care providers administering

anesthetics.1 With an incidence of 0.13% in the general population,

2 the topic of

intraoperative awareness has come under scrutiny by both the lay press and the scientific

95

community. This has intensified the search for the “Holy Grail” of intraoperative

anesthesia-a reliable, continuous monitor of the “depth of anesthesia.”3 However,

adequate “depth of anesthesia” is a vague term that spans from a state of sedation and

amnesia that prevents explicit recall4 to a state where there is no movement

5 or no

hemodynamic response to surgical stimuli.6 Furthermore, delivery of a single anesthetic

drug class (e.g., volatile anesthetic or propofol) results in a different anesthetic profile

than when a balanced anesthetic is delivered.7 Therefore, complete monitors of the

“depth of anesthesia” must characterize these clinical endpoints during the administration

of a variety of combinations of anesthetics.8

Processed EEG parameters are gaining popularity as intra-operative monitors of

depth of anesthesia.3 One depth of anesthesia monitor, the Bispectral Index (BIS, Aspect

Medical Systems, Newton, MA), is based on Bispectral analysis of the EEG.9 The

propriety BIS algorithm was a unique step forward in the use of EEG parameters to

determine anesthetic depth because it combined multiple distinct EEG parameters and a

large volume of prospectively collected clinical observations into a single descriptive

variable which was then prospectively tested and validated.3 The BIS is the only

processed EEG that has been found to decrease the incidence of intraoperative awareness

in a randomized controlled trial of patients with a large number of risk factors for

intraoperative awareness.10

In addition, titrating anesthetics to specific BIS target values

has been found to effect clinical outcomes-a BIS of 50-60 results in faster emergence

from anesthesia,11

whereas avoiding “deep anesthesia” (BIS < 40) may improve one year

survival of patients.12

96

During general anesthesia, the brainstem and the midbrain auditory function is

preserved, although meaningful interpretation of the auditory stimulus is inhibited.13,14

These brainstem responses to an auditory stimulus correlate with motor signs of

wakefulness and intraoperative awareness.15

The preservation of brainstem responses

that correlate with inadequate anesthesia (movement or awareness) suggests that the

auditory evoked potential (AEP) might be more robust in detecting inadequate anesthesia

as opposed to the EEG which solely monitors the cortical activity.16,17

The A-Line AEP

Index (AAI, Danmeter, Odense, Denmark) is the first commercially available monitor

that utilizes changes within the AEP to measure the depth of anesthesia.18

Like the BIS,

the AAI correlates well with the clinical level of sedation produced by increasing doses

of sevoflurane13,19

or propofol.9,20

Although adequate surgical anesthesia can be produced utilizing a volatile

anesthetic alone,5,6

hemodynamic depression21

and prolonged time to awakening22

limit

the practicality of utilizing a volatile anesthetic as the sole anesthetic agent. Therefore, an

opioid analgesic is commonly coadministered with smaller doses of a volatile anesthetic

to provided adequate analgesia and maintain a state of nonresponsiveness to surgical

stimulation.23

The addition of opioids is known to synergistically increase the clinical

level of sedation produced by propofol 24,25

and volatile anesthetics.26,27

However, the

effects of the addition of an opioid on the processed EEG parameters is controversial-

some reports show that the processed EEG is insensitive to opioids,9,28,29

whereas others

suggest that opioids do alter processed EEG parameters.30-32

Therefore, the “true” effects

of the addition of opioids to hypnotic drugs on the BIS (and AAI) are unclear.

97

The principle aim of this study was to characterize how the addition of opioids

sufficient to change the clinical level of sedation influenced processed EEG parameters

such as BIS and AAI. Data acquired from volunteers receiving various target

concentration pairs of sevoflurane and remifentanil were utilized to construct response

surfaces models of the observed level of sedation and the measured EEG parameters. We

hypothesized that the processed EEG parameters (BIS and AAI) do not accurately reflect

the level of clinical sedation observed with the addition of remifentanil to a sevoflurane

anesthetic. In addition, we hypothesized that with the co-administration of remifentanil

and sevoflurane, attempting to maintain a target BIS of 50-60 or a target AAI of 20-30

would result in overdosing the anesthetic-sevoflurane-remifentanil target concentration

pairs well above those that provide clinically adequate anesthesia (e.g., no awareness, no

movement, and no hemodynamic response in response to stimulation).


A portion of the data from this data set were published previously in a manuscript

examining the synergistic interaction between remifentanil and sevoflurane in producing

clinical sedation and analgesia to experimental painful stimuli that are surrogates for

intraoperative painful stimuli.33

Because of the minor overlap between the hypotheses of

the previous and the current manuscript and the large amount of data reported in each

manuscript, each analysis is reported in a separate manuscript.





Each

98







concentration pair.

99

volunteer was randomized to receive a target controlled infusion of remifentanil

(predicted effect site concentrations of 0.5-15 ng•mL-1

) or sevoflurane (0.3-6 vol % end

tidal alveolar concentration) as the primary agent with the other drug acting as the

secondary agent (Figure 4.1). The reader is referred to the previous manuscript by

Manyam, et al.,33

for complete details regarding the methods of volunteer preparation,

drug administration, and data collection. Because the methods of data analysis and

statistical analysis have substantial differences from the previous manuscript, they are

provided in detail.

4.3.1 BIS and AAI Measurements

To avoid variability arising from hysteresis between plasma concentration and

effect site, BIS and AAI were measured at each assessment point 5 minutes after the

targeted effect-site concentration (or stable end-tidal concentration) for a primary drug

“step,” was reached. The EEG parameters were averaged in a 40 second interval that

preceded the assessment of the Observer’s Assessment of Alertness/Sedation score

(OAA/S, Table 4.1).35

This interval was also considered a “quiet time” where no other

changes or assessments were made in the volunteers. Data resulting from faulty sensors

or monitor malfunction were not included in the subsequent analyses.

4.3.2 Demographic Data Analysis





100

Table 4.1: Observer’s Assessment of Alertness/Sedation (OAA/S) Score*

Responsiveness Score

Responds readily to name spoken in normal tone 5

Lethargic response to name spoken in normal tone 4

Responds only after name is called loudly and/or repeatedly 3

Responds only after mild prodding or shaking 2

Does not respond to mild prodding or shaking 1

Does not respond to noxious stimulus 0

* For the purposes of this study, an OAA/S ≤ 1 was considered nonresponsive, whereas

an OAA/S ≥ 4 was considered “awake.”

101

4.3.3 Measurement of Association

The performance of each of the processed EEG parameters was assessed by

comparison against the sedation score (OAA/S). Because a direct correlation can not be

calculated between an ordinal variable (OAA/S score) and either of the continuous

variables (processed EEG parameters), we calculated the prediction probability (Pk) as

described by Smith and Dutton36

for the association between the clinical sedation scale

(OAA/S) and BIS and AAI using SPSS Version 14 (SPSS Inc., Chicago, IL). The Pk

values were also calculated for BIS and AAI to test their ability to detect the anesthetic

state that corresponds with loss of “shake and shout” responses (OAA/S <=1, Table 4.1).

4.3.4 Response Surface Models of the Processed EEG Parameters

Response surface models were constructed for each processed EEG parameter

using the Greco-Berenbaum model as shown below:37

1..

...

50505050

50505050

max

+

++

++

=n

B

B

A

A

B

B

A

A

n

B

B

A

A

B

B

A

A

EC

C

EC

C

EC

C

EC

C

EC

C

EC

C

EC

C

EC

CE

E

α

α

where CA, CB are the concentrations of the two drugs, EC50A, EC50B are drug

concentrations causing 50% of the maximal drug effect, EMAX is the maximal drug effect,

α characterizes the extent of interaction between both drugs, n is a measure of response

steepness For each processed EEG parameter, the data were pooled and used to fit the

three-dimensional response surface using a naïve pooled technique. Model coefficients

and standard errors were estimated using MATLAB (MathWorks Inc., Natick, MA).

102

Models were built by an iterative process in which the log likelihood between the

observations and the model predictions was maximized. The contribution of each

coefficient was evaluated by excluding it from the model and determining whether the

model deteriorated significantly using the likelihood ratio test (∆ Likelihood Ratio ≥

30%). The standard error of the model parameters was estimated using the bootstrap

method for 5000 iterations.38

Model performance was evaluated by assessment of Error Prediction (observed vs.

predicted probability of effect for each dose combination) and the correlation coefficient.

The Error Prediction is defined as the following:

ObservededictedObservedXError ediction /Pr100Pr −=

The correlation coefficient of the regression parameter estimates was used to

evaluate how well the nonlinear regression models described the observed data. A large

value of the correlation coefficient (≥ 0.7) indicates that the responses predicted from the

surface described the observed data well.39

4.4 Results

All 24 volunteers completed the study. The demographics of the two groups are

shown in Table 4.2. There were no differences between the groups except that the

sevoflurane group contained equal numbers of male and female volunteers, whereas the

remifentanil group was predominately male volunteers.

For individual drugs, the relationship between the processed EEG parameters, the

measured drug concentrations, and the OAA/S score at each assessment point is shown in

Figure 4.2 and summarized in Table 4.3. We observed that most volunteers were sedated

(OAA/S ≤ 1) at sevoflurane concentrations greater than 1.5 vol. %. Adequate sedation

103


Group 1

Sevoflurane

Group 2

Remifentanil

Age [years] 25.0 ± 4.2 23.1 ± 2.7

Weight [kg] 70.8 ± 13.0 74.5 ± 9.3

Height [cm] 174.3 ± 9.0 177.8 ± 8.4

Sex [M:F] 4:4 7:1


females.

104

Figure 4.2: A scatter plot showing the relationship among processed EEG parameters,

individual anesthetic drug concentrations, and the clinical sedation scores. Each point

represents an assessment after target concentrations of the drug were achieved. Open

circles represent observations classified as conscious (volunteers responded to verbal

command, OAA/S ≥ 3), whereas filled circles are considered unconscious.

105

Table 4.3: Prediction Probability (Pk) - OAA/S Score*

BIS AAI Sevoflurane

End Tidal

[vol %]

Remifentanil

Ce

[ng••••mL-1

]

SEVO 0.97 (0.01) 0.87 (0.03) 0.99 (0.01) N/A

SEVO-REMI 0.87 (0.01) 0.75 (0.02) 0.87 (0.01) 0.56 (0.03)

REMI 0.76 (0.04) 0.52 (0.05) N/A 0.93 (0.02)

* Standard Errors are given in parentheses

106

could not be achieved at remifentanil concentrations in the clinical range (5- 10 ng•mL-1

).

Sedation using remifentanil could be achieved at concentrations higher than 20 ng•mL-1

.

Figures 4.3a and 4.3b show the distribution of BIS and AAI at clinically relevant

sedation states-loss of responsiveness to shouting (OAA/S = 2), loss of responsiveness to

shaking and shouting (OAA/S = 1), and loss of responsiveness to noxious stimulus

(OAA/S = 0). The data are presented in a group where only sevoflurane was administered

and in a group in which the volunteers received a combination of sevoflurane and

remifentanil.


The parameters for all the response surface models were identifiable. The Greco

model parameters estimated through nonlinear regression are shown in Table 4.4. The

estimates of “goodness of fit” (e.g., Log Likelihood, Standard Errors, and Correlation

Coefficient) suggest that the models described the BIS data better than the AAI data. The

response surfaces that describe BIS and AAI at various target concentrations of

sevoflurane and remifentanil are shown in Figures 4.4a and 4.4b, respectively.

Throughout most of the clinically relevant range of concentrations (sevoflurane 0- 3 vol

% and remifentanil 0- 7.5 ng•mL-1

) the residual error is below 10%.

Isoboles from Logit response surface models for clinical sedation (95%

probability of OAA/S score ≤ 1) and tolerance of surgical incision (the 95% probability

of no movement or hemodynamic response to a 50 mA electric tetanic stimulation)

previously reported by Manyam, et al.,33

are shown in Figures 4.5 and 4.6. In addition,

the raw data for each of the processed EEG parameters and the predicted processed EEG

parameter values for the concentration target pairs on the previously described isoboles

107

Figure 4.3: A box plot showing the distribution of BIS (top panel, Figure 4.3a) and AAI

(bottom panel, Figure 4.3b) at clinically relevant sedation states (OAA/S ≤ 2). The data

are presented in two groups-the first group (open boxes) show the distribution in the

processed EEG parameters where volunteers received only sevoflurane. The second

group (shaded boxes) show the distribution in the processed EEG parameters when the

volunteers received a combination of sevoflurane and remifentanil.

108

Figure 4.3

109

Table 4.4: Mean Model Parameters for the Greco Response Surface for Sevoflurane

and Remifentanil*

EC50,Sevoflurane

[vol %]

EC50,Remifentanil

[ng••••mL-1

]

Synergy

(α)

Gamma

(γ)

Log

Likeli

hood

Correlation

Coefficient

BIS

2.37 (0.06) 38.02 (2.57) 0.52(0.39) 1.12(0.02)

-

281.58

0.89

AAI 0.62 (0.06) 76.06 (17.40) 1.15(1.33) 1.12(0.08) -1.24 0.60

* Standard Errors are given in parentheses

110

Figure 4.4: The Greco response surface model predictions of the sevoflurane-

remifentanil interaction for BIS (top panel, Figure 4.4a) and AAI (bottom panel, Figure

4.4b) for unstimulated volunteers are presented. The symbols show measured responses

and the surface predicted by the model is represented by the grid-lined surface. The raw

data used to create this model is shaded based on the residual error.

111

Figure 4.5: The panel on the top (Figure 4.5a) shows a topographic view of the raw data

(BIS) overlaid upon isoboles for adequate clinical sedation (95% probability of achieving

an OAA/S score ≤ 1) and adequate surgical analgesia (95% probability of no movement

response or hemodynamic response to a 50 mA tetanic electrical stimulation). The panel

on the bottom (Figure 4.5b) demonstrates the predictions of the BIS response surface

model (mean and standard deviation) at different concentration pairs along the isoboles

for adequate clinical sedation and surgical analgesia.

112

Figure 4.5

0 5 10 150

1

2

3

Remifentanil ng/mL

Se

vo!

ura

ne

%V

/V

EC95

P(OAAS ≤ 1)

EC95

P(Tol. 50 mA)

BIS

80-100

60-80

40-60

< 40

0 5 10 150

1

2

3

77(2)

81(0.9)

80(0.7)

77(0.6)

74(0.5)

71(0.5)

68(0.5)

72(2)

79(1)68(2)

69(1)

65(1)

61(1)

57(0.9)

52(0.9)

49(0.8)

45(0.8)

43(0.8)

69(1)

Remifentanil ng/mL

Se

vo

!u

ran

e %

V/V

EC95

P(OAAS ≤ 1)

EC95

P(Tol. 50 mA)

113

Figure 4.6: The panel on the top (Figure 4.6a) shows a topographic view of the raw data

(AAI) overlaid upon isoboles for adequate clinical sedation (95% probability of

achieving an OAA/S score ≤ 1) and adequate surgical analgesia (95% probability of no

movement response or hemodynamic response to a 50 mA tetanic electrical stimulation).

The panel on the bottom (Figure 4.6b) demonstrates the predictions of the AAI response

surface model (mean and standard deviation) at different concentration pairs along the

isoboles for adequate clinical sedation and surgical analgesia.

114

Figure 4.6

0 5 10 150

1

2

3

Remifentanil ng/mL

Se

vo

!u

ran

e %

V/V

EC95

P(OAAS ≤ 1)

EC95

P(Tol. 50 mA)

AAI

75-100

50-75

25-75

< 25

0 5 10 150

1

2

3

60(11)

55(10)

51(9)

47(9)

44(9)

42(9)

39(9)

62(11)

58(10)52(9)

45(9)

40(9)

37(9)

34(10)

32(10)

30(10)

29(10)

28(10)

49(9)

Remifentanil ng/mL

Se

vo

!u

ran

e %

V/V

EC95

P(OAAS ≤ 1)

EC95

P(Tol. 50 mA)

115

are overlaid onto the isoboles. These figures clearly demonstrate that the addition of

small amounts of remifentanil (2.5 ng•mL-1

) results in an increase in the target BIS and

AAI necessary to produce clinically adequate sedation or anesthesia (Figures 4.5b and

4.6b).

4.5 Discussion

In this study, we utilized the volunteer paradigm previously employed by our

laboratory25,33

and others27,40,41

to generate response surface models for two anatomically

distinct processed EEG parameters (BIS and AAI) during the concomitant administration

of a wide range of target concentration pairs of a prototypic potent volatile anesthetic,

sevoflurane, and a prototypic potent synthetic opioid, remifentanil. Although we had

previously demonstrated that remifentanil synergistically potentiates the sedative effects

of sevoflurane,33

we did not observe more than a mild, additive increase in BIS and AAI

with the addition of remifentanil to a sevoflurane anesthetic. The fact that the BIS and

AAI are both insensitive to the observed changes in the clinical sedation state produced

by the addition of a small to moderate dose of remifentanil to a sevoflurane anesthetics

suggests that sevoflurane-remifentanil anesthetics titrated to traditional BIS or AAI

targets would result in a deeper than predicted anesthetic state. With an estimated effect

site concentration of 5 ng•mL-1

of remifentanil (an infusion of approximately 0.2 µg•kg-

1•min

-1), no more than 1% sevoflurane is required to produce clinically adequate

anesthesia without any concern of explicit recall, and yet the BIS would > 65 and the

AAI would be > 40. Therefore, during sevoflurane-remifentanil anesthesia, targeting a

BIS < 60 or an AAI <30 may result in too deep of an anesthetic state. This work

identifies an important limitation of the currently available algorithms of two distinct

116

processed EEG parameters and should serve as the basis for future development and

validation of any depth of anesthesia monitor.

4.5.1 Concentration-Effect Relationship

When examining the effects of prototypic anesthetic agents from a single drug

class on the processed EEG parameters, the administration of sedatives-hypnotic agents

(e.g., sevoflurane or propofol) results in a clear dose dependent increase in anesthetic

depth. In contrast, the administration of an opioid in isolation does very little to decrease

the processed EEG parameter (increase anesthetic depth) until extremely high

concentrations of the opioid are achieved. Our results were similar-we observed that the

BIS and AAI correlate well with sevoflurane concentrations (Pk‘s 0.97 and 0.87,

respectively) and more poorly with remifentanil (Pk‘s 0.76 and 0.52, respectively). In

addition, the BIS had a wider dynamic range in response to increasing drug concentration

than the AAI, consistent with previous reported response of the BIS and AAI.42-44

The

wider dynamic range available with the BIS could potentially translate into easier

titration of sevoflurane than with the small dynamic range of the AAI. However, the

ability of a monitor to track the concentration changes of a drug does not necessarily

improve its performance in predicting the depth of anesthesia. Therefore, when

developing algorithms to measure clinical depth of anesthesia, it is more important to

focus on capturing the clinical anesthetic state rather than the change in anesthetic drug

concentration(s).

We determined the concentration-CNS effect relationship of opioids using

remifentanil as a prototype opioid. Although, a remifentanil effect site concentration

above 15 ng•mL-1

(an infusion of approximately 0.6 µg•kg-1

•min-1

) is rarely used in

117

clinical practice we sampled remifentanil concentrations up to 60 ng•mL-1

in an attempt

to rigorously capture the sedative effects of remifentanil. Within the clinical range, we

did not observe a clinically significant level of sedation with remifentanil. The variability

in BIS and AAI within this range was similar to that observed when volunteers did not

reach a clinical level of sedation with sevoflurane. At supra-clinical remifentanil

concentrations, remifentanil produced a clinically significant level of sedation; however,

this opioid induced sedation rarely approached an OAA/S score of 1. In addition,

increasing the level of clinical sedation with remifentanil did not alter the AAI although

the BIS decreased modestly. Our results are similar to previous reports that showed that

the processed EEG parameters are insensitive to opioids 9,28,29

within the clinical range.

4.5.2 Prediction Probability

Several previous reports have demonstrated that the BIS and the AAI are useful

surrogates of depth of anesthesia.3 The BIS showed less variation at each level of clinical

sedation than did the AAI (Figures 4.3a and 4.4b). This may be an intrinsic characteristic

of the arbitrary scaling of the AAI to have its operating range for general anesthesia

between 0-30, therefore, small changes in clinical state might result in a large (erroneous)

increase in AAI. An alternative explanation might be the fact that the brainstem auditory

pathways are well preserved during moderate levels of anesthetics resulting in an

increased sensitivity to ascending (sensory) signals.13,14

Finally, the increased variability

may simply be the result of the more primitive (and poorer performing) electromyogram

filtering algorithms available on the early model AAI compared to the more developed

BIS.

118

Our results are in agreement with previous reports that have demonstrated that the

BIS outperforms the AAI when evaluating the performances of the processed EEG

parameters utilizing Prediction Probabilities (Pk).18,45

However, prediction probabilities

are limited in that they report only the direction and the goodness of correlation between

the clinical sedation score and the processed EEG parameter- they do not give any feel to

whether the change in the parameter is large or small. Therefore, even though the

addition of remifentanil to a sevoflurane anesthetic resulted in a minor change in

processed EEG parameters that was underwhelming compared to the large change in

clinical sedation, the modest decrease in the prediction probabilities does not reflect the

inability of the BIS or the AAI to capture the observed clinical change.


As a complement to prediction probability analyses, response surfaces analysis

was used to study the pharmacodynamic effects of adding remifentanil to a sevoflurane

anesthetic. Response surface methods have been utilized to model the interactions

between varieties of combinations of anesthetics. Using the Greco form of the response

surfaces models, we were able to characterize the relationship between the effect site

concentrations of remifentanil, the end tidal concentrations, and the BIS with a low

amount of error (R2 > 0.8). The response surface model for AAI had moderately good

correlation (R2 > 0.8), with the poorer fit most likely related to the larger variability in the

response and the smaller operating range. The pharmacodynamic response surface

revealed that the addition of remifentanil decreased the BIS in a minor and additive

fashion, whereas the AAI response surface showed that AAI is not significantly affected

by the addition remifentanil. A possible explanation for this difference is that the

119

brainstem responses are relatively resistant to opioid effects46

while the cortical responses

are decreased with the inhibition of ascending sensory signals.47

In order to give clinical meaning to the predictions made by the response surface

models for processed EEG parameters, we utilized the Logit response surface models for

adequate sedation for general anesthesia (probability of providing an OAA/S score ≤ 1)

and for adequate analgesia for general anesthesia (probability of no movement of

hemodynamic response to a 50 mA electrical tetanic current) that were previously

described by our laboratory33

to generate 95%tile

isoboles. Then the predicted values for

the BIS (Figure 4.5b) and the AAI (Figure 4.6b) for a variety of target concentration pairs

of sevoflurane and remifentanil that lay on the two isoboles were calculated from the

response surface models generated in this manuscript. These figures demonstrate that

with the addition of a modest remifentanil effect site concentration of 5 ng•mL-1

(an

infusion of approximately 0.2 µg•kg-1

•min-1

), adequate sedation would be provided with a

BIS of 81 and an AAI of 57 and adequate general anesthesia would be provided with a

BIS of 65 and an AAI of 41- all values considerably higher than the usual target range of

either of the processed EEG parameters (BIS 40-60 and AAI 15-30). Therefore, the

inability of the two anatomically distinct processed EEG parameters to characterize the

increase in clinical sedation and the increase in clinical anesthetic depth brought about by

the addition of even modest doses of remifentanil to a sevoflurane anesthetic would result

in an overdose in the amount of sevoflurane administered and “too deep” of a clinical

anesthetic level being targeted (Figure 4.7).

120

Figure 4.7: The isoboles that produce target BIS values of 40, 50, 60, and 70 are overlaid

upon isoboles for adequate clinical sedation (95% probability of achieving an OAA/S

score ≤ 1) and adequate surgical analgesia (95% probability of no movement response or

hemodynamic response to a 50 mA tetanic electrical stimulation).

121


The processed electroencephalogram (EEG) has emerged as an important

surrogate measure of the depth of anesthesia.9,48

Surrogate measures are employed when

the clinical drug effect of interest is difficult or impossible to measure. The processed

EEG has many characteristics of the ideal surrogate. In contrast to more clinically

oriented measures of drug effect, it is can be an objective, continuous, reproducible, non-

invasive, high resolution signal.3 It can also be used as an effect measure when an

experimental subject is unconscious, whereas many of the more clinically oriented

measurements require awake, cooperative subjects.49

The ability of the addition of even a small amount of synthetic opioid to decrease

the amount of potent volatile anesthetic required to produce clinically adequate

anesthesia has been reported in surgical patients using isobologram or dose reduction

analyses. Furthermore, previous work from our laboratory has demonstrated that the

addition of remifentanil to sevoflurane33

or propofol25

anesthetics results in a synergistic

increase in depth of anesthesia. In contrast, the lack of ability of the two processed EEG

parameters studied here to detect the increase in anesthetic depth produced by the

addition of even modest amounts of a synthetic opioid has been demonstrated with

isobologram analysis of surgical patients. Similar to our results here, response surface

analysis performed by Dahan, et al.,27

investigating the interaction of moderate levels of

alfentanil and sevoflurane anesthesia has shown that there is no increase in anesthetic

depth as measured by the BIS. Therefore, it would appear that despite the clinically

significant increase in the clinical sedation level and the anesthetic depth produced by the

addition of modest amounts of remifentanil to a sevoflurane anesthetic, there is minimal

122

effect of even supra-therapeutic doses of opioid on the depth of anesthesia measured by

the BIS and the AAI.

Our response surface models demonstrate that the targeting the familiar operating

range for the BIS of 40-60 would result in a 50-150% higher end tidal sevoflurane

concentration being administered than would be needed to provided clinically adequate

anesthesia if a modest dose of remifentanil (effect site concentration of 5 ng•mL-1

) was

administered (Figure 4.7). Besides the anticipated hemodynamic side effects expected

from this anesthetic overdose,21

if delivering too deep of an anesthetic (BIS < 40) results

in a reproducible increase in one year mortality,12

the resulting deep anesthesia could

have significant implications long after the perioperative period has ended. Therefore,

either new “context sensitive” operating ranges for the processed EEG parameters must

be derived to account for the unmeasured effects of the addition of varying doses of

opioids; a suitable easily usable “fudge factor” should be derived for adjusting the

measured processed EEG parameter for the opioid contribution; any anesthesiologist who

wanted to utilize a processed EEG parameter to titrate the administered anesthetic should

limit the administration of opioid to the emergence period as to avoid needing to calculate

the “corrected” BIS or AAI; or a monitor sensitive to the actual clinical conditions, with

or without opioids, needs to be developed. It is possible that the combination of real-time

pharmacokinetic-pharmacodynamic displays50

with the addition of the response surfaces

described here would be able to numerically and graphically provide anesthesiologists

with real time feedback as to the actual (predicted) clinical depth of anesthesia during a

balanced anesthetic. However, the lack of a ready solution suggests that the delivery of a

balanced anesthetic utilizing a closed loop controlled based on any of the conventional

123

processed EEG parameters could possibly result in clinically deeper anesthetics than

desired, especially if the algorithm attempts to utilize the unique pharmacokinetic and

pharmacodynamic characteristics of remifentanil to improve responsiveness and

pharmacologic control.51

Previously, we had identified “optimum” target combinations of sevoflurane and

remifentanil that provided adequate surgical anesthesia and minimized the time to

awakening.33

For anesthetics ranging in length between 0.5-24 hours, the target

sevoflurane concentration varied from 1.10-0.75% and the target remifentanil

concentration ranged from 4.1-6.1 ng•ml-1

(infusion rates of 0.15-0.22 µg•kg-1

•min-1

).

Targeting these optimum combinations would produce clinically adequate surgical

anesthesia with BIS (65-69) and AAI (41-46) higher than the normal operating ranges

suggested by the manufactures.

4.5.5 Limitations

The fact that our response surface models were determined in unstimulated

volunteers is a major constraint that may limit the applicability of our results. In

particular, the lack of constant stimulation from an endotracheal tube or the continuous

pain form a surgical incision may result in our volunteer data underestimating the

anesthetic requirements of surgical patients. However, the advantages of the volunteer

study paradigm to develop response surface models-key surgical stimulation can be

applied multiple times, repeated measurements can be made on the same subject, and the

entire dynamic range of anesthetic combinations can be examined, all without ethical

concerns of providing inadequate anesthesia during a surgical procedure, continues to

make the volunteer study paradigm popular.

124

The fact that we utilize pharmacokinetic models to predict the remifentanil effect

site concentration in lieu of measuring the actual blood drug concentration may

compound some of the variability in the opioid only, single drug data.52

However, as in

our previous study,33

there is convincing evidence to demonstrate that this may not be a

major source of pharmacokinetic variability. Another source of pharmacokinetic

variability may be the targeting of an end tidal alveolar pseudo-steady state of volatile

anesthetic instead of targeting the effect site concentration. The steady-state partial

pressure of the volatile anesthetic at the effect site correlates with the measured end tidal

alveolar partial pressure at steady state. However, achieving pseudo-steady state at the

alveoli results in an effect site concentration that would most likely not reach its own

pseudo-steady state. We did not choose to target a pseudo-steady state at the effect site

because we would have to assume a priori knowledge of which anatomic compartment

contained the pharmacologic effect site for sedation and for clinical anesthesia. Given the

fact that volatile anesthetics produce sedation through a supra-spinal site of action while

immobility is produced at the spinal cord level,53

the choice of effect site to target in the

pharmacokinetic simulations to determine when pseudo-steady state at the effect site is

achieved is one of many difficult assumptions that would be needed to construct an

accurate pharmacokinetic-pharmacodynamic model for sevoflurane. In addition, the time

involved in achieving a steady state alveolar concentration or a pseudo-steady state effect

site concentration would be prohibitively longer than that required to achieve alveolar

pseudo-steady state.

Although remifentanil induced hyperalgesia has been observed in the patients54

and volunteers55

receiving infusions of various durations, as detailed in our prior

125

manuscript,33

we did not find any differences between the baseline levels of tolerated

stimuli and the levels of stimuli tolerated at the lowest doses of sevoflurane. In addition,

one could conjecture that any opioid hyperalgesia that developed would not effect the

clinical sedation score (OAA/S) or the processed EEG parameters that were determined

during quiet periods prior to the determination of the analgesic response of each of the

targeted concentration pairs.

The Greco response surface model used to describe the response surface models

generated here is different than the Logit model utilized in the previous manuscripts from

our laboratory.33,56

Although the Logit model proved advantageous for the modelling of

stimuli whose responses can be dichotomized, the Greco model,37

along with the models

described by Minto57

and Bouillon,58

all handle continuous response variables (e.g.,

processed EEG parameters) extremely well. The main advantage of the Greco model is

that it assumes a sigmoidal Emax structure that is readily familiar to most readers of

pharmacodynamic modelling. The biggest limitation of the Greco model is that it cannot

account for a partial agonist-it presumes that remifentanil at sizeable concentrations will

produce a BIS or AAI of 0. This assumption causes a bias in the determination of the

response surface, however, because no model that accounts for partial agonists currently

exists, there is no way to overcome this limitation. Even with the assumption that Greco

model does not account for a partial agonist, by setting the CMAX, REMI at a high enough

value (i.e., 400 ng•mL-1

), the error in the response surface is not significantly large to

cause a change in model predictions.

126

4.5.6 Conclusions

Although clinical sedation increases significantly with the addition of a small to

moderate dose of remifentanil to a sevoflurane anesthetic, the BIS and AAI are

insensitive to this change in clinical state. Therefore, during sevoflurane-remifentanil

anesthesia, targeting a BIS < 60 or an AAI <30 may result in too deep of an anesthetic

state. If providing “too deep” of an anesthetic state produces undesirable side effects,

such as intraoperative hemodynamic instability or an increase in one year mortality,

correcting the measured processed EEG parameter to account for the actual measured

clinical anesthetic depth would be required to prevent these undesirable side effects. As a

first step, by superimposing the isobolograms for adequate surgical anesthesia and

adequate sedation on top of the isobolograms for various targets values for BIS or AEP, a

figure is developed that can be utilized to make crude clinical adjustments to either the

combination of sevoflurane and remifentanil administered or the targeted BIS or AEP

value necessary to produce the desired clinical depth of anesthesia (Figure 4.7).

Incorporation of these response surfaces into a real-time, pharmacokinetic-

pharmacodynamic display system50

may allow more precise concentration pairs or target

adjustments.

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87: 842-8

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effects of anesthesia with increasing end-expiratory concentrations of sevoflurane on

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fentanyl, alfentanil, remifentanil and sufentanil on loss of consciousness and bispectral

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rapidly extracted auditory evoked potentials index responses to noxious stimulation

during sevoflurane anesthesia. Anesth Analg 2004; 99: 1141-6, table of contents

43. Alpiger S, Helbo-Hansen HS, Vach W, Ording H: Efficacy of A-line AEP

Monitor as a tool for predicting acceptable tracheal intubation conditions during

sevoflurane anaesthesia. Br J Anaesth 2005; 94: 601-6

44. Alpiger S, Helbo-Hansen HS, Vach W, Ording H: Efficacy of the A-line

AEP monitor as a tool for predicting successful insertion of a laryngeal mask during

sevoflurane anesthesia. Acta Anaesthesiol Scand 2004; 48: 888-93

45. Kreuer S, Bruhn J, Larsen R, Buchinger H, Wilhelm W: A-line, bispectral

index, and estimated effect-site concentrations: a prediction of clinical end-points of

anesthesia. Anesth Analg 2006; 102: 1141-6

46. Schwender D, Rimkus T, Haessler R, Klasing S, Poppel E, Peter K:

Effects of increasing doses of alfentanil, fentanyl and morphine on mid-latency auditory

evoked potentials. Br. J. Anaesth. 1993; 71: 622-628

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dosage for equivalent intraoperative electroencephalographic suppression in patients with

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48. Gan TJ, Glass PS, Windsor A, Payne F, Rosow C, Sebel P, Manberg P:

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propofol, alfentanil, and nitrous oxide anesthesia. BIS Utility Study Group.


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51. Struys MM, Mortier EP, De Smet T: Closed loops in anaesthesia. Best

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Exp Med Biol 2003; 523: 35-43





CHAPTER 5

PROCESSED EEG SIGNALS AS INDICATORS OF

INADEQUATE ANESTHESIA §

5.1 Abstract

5.1.1 Background

The processed auditory evoked potential (AAI, Danmeter, Odense, Denmark)

and the Bispectral Index (BIS, Aspect Medical Systems, Newton, MA) of the

electroencephalogram are two mechanistically different technologies used to assess the

functional depression of the central nervous system during general anesthesia. The aim of

this study was to compare how the AAI and BIS perform in response to noxious

stimulation in volunteers who are profoundly sedated. This study examines the possibility

of using the AAI and BIS monitors intraoperatively to detect patient responses to

stimulation under inadequate anesthesia.

5.1.2 Methods

After obtaining institutional approval and informed consent, twenty two healthy

adult male and female volunteers were enrolled. Volunteers received a combination of

opioid (remifentanil, REMI) and hypnotic drug (sevoflurane, SEVO or propofol, PROP)

§ Will be submitted for review in Anesthesia & Analgesia, July 2006. Will be published in

Anesthesia & Analgesia pending review. Original article titled: “The auditory evoked

potential and bispectral index: A comparison of signal performance during clinically

inadequate anesthesia.”

133

at various target concentration pairs spanning the entire clinical spectrum (i.e. REMI 0-80

ng•mL-1

(computer controlled infusion), PROP 0-7.5 mcg•mL-1

(computer controlled

infusion) and end-tidal SEVO ranging from 0-7% atm). AAI, BIS, and heart rate were

digitally acquired throughout the experiment. Baseline AAI and BIS values were

recorded after volunteers reached steady-state drug levels. A series of randomly applied

experimental pain stimuli (pressure algometry on the leg to 50 psi, electrical tetany on the

leg to 50 mAmps, and thermal stimuli on the forearm to 50° C) were used to assess the

level of anesthesia. Response to stimulation was defined as withdrawal movement or a

heart rate increase of 20%. The magnitude and time course of AAI and BIS changes in

the first minute after volunteer response were considered the outcome of interest.

Artifactual corrupted AAI and BIS signals (movement artifact, etc.) were not analyzed.

For volunteers with OAAS <= 1 (i.e., subjects sedated as during general anesthesia), the

magnitude of the change in the AAI and the BIS values were plotted versus time to

examine the signal response in patients with and without adequate anesthesia, as assessed

by heart rate change and withdrawal movement in response to stimulation.

5.1.3 Results

All 22 subjects completed the experiment. The temporal profiles of AAI and BIS

values showed responses at a latency of 40 and 50 seconds respectively. For volunteers

with sedation scores equivalent to loss of conscious response(OAAS >= 1) both the AAI

and the BIS values showed robust responses when there was a heart rate or withdrawal

movement response to experimental pain stimuli. In those subjects in whom there were

no responses to pain, the AAI and BIS values showed no change compared to

prestimulation values.

134

5.1.4 Discussion

In this observational study, application of experimental pain measures to

volunteers receiving various combinations of remifentanil and sevoflurane producing

sedation scores equivalent to adequate anesthesia (OASS <= 1) resulted in robust

increases in the AAI and BIS values only in those volunteers who showed other signs of

inadequate anesthesia-withdrawal movement or increase in heart rate.

5.2 Introduction

Clinicians often depend on unreliable, nonspecific measures of anesthetic effect 1

such as hemodynamics, reflexes to stimuli, spontaneous respiration rate, etc. to determine

the level of anesthetic effect. To use these methods the clinician is dependent on a

number of factors such as training, experience and availability of intraoperative

monitoring methods. Some measures such as blood pressure are rarely available on a

continuous basis intraoperatively. Hemodynamic responses are often affected by the

presence of vasoactive and ionotropic drugs.2 A practical, more reliable method is needed

to determine patient responses to inadequate anesthesia. Such methodology would

improve intraoperative monitoring, enable more accurate drug administration, and may

eventually lead to closed loop computer controlled drug delivery.3,4

Processed EEG parameters are gaining popularity as intraoperative monitors of

depth of anesthesia.5 One such example, the Bispectral Index (BIS, Aspect Medical

Systems, Newton, MA), is based on Bispectral analysis of the EEG.6 The propriety BIS

algorithm was a unique step forward in the use of EEG parameters to determine

anesthetic depth because it combined multiple distinct EEG parameters and a large

135

volume of prospectively collected clinical observations into a single descriptive variable

which was then prospectively tested and validated.5

During general anesthesia, the brainstem and the midbrain auditory function is

preserved, although meaningful interpretation of the auditory stimulus is inhibited.7,8

These brainstem responses to an auditory stimulus correlate with motor signs of

wakefulness and intraoperative awareness.9 The preservation of brainstem responses that

correlate with inadequate anesthesia (movement or awareness) suggests that the auditory

evoked potential (AEP) might be more robust in detecting inadequate anesthesia as

opposed to the EEG which solely monitors the cortical activity.10,11

The A-Line AEP

Index (AAI, Danmeter, Odense, Denmark) is the first commercially available monitor

that utilizes changes within the AEP to measure the depth of anesthesia.12

Like the BIS,

the AAI correlates well with the clinical level of sedation produced by increasing doses

of sevoflurane7,13

or propofol.6,14

The principle aim of this study was to measure AAI and BIS responses to

stimulation in volunteers who were clinically sedated. We use multimodal experimental

pain measures to elicit movement or heart rate responses in volunteers anesthetized using

a combination of sevoflurane and remifentanil or propofol or remifentanil. The

magnitude and latency of BIS and AAI responses were estimated off line. We

hypothesized that the responses shown by processed EEG parameters (BIS and AAI) are

comparable to traditional markers of inadequate anesthesia such as increased heart rate or

movement. In addition, we hypothesized that the modality of stimulus, i.e., thermal,

electrical and mechanical, did bias the responses shown by processed EEG parameters.

136


A portion of the data from this data set were published previously in a manuscript

examining the synergistic interaction between remifentanil and sevoflurane in producing

clinical sedation and analgesia to experimental painful stimuli that are surrogates for

intraoperative painful stimuli.15

Because of the minor overlap between the hypotheses of

the previous and the current manuscript and the large amount of data reported in each

manuscript, each analysis is reported in a separate manuscript.





Each

volunteer was randomized to receive a target controlled infusion of remifentanil

(predicted effect site concentrations of 0.5-15 ng•mL-1

) or target controlled infusion of

propofol (predicted effect site concentrations of 0.5-7.5 mcg•mL-1

) or sevoflurane (0.3-6

vol % end tidal alveolar concentration) as the primary agent with the other drug acting as

the secondary agent (Figure 5.1). Five minutes after achieving the targeted effect-site

concentration (or stable end-tidal concentration) for a primary drug “step,” a battery of

pharmacodynamic assessments were made. Effect measures included the Observer’s

Assessment of Alertness/Sedation score (OAA/S)17

and three surrogates for surgical

stimulus- pressure algometry and tetanic electrical stimulation, as previously described

by Kern,18

and thermal stimulation.

The reader is referred to the previous manuscript by Manyam, et al.,15

for

complete details regarding the methods of volunteer preparation, drug administration, and

137

Figure 5.1: A schematic summary of the data collection and analysis. At each target

concentration pair, baseline measurements of AAI and BIS were determined by averaging

monitor indices in a 40 second time window (upper panel). Responses were elicited by

gradually increasing stimulus level in until the volunteers showed signs of discomfort

(20% increase in heart rate or a movement response). A safety limit of stimulation was

defined to prevent long term pain that could confound successive measurements (middle

panel). The AAI and BIS signals in the response time window (bottom panel) were used

for data analysis. Time “zero” in the response time window corresponds to the time at

which the volunteers responded in case of “responders” or the time at which the safety

limit was reached in the case of “non responders”.

138

Figure 5.1

139

data collection. Because the methods of data analysis and statistical analysis have

substantial differences from the previous manuscript, they are provided in complete

detail.

5.3.1 Baseline BIS and AAI Measurements

To avoid variability arising from hysteresis between plasma concentration and

effect site, BIS and AAI were measured at each assessment point five minutes after the

targeted effect-site concentration (or stable end-tidal concentration) for a primary drug

“step,” was reached. The processed EEG parameters were averaged in a 40 second

interval that preceded the assessment of the Observer’s Assessment of Alertness/Sedation

score (OAA/S).17

This interval was also considered a “quiet time” where no other

changes or assessments were made in the volunteers. Data resulting from faulty sensors

or monitor malfunction were not included in the subsequent analyses.

5.3.2 Demographic Data Analysis





5.3.3 Definition of Volunteer Responses

Volunteer responses to stimulation were defined as a movement and/ or a 20%

increase in heart rate.

140

5.3.4 Time Series Analysis

The protocol for determining responses in processed EEG parameters is outlined

in Figure 5.1. AAI and BIS signals stored were time aligned with the patient responses.

Time zero represents the time at which the volunteers responded to stimulation or the

maximal permissible stimulus was reached. Signal analysis was performed using

MATLAB (MathWorks Inc., Natick, MA). The magnitude of the response was defined as

the percentage change from the baseline assessment. The Percent change is defined as the

following:

( ) BISorAEPBISorAEPBISorAEP BaselineBaselineStimXngePercentCha /100 −=

The latency of responses was identified by time-averaging all the responses. The

window in which the percentage change of the processed EEG parameters exceeded

baseline variation was defined as the “Time-Window”. The average signal within this

time window was used in comparing responses across stimuli and comparing responses in

volunteers who were awake from those who were sedated.

5.4 Results

All 22 volunteers completed the study. The demographics of volunteers are shown

in Table 5.1.

The time course of AAI and BIS signal changes is shown in Figure 5.2 and Figure

5.3. The percent change at 30 seconds prior to volunteer response, the time of response (0

sec.), 30 and 60 seconds after the response are represented by the box plots. The central

line indicates the median value and the whiskers indicate 10 and 90% intervals. The

average response, computed by averaging all signals within the response time window is

shown in as a gray trace in Figure 5.2 and Figure 5.3. An unpaired t-test indicated that

141


Group 1

Sevoflurane

Group 2

Propofol

Group 2

Remifentanil

Age [years] 25.0 ± 4.2 28.6 ± 7.9 23.0 ± 3.0

Weight [kg] 70.8 ± 13.0 72.9 ± 13.2 75.0 ± 9.0

Height [cm] 174.3 ± 9.0 172.7 ± 10.5 178.0 ± 8.0

Sex [M:F] 4:4 8 : 3 7: 1


females.

142

Figure 5.2: A box plot showing the time course of AAI response to stimulation. The

average signal change is shown by the gray trace. The upper panel shows the AAI signal

change in volunteers who showed signs of discomfort when stimulated. The bottom panel

shows percent change AAI signal change in volunteers who showed no signs of

discomfort. Filled circles indicate outlier data.

143

Figure 5.3: A box plot showing the time course of BIS response to stimulation. The

average signal change is shown by the gray trace. The upper panel shows the BIS signal

change in volunteers who showed signs of discomfort when stimulated. The bottom panel

shows percent change BIS signal change in volunteers who showed no signs of

discomfort. Filled circles indicate outlier data.

144

responders and non responders differed with a significance values of < 0.01 for AAI and

<0.001 for BIS. The latency of responses was defined as the average time at which the

signal increased more that two standard deviations from its mean value.

5.4.1 Sedated vs. Awake volunteers

Signal responses in volunteers who did not have a clinical level of sedation, i.e.,

OAA/S >=2 volunteers were not oblivious to shaking, were compared against those who

were sedated to level at which they did not respond to shaking prior to stimulation. The

average percent change indicated in Figure 5.4 indicates larger changes in the signal in

volunteers who were awake than sedated.

5.4.2 Modality of Stimulus

Signal responses were compared during stimulation with multiple experimental

pain measures, attempted laryngoscopy and OAA/S assessment (Figure 5.5). The

monitors showed no preference to a particular stimulus modality although laryngoscopy,

considered a much more intense form of stimulation than other experimental showed the

largest change.

5.5 Discussion


laboratory15,18

and others19-21

to elicit responses to stimulation at varying levels of

anesthesia. Processed EEG parameters (BIS and AAI) were recorded during stimulation

using a variety of experimental pain measures and attempted laryngoscopy. The average

change in processed EEG parameter differed in those volunteers that exhibited movement

or heart rate increases in response to stimulation from those volunteers that did not

respond to stimulation. The changes in processed EEG signals were observed with in a

145

Figure 5.4: A box plot comparing signal responses in sedated (OAA/S <=1, or loss of

responsiveness to shaking and shouting) and awake (OAA/S >=2, or responsive to

shouting) volunteers. The upper and lower panels show AAI and BIS responses

respectively. Data is only shown for volunteers who responded to stimulation. Filled

circles indicate outlier data.

146

Figure 5.5: A box plot comparing signal responses among different stimuli. OAA/S

assessment, a predominantly auditory stimulus was considered to see if it produced any

change in signals. Data is only shown for volunteers who responded to stimulation. Filled

circles indicate outlier data.

147

reasonable latency (30-40 seconds) from the actual volunteer response. These results

suggest that processed EEG monitoring could provide a potential advantage if used

during surgery to predict patient’s anesthetic state when traditional markers of inadequate

5.5 Discussion


laboratory15,18

and others19-21

to elicit responses to stimulation at varying levels of

anesthesia. Processed EEG parameters (BIS and AAI) were recorded during stimulation

using a variety of experimental pain measures and attempted laryngoscopy. The average

change in processed EEG parameter differed in those volunteers that exhibited movement

or heart rate increases in response to stimulation from those volunteers that did not

respond to stimulation. The changes in processed EEG signals were observed with in a

reasonable latency (30-40 seconds) from the actual volunteer response. These results

suggest that processed EEG monitoring could provide a potential advantage if used

during surgery to predict patient’s anesthetic state when traditional markers of inadequate

anesthesia such as heart rate, blood pressure and movement can not be used. Although the

results show an easily observable change in the processed EEG parameters, the results

also showed large baseline variations the signals that may result to poor confidence in

changes that actually result from inadequate anesthesia. This work identifies an important

limitation of the currently available algorithms of two distinct processed EEG parameters

and should serve as the precursor for future development and validation of any depth of

anesthesia monitor.

148

5.5.1 Limitations

One of limitations in our study design is that a gradually increasing stimulus was

used as opposed to using a constant level of stimulus and changing the anesthetic depth to

elicit a response. While this approach is a closer replicate of inadequate anesthesia during

a typical surgical procedure, such a study design may prove difficult in a volunteer

setting. Changing the anesthetic depth in a step wise manner such that a response is

observed may expose the volunteers to a greater level of discomfort than necessary.

Anesthesia is best described as a “yes/no” phenomenon which can be described as a

probability; a graded change in the level of anesthesia may not be feasible.

A further limitation of our study design was that the surrogate pain stimuli used to

measure the analgesic response in volunteers is only a surrogate of intra-operative

surgical pain. By including a range of experimental pain stimuli to cover the range

expected during a surgical procedure, it is probable that the most stimulating intra-

operative events-surgical incision and laryngoscopy-could have been recreated in the

volunteer laboratory. However, since surgical patients can not be deliberately subject to

conditions that create inadequate anesthesia, volunteer studies are essential to allow the

collection of the high quality data needed to achieve the goal of determining monitor

response characteristics during inadequate anesthesia.

5.5.2 Conclusions

Processed EEG monitors such as, AAI and BIS showed robust responses only in

those volunteers who showed other signs of inadequate anesthesia-withdrawal movement

or increase in heart rate. Response latency of 20-60 seconds was observed. Although AAI

149

showed larger response amplitude when compared BIS, the large baseline variability in

the signal may limit its application as a monitor to detect inadequate anesthesia.

5.6 References

1. Schneider G, Sebel PS: Monitoring depth of anaesthesia. Eur J

Anaesthesiol Suppl 1997; 15: 21-8

2. Berne RM, Levy MN: Physiology. Fourth Edition, Mosby 1998

3. Locher S, Stadler KS, Boehlen T, Bouillon T, Leibundgut D, Schumacher

PM, Wymann R, Zbinden AM: A new closed-loop control system for isoflurane using

bispectral index outperforms manual control. Anesthesiology 2004; 101: 591-602

4. Glass PS, Rampil IJ: Automated anesthesia: fact or fantasy?







7. Schwender D, Conzen P, Klasing S, Finsterer U, Poppel E, Peter K: The

effects of anesthesia with increasing end-expiratory concentrations of sevoflurane on

midlatency auditory evoked potentials. Anesth Analg 1995; 81: 817-822

8. Plourde G, Belin P, Chartrand D, Fiset P, Backman SB, Xie G, Zatorre RJ:

Cortical processing of complex auditory stimuli during alterations of consciousness with

the general anesthetic propofol. Anesthesiology 2006; 104: 448-57

9. Schwender D, Klasing S, Madler C, Poppel E, Peter K: Depth of

anesthesia. Midlatency auditory evoked potentials and cognitive function during general

anesthesia. Int Anesthesiol Clin 1993; 31: 89-106

10. Schwender D, Daunderer M, Mulzer S, Klasing S, Finsterer U, Peter K:

Midlatency auditory evoked potentials predict movements during anesthesia with

isoflurane or propofol. Anesth Analg 1997; 85: 164-73

11. Schwender D, Golling W, Klasing S, Faber-Zullig E, Poppel E, Peter K:

Effects of surgical stimulation on midlatency auditory evoked potentials during general

anaesthesia with propofol/fentanyl, isoflurane/fentanyl and flunitrazepam/fentanyl.

Anaesthesia 1994; 49: 572-8

150

12. Struys MM, Jensen EW, Smith W, Smith NT, Rampil I, Dumortier FJ,

Mestach C, Mortier EP: Performance of the ARX-derived auditory evoked potential

index as an indicator of anesthetic depth: a comparison with bispectral index and

hemodynamic measures during propofol administration. Anesthesiology 2002; 96: 803-

16

13. Katoh T, Suzuki A, Ikeda K: Electroencephalographic derivatives as a tool

for predicting the depth of sedation and anesthesia induced by sevoflurane.


14. Schwender D, Faber-Zullig E, Klasing S, Poppel E, Peter K: Motor signs

of wakefulness during general anaesthesia with propofol, isoflurane and

flunitrazepam/fentanyl and midlatency auditory evoked potentials. Anaesthesia 1994; 49:

476-84







2002; 96: 400-8

17. Chernik DA, Tucker M, Gigli B, Yoo K, Paul K, Laine H, Siegel JL:

Validity and reliability of the Neurobehavioral Assessment Scale. J Clin

Psychopharmacol 1992; 12: 43-8



100: 1373-81

19. Vuyk J: Clinical interpretation of pharmacokinetic and pharmacodynamic

propofol-opioid interactions. Acta Anaesthesiol Belg 2001; 52: 445-51



16




CHAPTER 6

SUMMARY AND CONCLUSIONS

This dissertation is broadly aimed at improving anesthetic drug management

within the operating room. The dissertation contains two approaches to meet this goal.

The specific aims of the two approaches are to (1) determine the combined

pharmacodynamic effect caused by opioids and hypnotic drugs and prescribe a safe and

efficient combination that will meet the needs of a general patient population and (2)

provide anesthesiologists with real-time feedback of the patient’s anesthetic state thus

enabling them to identify outliers and improve accuracy of drug titration.

6.1 Summary

The first specific aim is addressed by Chapters 2 and 3. The results can be

summarized as follows:

• A volunteer study was conducted to determine the interaction between a

commonly used volatile hypnotic drug (sevoflurane) and a commonly used opioid

(remifentanil). Analgesia and sedation were quantified at various concentrations

of the two drugs by using surrogate measures. Response surface models were

used to describe interaction between the two drugs. The response surfaces enable

us to predict the probability of being sedated and tolerating painful stimulus in a

patient belonging to a general patient population.

152

• The response surfaces were combined with pharmacokinetic models to determine

efficient and safe combinations of remifentanil and sevoflurane that could directly

applied to anesthetic practice. Optimal combinations were also prescribed for a

variety of anesthetic procedure lengths.

• Response surface models were used to describe the interaction between an

intravenous hypnotic drug (propofol) and an opioid (remifentanil). Data that was

previously reported in a manuscript was combined with data from a new set of

volunteers to create models that predict the probability of being sedated and

tolerating painful stimulus in a patient belonging to a general patient population.

These response surface models were combined with combined with

pharmacokinetic models to determine efficient and safe combinations of

remifentanil and propofol that could be used in anesthetic practice.

The second specific aim is addressed by Chapters 3 and 4. The results can be

summarized as follows:

• A volunteer study was conducted to determine the relation between the outputs of

real-time monitors that are surrogates of central nervous system effect, drug

concentration and the true state of sedation. The linearity of the monitor was

described qualitatively by using graphs and quantitatively by prediction

probabilities.

• Processed EEG parameters (e.g., BIS and AAI) were used to characterize the

interaction between a hypnotic drug (sevoflurane) and opioid (remifentanil).

Response surface models were used to suggest processed EEG targets that can be

associated with adequate sedation and analgesia.

153

• Movement and heart rate increase during surgery, often associated with

inadequate anesthesia, is compared to responses observed in processed EEG

parameters by means of a volunteer study. Specifically, the latency of the monitor

response and magnitude are reported. Responses in volunteers that were deeply

sedated were compared against those in volunteers who did not have adequate

sedation.

6.2 Conclusions

The conclusions that support the first specific aim are as follows:

• Response surface analyses demonstrate a synergistic interaction between opioids

(remifentanil) and hypnotic volatile anesthetics (sevoflurane) for sedation and all

analgesic endpoints. We found that the addition of remifentanil to sevoflurane

profoundly reduced the amount of sevoflurane needed to produce sedation and

analgesia.

• Pharmacodynamic models can accurately predict the analgesic and sedative

effects produced by the administration of hypnotic drugs and opioids. We found

that response surface models such as the logit model capture population

pharmacodynamics with high fidelity.

• It is possible to determine, through simulation, combinations of hypnotics and

opioids that provide clinically adequate anesthesia and result in the most rapid

emergence from anesthesia. We designed and implemented techniques that

combine the pharmacokinetic and pharmacodynamic models to identify strategic

combinations of opioids and hypnotic drugs that ensure adequate anesthesia while

simultaneously minimizing the time to emerge from the anesthetic procedure.

154

• Pharmacokinetic advantages of fast acting opioids (remifentanil) over intravenous

hypnotic drugs (propofol) would result in higher opioid concentrations being

targeted as the duration of the anesthetic increased.

The conclusions that support the second specific aim are as follows:

• Processed EEG monitors correlate well with hypnotic drug concentrations and

level of sedation when a single drug is used. We observed that BIS and AAI

monitors correlate well with the concentration of the hypnotic drug.

• Processed EEG monitors do not represent the level of sedation when two drugs

(opioid and hypnotic) are used to provide anesthesia. We observed that BIS and

AAI monitors are largely insensitive to the increase in sedative effect produced.

This may potentially lead to overdose in clinical practice if the drugs are titrated

by using processed EEG monitors. We address this limitation by suggesting target

processed EEG monitor indices that correlate with adequate analgesia and

sedation.

• Responses of under anesthetized patients to painful stimuli may be detected by

processed EEG monitors. We test the potential future application of processed

EEG monitors in detecting patient responses to stimulation when traditional

markers such as heart rate and movement are obscured by the presence of

vasoactive drugs or drugs that produce neuromuscular blockade.

6.3 Impact

The broad impact of the two specific aims is to improve the accuracy with which

anesthetic drugs are dosed while still maintaining a strict regard for the safety of the

155

patient. The clinical implications that are discussed at the end of chapters 2, 3, 4 and 5

may be summarized by the following statements.

• The presence of a modest amount of opioid may decrease the amount of hypnotic

drug needed to produce sedation by several fold.

• Model based drug dose recommendations will ensure balanced anesthesia intra-

operatively and speed up emergence post-operatively.

• The application of such dose recommendations will lead to greater patient safety

and reduced operating costs.

• Processed EEG monitors can improve drug delivery by measuring individual

patient response to drugs. This enables the identification of outlier patients

thereby minimizing the adverse events associated with over dose or under dose of

anesthetic drugs

• Several potential limitations of the monitors are identified. This enables clinicians

to better understand the operating characteristics of these monitors. This also

serves as guide to the future development of depth of anesthesia monitors.

6.4 Future Work

The current work examines a quantitative and empirical approach to improving

anesthetic drug delivery and clinical outcomes. Traditional anesthetic “recipes” are

gradually becoming obsolete as researchers show the benefit of an empirically derived,

model based approach to anesthesia. Some concepts that are part of this dissertation such

as response surface models seek to quantify anesthetic effect. This improves safety and

emergence by enabling precise titration of the drug that is targeted to reach the desired

level of effect rather than “more than adequate” approach to drug dosing. As improved

156

methods to quantify anesthetic effect become available and as interactions among the

entire plethora of drugs used in anesthetic practice are reported we face obvious

questions, such as, how do we practically utilize such multidimensional information to

improve clinical outcomes? One approach to this challenge that our laboratory as well as

other scientists have taken is to develop information displays that combine patient’s

physiologic data with population models in an intuitive manner. The secondary use of

quantifying anesthetic effect among combinations of drugs is in drug dose optimization.

Drug dose suggestions commonly accompany the packages in which drugs are dispensed.

They rarely contain recommendations on combined use with other drugs. The choice of

drug combination is often based on training and experience with the said combination.

Offline drug dose optimization combines desired positive clinical outcomes such as

adequate anesthesia, fast induction, fast emergence and minimum amount of drug use

with negative outcomes such as, respiratory depression, hyper(or hypo) variable cardio

vascular system, nausea and memory loss, to identify a drug dose and combination that

provides the maximum therapeutic benefit. Such an optimization technique can also

provide economic advantages by utilizing cheaper drugs (which often corresponds to

drugs with slower kinetics and greater side effects) strategically. Drug dose optimization

may offer the greatest advantage in countries where modern drugs are prohibitively

expensive or unavailable.

methods to improve anesthetic drug management by sandeep …

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