automation, in clinical anesthesia - cIRcle - University of British

266 Pages·2009·31.31 MB·English

AUTOMATION, IN CLINICAL ANESTHESIA
by
Stephane Bibian
Master of Applied Science, University of British Columbia, Vancouver, Canada, 1999
Bachelor of Science, E.S.I.E.E., Amiens, France, 1997
A thesis submitted in partial fulfillment of
the requirements for the degree of
DOCTORATE OF PHILOSOPHY
in
THE FACULTY OF GRADUATE STUDIES
(Electrical and Computer Engineering)
THE UNIVERSITY OF BRITISH COLUMBIA
July 2006
©Stephane Bibian, 2006
Abstract
This thesis investigates the design and performance of a controller for the maintenance of anesthesia during
surgery. The controller is designed to be robustly stable for a large population of patients.
Even though anesthetic drugs are amongst the most dangerous drugs used in today's clinical setting,
anesthesia procedures are known to be very safe. Hence, the impact of automation in anesthesia in terms of
patients' safety cannot be clearly established. However, there are a number of significant clinical advantages
to be gained by closing the loop:
1. Recent evidences suggest that most patients undergoing anesthesia procedures are overdosed. This is
one of the main reasons for patients' discomfort and slow recovery. Literature suggests that closed-
loop systems can significantly reduce drug consumption and lessen recovery times, thus improving the
patient outcome while reducing drug-associated costs and bed occupancy.
2. Anesthesiologists have access to intravenous agents with fast onset of action and fast metabolism.
Using a closed-loop controller would allow for an infusion-type titration that provides smoother tran
sitions, thus avoiding the respiratory and hemodynamic depression observed in a bolus-based manual
regimen.
3. Closed-loop controllers are also particularly well-suited for solving complex optimization problems.
The profound synergy that exists between intravenous anesthetics and opioids could then be fully
exploited. This could be a significant factor contributing to a reduction in drug usage and the
improvement of patients' comfort.
This project is particularly challenging. In particular:
1. There is no accepted measure of depth of anesthesia. Hence, it is necessary to work at the conceptual
and sensor levels in order to define adequate feedback measures.
2. Drug effect modeling suffers from many shortcomings. In particular, published studies are often not
in agreement regarding model parameters.
3. Uncertainty of dose/response models is daunting. Measuring this uncertainty is necessary in order to
ensure stability of the control design.
ii
ABSTRACT AND THESIS ORGANIZATION iii
While the anesthesia closed-loop concept has already been investigated in the past, no breakthrough has
yet been achieved. We feel it is necessary to investigate the anesthesia system from a control engineering
perspective.
This thesis is divided into two distinct parts. Part A contains the first 4 chapters and presents a
thorough introduction to clinical anesthesia. The main concepts, terminology and issues are covered,
including anesthesia monitors and basic pharmacology principles. A review of the prior closed-loop control
attempts is presented in Chapter 4.
Part B contains the chapters 5 to 8. In these chapters, we investigate a new sensor technology to quantify
both cortical and autonomic activity. This technology is used to derive drug effect models, from which
uncertainty bounds are derived. Based on this uncertainty analysis, we derive robustly stable controllers
achieving clinically adequate performances.
Finally, we invite the readers to refer to Chapter 9 for a complete synopsis and summary of this thesis.
Table of Contents
Abstract ii
Table of Contents iv
List of Tables ix
List of Figures xi
List of Acronyms xx
Acknowledgements xxii
Dedication xxiii
PART A: The Anesthesia Framework 1
1 Clinical Anesthesia: Terminology, Concepts, and Issues 2
1.1 Clinical Anesthesia: A Key Specialty in Critical Care 2
1.1.1 The Early Days 3
1.1.2 Risks and Outcome in Anesthesia 3
1.2 Modern Concepts 6
1.2.1 The Role of the Anesthesiologist 6
1.2.2 Functional Components of Clinical Anesthesia 7
1.2.3 Modern Balanced Anesthesia 8
1.3 An Extensive Pharmacopoeia 8
1.3.1 Anesthetics (i.e. hypnotics) 8
1.3.2 Opioids • 10
1.3.3 Neuromuscular Blocking Agents (NMBs) 10
1.3.4 Drugs: Action, Effect and Interaction 11
1.4 Conduct of Anesthesia 12
1.5 Drug Delivery and Monitoring in Anesthesia 14
1.5.1 Drug Delivery Devices 14
1.5.2 Anesthesia Monitors 15
1.6 Summary 16
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ABSTRACT AND THESIS ORGANIZATION v
2 Quantifying Depth of Anesthesia: a Review 17
2.1 The Use of Surrogate Measures 18
2.2 Quantifying the Depth of Hypnosis: a Review 19
2.2.1 Review of Tools and Techniques 19
2.2.2 Clinical Relevance, Interest and Potentials of the BIS Monitor 26
2.3 Quantifying the Depth of Analgesia 29
2.3.1 Using Physiological Measures 29
2.3.2 Using Heart Rate Variability 30
2.4 Summary 31
3 Modeling Anesthetic Drugs: the Traditional Approach 32
3.1 Pharmacokinetics 32
3.1.1 Principles and Concepts 33
3.1.2 Modeling 34
3.1.3 Literature Review 38
3.1.4 Concluding Remarks 40
3.2 Pharmacodynamics 42
3.2.1 The Dose/Response Relationship: a Steady-State Model 42
3.2.2 The ko Parameter: a Measure of the Effect Dynamics 45
e
3.2.3 Literature Review 46
3.2.4 Concluding Remarks 48
3.3 Summary 51
4 Closing the Loop in Anesthesia: a Review 53
4.1 Review 53
4.2 Summary 54
PART B: Sensing, Modeling, and Control 58
5 Quantifying Cortical and Autonomic Activity Using Wavelets 59
5.1 The Discrete Wavelet Transform 61
5.1.1 Standard Wavelet Dyadic Decomposition 61
5.1.2 Change in Phase and Wavelets 62
5.2 Estimating the Anesthetic Drug Effect: the WAVCNS 62
5.2.1 Concepts and Derivation 62
5.2.2 Practical Issues and Implementation 73
5.2.3 Clinical Results 76
ABSTRACT AND THESIS ORGANIZATION vi
5.3 Measuring the Autonomic Nervous System (A-NS) State: the WAVANS Index 82
5.3.1 The Use of Heart Rate Variability 82
5.3.2 The WAVANS 84
5.3.3 Implementation Issues 85
5.3.4 Case Reports 86
5.4 Dynamic Behavior 91
5.5 Summary 94
6 A System Oriented Approach to Pharmacodynamic modeling 95
6.1 A New System Oriented Approach to PD Modeling 95
6.1.1 Sheiner's Approach 96
6.1.2 Proposed Methodology : 101
6.2 Application to Propofol and WAVCNS Index 103
6.2.1 Clinical Data 103
6.2.2 PD Identification - Traditional Approach 104
6.2.3 PD Identification - New Approach 105
6.2.4 Adequacy of the Identification Data 107
6.2.5 Identification Results 107
6.2.6 Model Validation 109
6.2.7 Nominal Propofol PD Models 112
6.3 Discussion 114
6.3.1 Clinical Adequacy of the Identification Data 114
6.3.2 System Oriented vs. Traditional Approach 116
6.3.3 Dose vs. Response Characteristics 120
6.3.4 Frequency Response Characteristic 121
6.4 Summary 123
7 Managing PKPD Uncertainty 126
7.1 Quantifying Uncertainty 126
7.1.1 The Relative Uncertainty Framework 128
7.1.2 Selection of a Near Optimal Nominal Model 128
7.2 Application to Propofol PKPD Models 134
7.2.1 Origins of PKPD Uncertainty 134
7.2.2 Propofol PKPD Uncertainty Results 138
7.3 Reducing Uncertainty 141
7.3.1 Passive Methods 141
7.3.2 Total and Partial Self Tuning , 144
7.4 Summary 149
ABSTRACT AND THESIS ORGANIZATION vii
8 SISO Control Design 155
8.1 Patient Simulator 156
8.1.1 Simulator Structure 156
8.1.2 Noise Modeling 158
8.1.3 Patient Simulator Output 161
8.1.4 Tests Setup and Performance Requirements 161
8.2 PID Loop Shaping Design 164
8.2.1 First Design 164
8.2.2 Second Design 166
8.2.3 Third Design 166
8.3 Hoo Control Design 168
8.3.1 Hoo Design Principle 168
8.3.2 Implementation 170
8.3.3 Application to Anesthesia Control 170
8.4 Reducing the Uncertainty 172
8.4.1 Accounting for age 174
8.4.2 Identifying the PK time delay 174
8.5 The Clinicians' Point of View 176
8.5.1 Increased Controller Gain 178
8.5.2 Use of a Smith Predictor Structure 179
8.5.3 Faster Sensing Dynamics 182
8.5.4 Improved Performance: Important Trade-offs 183
8.6 Summary 184
9 Conclusion, Contributions and Recommendations 187
9.1 Significance 187
9.2 Synopsis and Contributions 188
9.2.1 Synopsis 189
9.2.2 Contributions and Implications 190
9.3 Future Work 192
Bibliography 197
ABSTRACT AND THESIS ORGANIZATION viii
A Pharmacopoeia 209
A.l Vapour Anesthetics 209
A.2 Intravenous Anesthetics 209
A. 3 Opioids 210
B Propofol and Remifentanil PKPD 213
B. l Propofol 213
B.l.l Pharmacokinetics 213
B.1.2 Pharmacodynamics 216
B.2 Remifentanil 219
B.2.1 Pharmacokinetics 219
B.2.2 Pharmacodynamics 220
C Propofol PKPD Nominal and Uncertainty Models 222
D Clinical Studies 229
D.l Arthroscopy Observational Study 229
D.l.l Protocol 229
D.l.2 Results 230
D.l.3 Comments and Unexpected Results 230
D.2 Laryngeal Mask Airway Study 230
D.2.1 Protocol 232
D.2.2 LMA Results 235
D.3 Electro-Convulsive Shock Therapy Study 236
E Survey 242
List of Tables
1.1 Contemporary anesthetic mortality rates (adapted from Brown [5]) 5
3.1 Propofol PK parameter sets from [101] where BW stands for body weight, ven= 1 for venous sampling,
ven= 0 for arterial sampling, bol= 1 for bolus administration, and bol= 0 for infusion administration. 39
3.2 Parameter estimates from the NONMEM analysis ([101]) 39
3.3 Pharmacokinetic parameters of remifentanil as a function of the age and lean body mass (from [103]). 40
4.1 Prior Art: a Literature Survey (Part 1) 55
4.2 Prior Art: a Literature Survey (Part If) 56
6.1 PD models obtained from the proposed approach with E =0 and Eo=100 108
ma3:
6.2 Population-normed PD models with age as a linear covariate 114
6.3 Goodness of fit of patient-specific models obtained from the traditional PD approach. 118
6.4 Goodness of fit of patient-specific models obtained from the system-oriented PD approach 118
6.5 Goodness of fit of population-normed models obtained from the traditional PD approach 119
6.6 Goodness of fit of population-normed models obtained from the system-oriented PD approach. . . . 119
7.1 Propofol PKPD Inter-patient Variability 135
7.2 ECT Intra- and Inter-patient Variability 137
8.1 Control Performance Requirements 163
8.2 PID Controller Characteristics 165
8.3 Control Performance - PID Loop Shaping Design 167
8.4 Hoo Controller Characteristics 171
8.5 Control Performance - Hoc Control Design 172
8.6 HQO Controller Characteristics 174
8.7 Control Performance - HQO Control Design based on Age 175
8.8 Control Performance Requirements - Clinicians' Point of View 177
8.9 HQO Controller Characteristics with Increased Gain 179
8.10 Control Performance - Improved HQO Control Designs 180
ix
LIST OF TABLES x
B.l Propofol PK parameter sets (hybrid form) 214
B.2 Propofol effect site half-life and pharmacodynamic parametric constants 217
B.3 Remifentanil PK parameter sets (hybrid form) 219
B. 4 Remifentanil effect site half-life and pharmacodynamic parametric constants 221
C. l PKPD Propofol nominal models - part I 223
C.2 PKPD Propofol nominal models - part II 224
C.3 PKPD Propofol nominal models - part III 225
C.4 PKPD Propofol uncertainty weights - part I 226
C.5 PKPD Propofol uncertainty weights - part II 227
C. 6 PKPD Propofol uncertainty weights - part III 228
D. l Patients' data and titration. Doses are expressed in \ig (sufentanil, fentanyl, remifentanil) or mg
(propofol) 231
D.2 LMA Study population (patients #001 to #049) 233
D.3 LMA Study population (patients #050 to #076) 234
D.4 LMA study population - demographics and dosage summary 235
D.5 ECT Study Population 238
D.6 Thiopental pharmacokinetic models 240
D.7 Thiopental identified pharmacodynamic models 241