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Estimation of Subject Specific ICP Dynamic Models Using Prospective Clinical Data Biomedicine 2005, Bologna, Italy. W. Wakeland 1,2 , J. Fusion 1 , B. Goldstein 3 1 Systems Science Ph.D. Program, Portland State University, Portland, Oregon, USA - PowerPoint PPT Presentation
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1 ELECTRICAL & COMPUTERENGINEERING
BIOMEDICAL SIGNAL PROCESSING LABORATORYb s p . p d x . ed u
System Science
Ph.D. Program
Oregon Health & Science Univ.Complex Systems Laboratory
Estimation of Subject Specific ICP Dynamic Models Using Prospective
Clinical Data
Biomedicine 2005, Bologna, Italy
W. Wakeland 1,2, J. Fusion 1, B. Goldstein 3
1 Systems Science Ph.D. Program, Portland State University, Portland, Oregon, USA
2 Biomedical Signal Processing Laboratory, Department of Electrical and Computer Engineering, Portland State University, Portland, Oregon, USA
3 Complex Systems Laboratory, Doernbecher Children’s Hospital, Division of Pediatric Critical Care, Oregon Health & Science University, Portland,
Oregon, USA This work was supported in part by the Thrasher Research Fund
2ELECTRICAL & COMPUTERENGINEERING
BIOMEDICAL SIGNAL PROCESSING LABORATORYb s p . p d x . ed u
Oregon Health & Science Univ.Complex Systems Laboratory
System Science
Ph.D. Program
Aim
• To develop tools for improving care of children with severe traumatic brain injury (TBI) Help improve diagnosis and treatment of
elevated intracranial pressure (ICP) Improve long-term outcome following
severe TBI• One potential approach:
Create subject-specific computer models of ICP dynamics
Use models to evaluate therapeutic options
3ELECTRICAL & COMPUTERENGINEERING
BIOMEDICAL SIGNAL PROCESSING LABORATORYb s p . p d x . ed u
Oregon Health & Science Univ.Complex Systems Laboratory
System Science
Ph.D. Program
Motivation• TBI is the leading cause of death and
disability in children 150,000 pediatric brain injuries 7,000 deaths annually (50% of all childhood
deaths) 29,000 children with new, permanent
disabilities• Death rate for severe TBI (defined as a
Glasgow Coma Scale score < 8) remains between 30%-45% at major children's hospitals
• A recently published evidence-based medicine review reports that elevated ICP is a primary determinant of outcome following TBI
4ELECTRICAL & COMPUTERENGINEERING
BIOMEDICAL SIGNAL PROCESSING LABORATORYb s p . p d x . ed u
Oregon Health & Science Univ.Complex Systems Laboratory
System Science
Ph.D. Program
Background: Intracranial Pressure (ICP)
• TBI often causes ICP to increase Frequently due, at least initially, to
internal bleeding (hematoma) Elevated ICP is defined as > 20 mmHg
• Persistent elevated ICP reduced blood flow insufficient tissue perfusion (ischemia) secondary injury poor outcome
• Poor outcomes often occur despite the availability of many treatment options The pathophysiology is complex and
only partially understood
5ELECTRICAL & COMPUTERENGINEERING
BIOMEDICAL SIGNAL PROCESSING LABORATORYb s p . p d x . ed u
Oregon Health & Science Univ.Complex Systems Laboratory
System Science
Ph.D. Program
Background: Treatment Options
• Treatment options include, among many others: Draining cerebral spinal fluid (CSF) via
a ventriculostomy catheter Raising the head-of-bed (HOB)
elevation to 30 to promote jugular venous drainage
Inducing mild hyperventilation
6ELECTRICAL & COMPUTERENGINEERING
BIOMEDICAL SIGNAL PROCESSING LABORATORYb s p . p d x . ed u
Oregon Health & Science Univ.Complex Systems Laboratory
System Science
Ph.D. Program
Background: ICP Dynamic Modeling
• Many computer models of ICP have been developed over the past 30 years Models have sophisticated logic (differential
eqns.) Potentially very helpful in a clinical setting
• However, clinical impact of models has been minimal Complex models are difficult to understand
and use• Another issue is that clinical data often lack
the annotations needed to facilitate modeling Exact timing for medications, CSF drainage,
ventilator adjustments, etc.
7ELECTRICAL & COMPUTERENGINEERING
BIOMEDICAL SIGNAL PROCESSING LABORATORYb s p . p d x . ed u
Oregon Health & Science Univ.Complex Systems Laboratory
System Science
Ph.D. Program
Method: Research Approach
• Use an experiment protocol (next slide) to collect prospective clinical data Physiologic signals recorded continuously
electrocardiogram, respiration, arterial blood pressure, ICP, oxygen saturation
Plus annotations to indicate the precise timing of therapies and physiologic challenges
• Use collected data to create subject-specific computer models of ICP dynamics
• Use subject-specific models to predict patient response to treatment and challenges
8ELECTRICAL & COMPUTERENGINEERING
BIOMEDICAL SIGNAL PROCESSING LABORATORYb s p . p d x . ed u
Oregon Health & Science Univ.Complex Systems Laboratory
System Science
Ph.D. Program
Method: Experimental Protocol
• Mild physiologic challenges Applied over multiple iterations to three
subjects with severe traumatic brain injury • Change the angle of the head of the bed
(HOB) Randomly assigned, between 0º and 40º, in
10º increments, for 10 minute intervals • Change minute ventilation (or respiration
rate, RR) Clinician adjusts RR to achieve specified
ETCO2 target from [-3 to -4] mmHg to [+3 to +4] mmHg from baseline
9ELECTRICAL & COMPUTERENGINEERING
BIOMEDICAL SIGNAL PROCESSING LABORATORYb s p . p d x . ed u
Oregon Health & Science Univ.Complex Systems Laboratory
System Science
Ph.D. Program
Method: Model Estimation
NonlinearOptimizing Algorithm
InitialParameters ICP
DynamicModel
Estimated Parameters
HOB and RRChallenges
ErrorComputation
Predicted ICP
Measured ICP
Error
10ELECTRICAL & COMPUTERENGINEERING
BIOMEDICAL SIGNAL PROCESSING LABORATORYb s p . p d x . ed u
Oregon Health & Science Univ.Complex Systems Laboratory
System Science
Ph.D. Program
Method: Simulink ICP Dynamic Model
11ELECTRICAL & COMPUTERENGINEERING
BIOMEDICAL SIGNAL PROCESSING LABORATORYb s p . p d x . ed u
Oregon Health & Science Univ.Complex Systems Laboratory
System Science
Ph.D. Program
Method: Model, Core Logic
• The timing for physiologic challenges is a key input to the model
• The state variables are the volumes of each fluid compartment
• Key feedback loops Volume pressure flow volume ∑ (volumes) ICP pressures
flows ∑ (volumes) • Autoregulation is modeled by
changing arterial-to-capillary flow resistance [only]
12ELECTRICAL & COMPUTERENGINEERING
BIOMEDICAL SIGNAL PROCESSING LABORATORYb s p . p d x . ed u
Oregon Health & Science Univ.Complex Systems Laboratory
System Science
Ph.D. Program
Method: Model, Impact of Challenges
• Impact of RR on ICP
intracranial arterial pressure ↓intracranial venous pressure ↓
↑ө ICP↓
• Impact of HOB angle (ө) on ICP
ICP↓arterial blood
volume ↓
↑RR PaCO2 ↓ indicated blood flow ↓
arterial-to-capillary flow ↓
capillary resistance ↑
13ELECTRICAL & COMPUTERENGINEERING
BIOMEDICAL SIGNAL PROCESSING LABORATORYb s p . p d x . ed u
Oregon Health & Science Univ.Complex Systems Laboratory
System Science
Ph.D. Program
Method: Parameters Estimated
• Autoregulation factor• Basal cranial volume• CSF drainage rate• Hematoma increase rate pressure time constant • ETCO2 time constant• Smooth muscle “gain
constant”• Systemic venous pressure
14ELECTRICAL & COMPUTERENGINEERING
BIOMEDICAL SIGNAL PROCESSING LABORATORYb s p . p d x . ed u
Oregon Health & Science Univ.Complex Systems Laboratory
System Science
Ph.D. Program
Results: Patient 1, Session 4. A series of changes to HOB elevation
and RR
15ELECTRICAL & COMPUTERENGINEERING
BIOMEDICAL SIGNAL PROCESSING LABORATORYb s p . p d x . ed u
Oregon Health & Science Univ.Complex Systems Laboratory
System Science
Ph.D. Program
Results: Patient 2, Session 1. A series of changes to HOB elevation
16ELECTRICAL & COMPUTERENGINEERING
BIOMEDICAL SIGNAL PROCESSING LABORATORYb s p . p d x . ed u
Oregon Health & Science Univ.Complex Systems Laboratory
System Science
Ph.D. Program
Results: Patient 2, Session 4. A series of changes to RR
17ELECTRICAL & COMPUTERENGINEERING
BIOMEDICAL SIGNAL PROCESSING LABORATORYb s p . p d x . ed u
Oregon Health & Science Univ.Complex Systems Laboratory
System Science
Ph.D. Program
Results: Patient 2, Session 7. A series of changes to HOB elevation and RR
18ELECTRICAL & COMPUTERENGINEERING
BIOMEDICAL SIGNAL PROCESSING LABORATORYb s p . p d x . ed u
Oregon Health & Science Univ.Complex Systems Laboratory
System Science
Ph.D. Program
Results: Summary
19ELECTRICAL & COMPUTERENGINEERING
BIOMEDICAL SIGNAL PROCESSING LABORATORYb s p . p d x . ed u
Oregon Health & Science Univ.Complex Systems Laboratory
System Science
Ph.D. Program
Discussion: Model vs. Actual Response
• Model response to HOB changes was very similar to actual response (error < 1 mmHg)
• Response to RR changes did not fully reflect the patient’s actual response in all cases Error > 2 mmHg in many cases Revealed several model deficiencies
Lack of systemic adaptationDoes not capture interaction affectsIncorrect response to RR changes
20ELECTRICAL & COMPUTERENGINEERING
BIOMEDICAL SIGNAL PROCESSING LABORATORYb s p . p d x . ed u
Oregon Health & Science Univ.Complex Systems Laboratory
System Science
Ph.D. Program
Discussion: Model Deficiencies
• Systemic adaptation (make change; return to baseline) P2S7: When HOB moved from 30º to 0º; then back
to 30º, the ending in vivo ICP was lower than its starting point
In the model, ICP returned to its original value• Interaction of interventions
ICP impact depended on whether the interventions were temporally clustered or dispersed
Model did not capture these differences • Incorrect model response to RR changes
Changes in smooth muscle tone in the model affect the arterial-to-capillary blood flow resistance, but not [directly] the arterial volume
21ELECTRICAL & COMPUTERENGINEERING
BIOMEDICAL SIGNAL PROCESSING LABORATORYb s p . p d x . ed u
Oregon Health & Science Univ.Complex Systems Laboratory
System Science
Ph.D. Program
Discussion: Summary
• Model of ICP dynamics was calibrated to replicate the ICP recorded from specifics patient during an experimental protocol
• Results demonstrated the potential for using clinically annotated prospective data to create subject-specific computer simulation models
• Future research will focus on improving the logic for cerebral autoregulatory mechanisms and physiologic adaptation