1. W. Wakeland 1,2, J. Fusion 1, B. Goldstein 3
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

2. 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

3. 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

4. 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

5. 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

6. 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.

7. 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

8. 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

9. Method: Model Estimation
HOB and RR
Challenges
Initial
Parameters
Nonlinear
Optimizing Algorithm
ICP
Dynamic Model
Estimated Parameters
Error
Predicted ICP
Error
Computation
Measured ICP

10. Method: Simulink ICP Dynamic Model

11. 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]

12. Method: Model, Impact of Challenges
Impact of HOB angle (ө) on ICP
intracranial arterial pressure ↓
intracranial venous pressure ↓ ↑ө
ICP↓
Impact of RR on ICP
ICP↓
arterial blood volume ↓
↑RR
PaCO2 ↓
indicated blood flow ↓
arterial-to-capillary flow ↓
capillary resistance ↑

13. 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

14. Results: Patient 1, Session 4
Results: Patient 1, Session 4. A series of changes to HOB elevation and RR

15. Results: Patient 2, Session 1. A series of changes to HOB elevation

16. Results: Patient 2, Session 4. A series of changes to RR

17. Results: Patient 2, Session 7
Results: Patient 2, Session 7. A series of changes to HOB elevation and RR

18. Results: Summary

19. 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 adaptation
Does not capture interaction affects
Incorrect response to RR changes

20. 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

21. 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