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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

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

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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

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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

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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

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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

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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.

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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

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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

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Method: Model Estimation

NonlinearOptimizing Algorithm

InitialParameters ICP

DynamicModel

Estimated Parameters

HOB and RRChallenges

ErrorComputation

Predicted ICP

Measured ICP

Error

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Method: Simulink ICP Dynamic Model

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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]

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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 ↑

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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

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

and RR

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

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

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

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Results: Summary

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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

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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

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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