Published In

Modelling in Medicine and Biology VI

Document Type

Article

Publication Date

2005

Subjects

Brain damage -- Computer simulation, Intracranial pressure -- Measurement, Clinical trials -- Computer simulation

Abstract

We used a prospective clinical trial to generate physiologic data to create subject-specific in silico (computer simulation) models of intracranial pressure dynamics in children with severe traumatic brain injury. The trial included a physiologic challenge protocol with changes in head-of-bed elevation and minute ventilation, applied over multiple iterations to three subjects. Physiologic signals (electrocardiogram, respiration, arterial blood pressure, intracranial pressure [ICP], oxygen saturation) were recorded continuously, along with clinical annotations indicating the precise timing of physiologic challenges. Several parameters within the model of ICP dynamics were estimated for each subject based on the ICP response to the challenges. Estimation was done using a standard optimization algorithm to minimize the difference between the ICP trajectory predicted by the model and the actual ICP data. The ICP trajectory predicted by the model was similar to the actual ICP data in all cases, and the mean absolute error varied between 0.5 - 2.8 mmHg (mean = 1.4mmHg). These results demonstrate the potential for using clinically annotated prospective data to create subject-specific computer simulation models. Future research will focus on improvements in the logic for cerebral autoregulatory mechanisms and physiologic adaptation.

Description

This is the publishers final PDF of an article published in "Modelling in medicine and biology, VI" by Wit Press © 2005, pp 57-66 and is available online at http://library.witpress.com/pages/PaperInfo.asp?PaperID=15451

See additional files below for accompanying presentation.

DOI

10.2495/BIO050061

Persistent Identifier

http://archives.pdx.edu/ds/psu/9922

biomed2005.ppt (1056 kB)
Presentation - Estimation of Subject Specific ICP Dynamic Models Using Prospective Clinical Data

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