Optimising Ventilation Using a Simple Model of Ventilated ARDS Lung Geoffrey M Shaw1, J. Geoffrey Chase2, Toshinori Yuta2, Beverley Horn2 and Christopher E Hann2 1Univ of Otago, Christchurch School of Medicine and Health Sciences 2 Univ of Canterbury,

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Optimising Ventilation Using a Simple Model of
Ventilated ARDS LungGeoffrey M Shaw1, J.
Geoffrey Chase2, Toshinori Yuta2, Beverley Horn2
and Christopher E Hann21Univ of Otago,
Christchurch School of Medicine and Health
Sciences2 Univ of Canterbury, Dept of Mechanical
Engineering, Centre for Bio-Engineering
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Introduction

• Mechanical ventilation is a bread and butter
  therapy in critical care
• It is well known that a properly or well
  ventilated patient has an increased likelihood of
  improved outcome
• However, selecting optimal settings, such as PEEP
  and tidal volume are difficult
• Especially, as these settings can change
  regularly as patient condition evolves,
  particularly in ARDS
• Hence, a method of monitoring and capturing these
  changes and then optimising ventilation would
  offer significant clinical benefit.
• ? Models offer the opportunity to both monitor
  and optimise ventilated patient status for better
  outcomes

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Model Basics

• Goal capture critically ill patient behaviour
• Healthy region is kept inflated under PEEP
• Most of volume change occurs in abnormal region
• Recruitment and Derecruitment (R/D) is the
  fundamental mechanism of volume change
• Clinical Tradeoff Maximise gas exchange and
  minimise risk of damage (e.g. tidal volume and
  PEEP within reason)
• Requirement Simple model to determine the
  recruitment status of a patient and thus the
  pressure, volume changes for various PEEP and
  tidal volume settings/choices

Collapsed
Peak Volume
Tidal Volume
Abnormal
End Exp. Volume
Inspiretory Pressure
Healthy
Volume
PEEP
Peak Pressure
Pressure
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More Detail

• Compartments with different superimposed pressure
• Lung units cluster of alveoli and distal airways

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Model

• Recruitment is described by Threshold Opening
  Pressure (TOP)
• Derecruitment is described by Threshold Closing
  Pressure (TCP)

• Skewed normal distribution
• Unique to a patient and condition

TCP
Number of Units
TOP
Pressure
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Results

• True lung PV curve with associated threshold
  pressure distributions

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PEEP

• Unique distributions for different levels of PEEP
  are found

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Clinical Application

• Optimisation of ventilation
• Parameter identification patient specific model
• Simulation to determine effectof settings on PV
  curve
• Optimise ventilator settingsas desired

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Clinical Application

• Optimisation of ventilator treatment
• Reduces recovery time
• Detect over-inflation
• Up-to-minute condition specific result
• Result immediately applicable
• Unique to patient and condition
• Provides continuous patient monitoring
• Simple GUI based system could be readily put on a
  PDA

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Clinical Application

• Data requirements
• Pressure and flow (volume) data at different PEEP
  values (2 minimum, 3 preferred current and /-
  2-5 cmH2O
• Data acquisition
• Obtain data directly from ventilator
• Patient kept on ventilator
• No additional tests, i.e. CT, MRI
• Fully/Semi automatic data acquisition,
  simulation, and analysis
• Similar data can be used for full validation study

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GUI
ResultingPV curve
Lungparameters
Alternativesettings
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Summary

• Simplified model of mechanics captures
  fundamental characteristics
• Shows a potential to be a clinical tool to
• Estimate and track state of disease
• Provide continuous monitoring
• Provide objective optimal ventilator settings
• Minimum interference to the patient and staff

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Any Questions?