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, Dept of Mechanical Engineering, Centre for Bio-Engineering
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
Model Basics
•
•
•
Collapsed
Peak
Volume
Abnormal
End Exp.
Volume
•
Tidal •
Volume
Clinical Tradeoff: Maximise gas exchange
and minimise risk of damage (e.g. tidal volume
and PEEP “within reason”)
Inspiretory Pressure
•
Volume
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
Healthy
PEEP
Pressure
Peak Pressure
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
More Detail
• Compartments with different superimposed pressure
• Lung units – cluster of alveoli and distal airways
Model
Number of Units
• 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
TOP
Pressure
Results
• True lung PV curve with associated threshold pressure
distributions
PEEP
• Unique distributions for different
levels of PEEP are found
Clinical Application
• Optimisation of ventilation
– Parameter identification = patient specific model
– Simulation to determine effect
of settings on PV curve
– Optimise ventilator settings
as desired
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
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
GUI
Lung
parameters
Resulting
PV curve
Alternative
settings
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
Any Questions?