Optimization of Ventilator Treatment Using Model of Lung
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Transcript Optimization of Ventilator Treatment Using Model of Lung
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?