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 Sciences2 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• 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

Pressure

Vol

ume

PEEP

Healthy

Abnormal

CollapsedPeak

Volume

Peak Pressure

Inspiretory Pressure

TidalVolume

End Exp.Volume

More Detail

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

Model

• Skewed normal distribution• Unique to a patient and condition

• Recruitment is described byThreshold Opening Pressure (TOP)

• Derecruitment is described byThreshold Closing Pressure (TCP)

TOP

TCP

Pressure

Num

ber

of U

nits

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

Lungparameters

ResultingPV curve

Alternativesettings

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?