Optimal PEEP – the final solution
(Model-Based Mechanical Ventilation for Intensive Care)
Geoffrey M Shaw1
J.Geoffrey Chase2
Chiew Yeong Shiong2
Nor Salwa Damanhuri2
Erwin van Drunen2
1Intensive
Care, Christchurch Hospital, New Zealand
2Mechanical Engineering, University of Canterbury, New Zealand
Department of Intensive Care
Melbourne
Christchurch
Department of Intensive Care
Universities of Canterbury, Otago, and
Christchurch Hospital
Department of Intensive Care
Presentation Outline
• Acute Lung Injury (ALI) and Acute Respiratory
Distress Syndrome (ARDS) in Intensive Care
Unit (ICU)
• Treatment for ALI and ARDS (Mechanical
Ventilation)
• Model-Based Mechanical Ventilation
– Minimal Model
– Elastance Model
• Results and Discussion
• Conclusion and Future Work
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Acute Respiratory Distress Syndrome
•
A syndrome of acute onset of
respiratory failure with findings of
bilateral infiltrates on chest
radiograph, a partial pressure of
arterial oxygen to fraction of inspired
oxygen ratio (PaO2/FiO2) less than
300 (ARDS if less than 200mmHg)
and the absence of elevated left heart
filling pressure determined either
diagnostically with a pulmonary artery
catheter (pulmonary artery occlusion
pressure of < 18mmHg) or clinically
(absences of evidence of left arterial
hypertension)
•
1.3 to 22 per 100,000 (ALI: 17.9 to
34 per 100,000)
•
Mortality is up to 70%
Ware et al. (2000). The acute respiratory distress syndrome
Gattinoni, L. & Pesenti, A. (2005). The concept of "baby
lung“
Ferguson, N. D. et al(2005). Airway pressures, tidal
volumes, and mortality in patients with acute respiratory
distress syndrome.
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Treatment for ALI/ ARDS
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Model-based Mechanical ventilation
• Mechanical ventilation (MV) is the primary form of
support for ALI/ARDS patients
• However, due to intra- and inter- patient-variability
reduce the efficacy of general protocols
• Computer modelling can be used to identify and
characterise patient-specific pulmonary mechanics and
guide clinical decisions
• 2 Model-based Methods
– Minimal Model
– Lung Elastance Monitoring
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Model-based Mechanical
Ventilation Part 1
A Minimal Model of Lung Mechanics
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Minimal Model – Recruitment
• Traditional Theory
– Isotropic Balloon like
expansion followed by overstretching
• Recruitment Theory
– Alveoli open or collapse
– Recruitment continues
throughout the cycle
– Once recruited – no
significant volume change
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Minimal Model – Recruitment
Alveoli do not behave like balloons
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Threshold Opening Pressure/
Threshold Closing Pressure
•
Threshold Opening Pressure (TOP) – Clinical Pressure when Alveoli Opens
•
Threshold Closing Pressure (TCP) – Clinical Pressure when Alveoli Collapse
•
TOP > TCP
Crotti, et al. (2001). Recruitment and derecruitment during acute respiratory failure: a clinical study
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Minimal Model Development
• Based on the following Concepts:
– Lung is modelled as a collection of lung
units
– Either Recruited or Collapsed
– The state of every unit is governed by TOP
and TCP
– The TOP and TCP for every lung unit
assumed normally distributed.
*allowing fitting of a Gaussian
Distribution Curve: Mean and
Standard Deviation
Original data sourced form:
BERSTEN, A. D. 1998. Measurement of overinflation
by multiple linear regression analysis in patients
with acute lung injury. Eur Respir J, 12, 526-532.
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Model Basics
Mean Threshold Opening
Pressure
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Standard Deviation of the
distribution
Clinical Model Validation
•
MEAN FITTING ERROR
–
–
•
•
1.62% - Inflation
4.42% - Deflation
Capable of capturing
patients fundamental
lung mechanic
Model TOP, TCP and SD
Original data sourced form:
BERSTEN, A. D. 1998. Measurement of overinflation
by multiple linear regression analysis in patients
with acute lung injury. Eur Respir J, 12, 526-532.
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Model - Application
• PEEP selection based on TOP and TCP concept
– TOP – How much pressure required to open the Lung units
– TCP – Maintain Recruitment
• Can this give us insight about the disease process?
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Change in TOP or SD
• Monitor the Change of TOP or Standard Deviation
• Potential to group Patients based on TOP and SD
information
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Disease State Grouping (DSG)
How does the
lung recover?
ALI
ARDS
Normal
H1N1
How does lung
injury progress?
•
•
•
A metric to classify patients disease state.
Potential to guide MV treatment based on
patient’s condition
Theoretical – Warrant investigation on TOP and
SD relation with known patient’s disease state
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Disease State Grouping (DSG)
E.g. “Bad cold”
vs
Bird/Swine flu
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Possible Examples in DSG
H1N1
Healthy
Beginning of
ALI
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ARDS
Example - Clinical
• Case Study 1
– 59y Male (survived)
– Pneumonia, COPD
• Day 0 (PEEP = 12cmH2O)
–
–
–
–
Auto PEEP = 14cmH2O
PaO2 = 114
FiO2 = 0.4
Average Mean TOP = 45cmH2O
• Day 3 (PEEP = 12cmH2O)
–
–
–
–
Auto PEEP = 8cmH2O
PaO2 = 80
FiO2 = 0.4
No significant changes in
Standard deviation - The lung
state remains unchanged.
– Mean TOP drop with time – The
patients lung became less stiff
compared to earlier.
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SUNDARESAN, A., CHASE, J., SHAW, G., CHIEW, Y. S. &
DESAIVE, T. 2011. Model-based optimal PEEP in
mechanically ventilated ARDS patients in the Intensive
Care Unit. BioMedical Engineering OnLine, 10, 64.
• Case Study 2
– 69y Male (Deceased)
– Intra-abdominal sepsis
• Day 0 (PEEP = 15cmH2O)
– Auto PEEP = 11cmH2O
– PaO2 = 126
– FiO2 = 0.7
• Day 7 (PEEP = 12.5cmH2O)
– Auto PEEP = 2.3cmH2O
– PaO2 = 98
– FiO2 = 0.35
• Day 14 (PEEP =10cmH2O)
*
–
–
–
–
–
Auto PEEP = 1.6cmH2O
PaO2 = 93
FiO2 = 0.4
TOP drops  Lung is less stiff
But SD increases meaning more
lung (alveoli) are injured.
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SUNDARESAN, A., CHASE, J., SHAW, G., CHIEW, Y. S. &
DESAIVE, T. 2011. Model-based optimal PEEP in
mechanically ventilated ARDS patients in the Intensive
Care Unit. BioMedical Engineering OnLine, 10, 64.
Model-based Mechanical
Ventilation Part 2
Continuously Monitoring Lung
Elastance to Guide Mechanical
Ventilation PEEP
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Lung Elastance Monitoring
Respiratory System
Equation of Motion
V(t)
Paw = Ers.V + Rrs.Q + P0
•
•
•
•
•
•
Paw
Ers
V
Rrs
Q
P0
-
Airway Pressure
Respiratory Elastance
Volume
Airway Resistance
Flow
Offset Pressure (PEEP)
BATES, J. H. T. 2009. Lung Mechanics: An Inverse
Modelling Approach, Cambridge University
Press.
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Ers
 P(t)
Rrs
Q(t)
What if... Respiratory System Elastance changes with Time
during each volume increase?
Paw (t) = Edrs (t).V(t) +Rrs.Q(t) + P0
Can we capture the lung condition with time?
•
•
•
Continuous Monitoring of Lung Elastance/ Dynamic Lung
Elastance and Resistance
Integral Based Method (Similar to Multiple Linear regression)
Monitoring the Elastance Trend may provide an opportunity to
optimise PEEP
SUAREZ-SIPMANN, F., BOHM, S. H., TUSMAN, G., PESCH, T., THAMM, O., REISSMANN, H., RESKE, A., MAGNUSSON, A. &
HEDENSTIERNA, G. 2007. Use of dynamic compliance for open lung positive end-expiratory pressure titration in an
experimental study. Crit Care Med, 35, 214 - 221.
CARVALHO, A., JANDRE, F., PINO, A., BOZZA, F., SALLUH, J., RODRIGUES, R., ASCOLI, F. & GIANNELLA-NETO, A. 2007.
Positive end-expiratory pressure at minimal respiratory elastance represents the best compromise between mechanical
stress and lung aeration in oleic acid induced lung injury. Critical Care, 11, R86.
LAMBERMONT, B., GHUYSEN, A., JANSSEN, N., MORIMONT, P., HARTSTEIN, G., GERARD, P. & D'ORIO, V. 2008. Comparison
of functional residual capacity and static compliance of the respiratory system during a positive end-expiratory pressure
(PEEP) ramp procedure in an experimental model of acute respiratory distress syndrome. Critical Care, 12, R91.
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Concept of Minimal Elastance
•
During each breathing cycle, as PEEP rises, respiratory
elastance (Ers) may fall as new lung volume is recruited faster
than pressure can build up in the lung. This indicates
recruitability
•
If there is little or no recruitment, Ers rises with PEEP
indicating that inspiratory pressure was unable to recruit
significant new lung volume and now the pressure is, instead,
beginning to stretch already recruited lung
•
Hence, recruitment and potential lung injury can be balanced
by selecting PEEP at minimum Ers
•
Compared to a single, constant Ers value at each PEEP,
identifying time-variant Edrs allows this change to be seen
dynamically within each breath as pressure increases thus
allowing a more detailed view of patient’s lung physiology.
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Model-based Mechanical
Ventilation Part 2
Clinical Trials for Proof of concept
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Clinical Protocol
• Patients underwent a protocol-based step-wise incremental
PEEP recruitment manoeuvre (RM) using SIMV (Vt =500 ml)
The ETT cuff pressure was inflated to ~60 cmH2O to ensure
there was no leakage so changes in FRC could be measured
• Baseline measurements were taken, then PEEP was
decreased to ZEEP or reduced to a “safe” clinical level as
determined by the PI)
• During the RM, PEEP was increased using 5 cmH2O steps
until peak airway pressure reached at least 45 cmH2O. Other
settings were maintained throughout the RM.
• Each PEEP level was maintained for 10~15 breaths until
stabilisation before increasing to a higher PEEP level.
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Patients Recruited in CHC Hospital
• A total of 10 patients have been included in the
1st phase of the trial. (Still recruiting more)
Patients
Sex
1
2
3
4
5
6
7
8
9
10
F
M
M
M
M
M
M
F
M
M
Age
(year)
61
22
55
88
59
69
56
54
37
56
Clinical Diagnostic
Peritonitis, COPD
Trauma
Aspiration
Pneumonia, COPD
Pneumonia, COPD, CHF
Intra-abdominal sepsis, MOF
Legionnaires
Aspiration
H1N1, COPD*
Legionnaires, COPD*
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P/F Ratio
(mmHg)
209
170
223
165
285
280
265
303
193
237
FiO2
0.35
0.50
0.35
0.40
0.40
0.35
0.55
0.40
0.40
0.35
Patient 6 (Trauma)
•
•
•
•
•
As PEEP Increases,
Respiratory System
Elastance drop until
minimal before rising
Minimal Elastance
(Maximum Compliance)
was observed at PEEP
15cmH2O
The inflection line is
identified as +5~10 %
above minimal Elastance.
Selecting PEEP at
Minimum Elastance
(Maximum Compliance) is
not a new concept.
Relatively few clinical
trials have been carried
out.
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Example – Variable PEEP with
Respiratory System Elastance
Pt 2:
(Trauma)
Pt 6:
(Intra-abdominal
sepsis, CHF)
Minimal Elastance
PEEP
= 15cmH2O
Minimal Elastance
PEEP
= 15cmH2O
Inflection PEEP
= 6~9cmH2O
Inflection PEEP
= 7.5~10cmH2O
Pt 8:
(Aspiration)
Pt 10:
(Legionnaires, COPD)
Minimal Elastance
PEEP
= 25cmH2O
Minimal Elastance
PEEP
= 20cmH2O
Inflection PEEP
= 12~18cmH2O
Inflection PEEP
= 12~15cmH2O
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Patient 6 (Intra-abdominal sepsis, CHF)
•
•
•
•
•
Using Edrs with time, it is
possible to identify the
change of Respiratory
Elastance within a
breathing cycle
A drop in Edrs will indicate
the recruitment over
pressure build up.
An increase will suggest
recruitment.
The Respiratory system
compliance within each
breath can be monitored
Edrs potentially provides
higher resolution in
monitoring the patients
breathing condition
compared to a single
Elastance value within a
breath
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New Concept - Variable PEEP with Edrs
Pt 2: (Trauma)
Pt 6: (Trauma)
Pt 8: (Aspiration)
Pt 10:
Legionnaires,
COPD
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Example: Monitoring Edrs with time
Pressure and Edrs
40
30
20
10
0
10
15
20
25
Time (seconds)
Measured Pressure (Blue Line)
Model Pressure Fitting (Black Dots)
Edrs within a breath (Red Line)
What happens to Ers if PEEP Changes?
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30
Comparing Lower and Higher PEEP in a
Patient
•
Pressure and Edrs
40
•
30
20
Ventilated at
Lower PEEP
Edrs within a
breath drops,
suggesting
recruitment
10
0
10
15
20
25
30
Time (seconds)
Pressure and Edrs
40
•
30
•
20
10
Ventilated at
Higher PEEP
Edrs within a
breath increases,
suggesting over
distension
0
10
15
20
25
30
Time (seconds)
BERSTEN, A. D. 1998. Measurement of overinflation
by multiple linear regression analysis in patients
with acute lung injury. Eur Respir J, 12, 526-532.
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Animal Trials in Belgium
• Animal Trials have been carried out to investigate the
performance of the models.
• Healthy anesthetised piglet was ventilated with fixed
tidal volume using Engström CareStation (Datex, General
Electric, Finland).
• ARDS was induced using oleic acid.
• Subject’s arterial blood gases were sampled to monitor
the development of ARDS.
• Elastance (Ers, Edrs), and resistance (Rrs)
using integral based method
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Use of Electrical Impedance Tomography to compare
with our findings
• Electric Impedance
Tomography
(Collaborations)
Zhao, Z., D. Steinmann, et al. (2010). "PEEP titration
guided by ventilation homogeneity: a feasibility
study using electrical impedance tomography."
Crit Care 14: R8.
• Minimal Model
Sundaresan, A., T. Yuta, et al. (2009). "A minimal
model of lung mechanics and model-based
markers for optimizing ventilator treatment in
ARDS patients." Computer Methods and
Programs in Biomedicine 95(2): 166-180.
• Elastance Model
Chiew, YS, Chase, JG, Shaw, GM, and Sundaresan, A
and Desaive, T, Model-Based PEEP
Optimization for Mechanically Ventilated ARDS
Patients, BioMedical Engineering Online 2011.
• Cross comparison and
validation
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Animal trial results (unpublished)
Elastance
150
100
Integral Based Constant Elung
Integral based Median Edrs
Multiple Linear Regression Edrs
Vent Dynamic Elastance
Vent Static Elastance
50
0
20
40
60
80
100
120
140
160
180
Time (minutes)
•
•
•
•
•
Blue Line
Black Line
Green Line
Red Line
Pink Line
inspiratory pause
- Integral Based Constant Ers.
- Integral Based Median Edrs.
- Multiple Linear Regression Median Edrs.
- Ventilator Dynamic Elastance (∆P/Vt).
- Ventilator Static Elastance ((Pend insp – P0)/Vt) – The ventilator has an
allowing estimation of ‘Static Elastance’.
*Blue, Black and Green Line Overlaps each other
Computer Methods estimating Ers was able to reproduce the findings in ventilator.
Change in Elastance was observed with the development of ARDS
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200
Conclusion and Future Work
• The initial clinical trials indicate that the minimal model
and respiratory elastance monitoring may be able to
assist in the clinical decision for optimizing MV
– Minimal Model – There is insufficient clinical data to determined
the Disease Sate Groups. (What value is high TOP/SD?)
– Minimal Elastance Selection – Proof of concept that warrants
further investigation
• More trials to validate the performance of the model
– ARDS Animal model – University of liege, Belgium (June 2012)
– Clinical trials open for recruitment (On going)
• Bedside and real time application (In progress)
– Tablet +Software and Ventilator interface development
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Research Collaborations?
•
Main Collaboration on MV Research
–
–
–
•
Other Collaborations
–
–
–
–
•
Cardiovascular Research Center, University of Liege, Liege, Belgium
Intensive Care Unit, CHU Sart-Tilman, Liege, Belgium
Institute for Technical Medicine, Furtwangen University, Germany
Intensive care and burn unit, University Hospital of Lausanne, Lausanne, Switzerland
St-Luc University Hospital, Intensive care unit, Brussels, Belgium
Intensive Care Unit, Clinique Notre Dame de Grâce, Gosselies, Belgium
Intensive care unit, University Hospital of Geneva, Geneva, Switzerland
Prospective Collaborations?
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Department of Intensive Care
Thank you!
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Supplementary material
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Identifying Ers, Rrs and Edrs
• Multiple Linear Regression (MLR)
– Solving a Matrix
• Integral Based Method - Similar to MLR
– Instead of using data points of a curve, it uses the area
under the curve
– More information and more robust to noise
• Paw = Ers.V + Rrs.Q + P0
– We can identify Ers, and Rrs
• Using this Ers and Rrs from previous Equation
• Paw (t) = Edrs (t).V(t) +Rrs.Q(t) + P0 can be solved.
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Publications
•
Sundaresan, A., T. Yuta, et al. (2009). "A minimal model of lung mechanics
and model-based markers for optimizing ventilator treatment in ARDS
patients." Computer Methods and Programs in Biomedicine 95(2): 166-180.
•
Chiew, YS, Chase, JG, Shaw, GM, and Sundaresan, A and Desaive, T, ModelBased PEEP Optimization for Mechanically Ventilated ARDS Patients,
BioMedical Engineering Online 2011.
•
Sundaresan, A., J. Geoffrey Chase, et al. (2011). "Dynamic functional
residual capacity can be estimated using a stress-strain approach."
Computer Methods and Programs in Biomedicine 101(2): 135-143.
•
Sundaresan, A, Chase, JG, Shaw, GM, Chiew, YS and Desaive, T, ModelBased Optimal PEEP in Mechanically Ventilated ARDS Patients in the
Intensive Care Unit, BioMedical Engineering Online 2011, 10:64.
•
Chiew, YS, Desaive, T, Lambermont, B, Janssen, N, Shaw, GM, Schranz, C,
Moeller, K and Chase, JG (2012), “Physiological relevance of a minimal
model in Healthy Pigs Lung”, 8th IFAC Symposium on Biological and Medical
Systems, Budapest, Hungary. (In-Review) (Invited Paper)
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Publications
•
Mishra, A, Chiew, YS, Shaw, GM, and Chase, JG (2012), “Model-Based
Approach to Estimate dFRC in the ICU Using Measured Lung
Dynamics”, 8th IFAC Symposium on Biological and Medical Systems,
Budapest, Hungary. (In-Review)
•
Chiew, YS, Desaive, T, Lambermont, B, Janssen, N, Shaw GM and Chase,
JG (2012), “Performance of lung recruitment model in healthy
anesthetized pigs”, 2012 World Congress of Medical Physics and
Biomedical Engineering, Beijing, China, May 26-31, 1-page. (Accepted)
•
Chiew, YS, Chase, JG, Shaw, GM and Desaive T (2012), “Respiratory
system elastance monitoring during PEEP titration”, 32th International
Symposium of Intensive Care and Emergency Medicine (ISICEM),
Brussels, Belgium, March 20-23, 1-page. (Poster Presentation)
•
Sundaresan, A., Shaw, G. M., Chiew, Y.S. and Chase, J.G., PEEP in
mechanically ventilated patients: a clinical proof of concept, AustraliaNew Zealand Intensive Care Society (ANZICS) ASM, Taupo, New Zealand,
March 31 – April 1, 1-page, (2011).
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