1. 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

2. Melbourne
Christchurch

3. Universities of Canterbury, Otago, and Christchurch Hospital

4. 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

5. 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.

6. Treatment for ALI/ ARDS

7. 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

8. Model-based Mechanical Ventilation Part 1
A Minimal Model of Lung Mechanics

9. Minimal Model – Recruitment
Traditional Theory
Isotropic Balloon like expansion followed by over-stretching
Recruitment Theory
Alveoli open or collapse
Recruitment continues throughout the cycle
Once recruited – no significant volume change

10. Minimal Model – Recruitment
Alveoli do not behave like balloons

11. 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

12. 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 Measurement of overinflation by multiple linear regression analysis in patients with acute lung injury. Eur Respir J, 12,

13. Model Basics Standard Deviation of the distribution
Mean Threshold Opening Pressure
Standard Deviation of the distribution

14. 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 Measurement of overinflation by multiple linear regression analysis in patients with acute lung injury. Eur Respir J, 12,

15. 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?

16. Change in TOP or SD Monitor the Change of TOP or Standard Deviation
Potential to group Patients based on TOP and SD information
OPTIMAL PEEP
Ten patients
SD higher for TOP than TCP (expected)
Linear increase in TCP and drop in TOP as function of PEEP.
Model unmodified from prior section
Auto-PEEP excluded 16

17. 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

18. Disease State Grouping (DSG)
E.g. “Bad cold” vs
Bird/Swine flu

19. Possible Examples in DSG
H1N1
ARDS
Healthy
Beginning of ALI

20. Example - Clinical Case Study 1 Day 0 (PEEP = 12cmH2O)
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
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.
SUNDARESAN, A., CHASE, J., SHAW, G., CHIEW, Y. S. & DESAIVE, T Model-based optimal PEEP in mechanically ventilated ARDS patients in the Intensive Care Unit. BioMedical Engineering OnLine, 10, 64.

21. Case Study 2 Day 0 (PEEP = 15cmH2O) Day 7 (PEEP = 12.5cmH2O)
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. *
SUNDARESAN, A., CHASE, J., SHAW, G., CHIEW, Y. S. & DESAIVE, T Model-based optimal PEEP in mechanically ventilated ARDS patients in the Intensive Care Unit. BioMedical Engineering OnLine, 10, 64.

22. Model-based Mechanical Ventilation Part 2
Continuously Monitoring Lung Elastance to Guide Mechanical Ventilation PEEP

23. Lung Elastance Monitoring
Respiratory System Equation of Motion
Paw = Ers.V + Rrs.Q + P0
Paw - Airway Pressure
Ers - Respiratory Elastance
V - Volume
Rrs - Airway Resistance
Q - Flow
P0 - Offset Pressure (PEEP)
BATES, J. H. T Lung Mechanics: An Inverse Modelling Approach, Cambridge University Press.
Q(t)
Rrs
P(t)
Ers
V(t)

24. Paw (t) = Edrs (t).V(t) +Rrs.Q(t) + P0
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 Use of dynamic compliance for open lung positive end-expiratory pressure titration in an experimental study. Crit Care Med, 35,
CARVALHO, A., JANDRE, F., PINO, A., BOZZA, F., SALLUH, J., RODRIGUES, R., ASCOLI, F. & GIANNELLA-NETO, A 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 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.

25. 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.

26. Model-based Mechanical Ventilation Part 2
Clinical Trials for Proof of concept

27. 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.

28. 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
Age (year)
Clinical Diagnostic
P/F Ratio
(mmHg)
FiO2 1 F 61
Peritonitis, COPD
209
0.35 2 M 22
Trauma
170
0.50 3 55
Aspiration
223 4 88
Pneumonia, COPD
165
0.40 5 59
Pneumonia, COPD, CHF
285 6 69
Intra-abdominal sepsis, MOF
280 7 56
Legionnaires
265
0.55 8 54
303 9 37
H1N1, COPD*
193 10
Legionnaires, COPD*
237

29. 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.

30. Example – Variable PEEP with Respiratory System Elastance
Pt 2:
(Trauma)
Minimal Elastance PEEP
= 15cmH2O
Inflection PEEP
= 6~9cmH2O
Pt 6:
(Intra-abdominal sepsis, CHF)
Minimal Elastance PEEP
= 15cmH2O
Inflection PEEP
= 7.5~10cmH2O
Pt 8:
(Aspiration)
Minimal Elastance PEEP
= 25cmH2O
Inflection PEEP
= 12~18cmH2O
Pt 10:
(Legionnaires, COPD)
Minimal Elastance PEEP
= 20cmH2O
Inflection PEEP
= 12~15cmH2O

31. 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

32. New Concept - Variable PEEP with Edrs
Pt 2: (Trauma)
Pt 6: (Trauma)
Pt 8: (Aspiration)
Pt 10:
Legionnaires,
COPD

33. Example: Monitoring Edrs with time
Measured Pressure (Blue Line)
Model Pressure Fitting (Black Dots)
Edrs within a breath (Red Line)
What happens to Ers if PEEP Changes?

34. Comparing Lower and Higher PEEP in a Patient
Ventilated at Lower PEEP
Edrs within a breath drops, suggesting recruitment
Ventilated at Higher PEEP
Edrs within a breath increases, suggesting over distension
BERSTEN, A. D Measurement of overinflation by multiple linear regression analysis in patients with acute lung injury. Eur Respir J, 12,

35. 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

36. 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):
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

37. Animal trial results (unpublished)
Blue Line - Integral Based Constant Ers.
Black Line - Integral Based Median Edrs.
Green Line - Multiple Linear Regression Median Edrs.
Red Line - Ventilator Dynamic Elastance (∆P/Vt).
Pink Line - Ventilator Static Elastance ((Pend insp – P0)/Vt) – The ventilator has an inspiratory pause 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

38. 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

39. Research Collaborations?
Main Collaboration on MV Research
Cardiovascular Research Center, University of Liege, Liege, Belgium
Intensive Care Unit, CHU Sart-Tilman, Liege, Belgium
Institute for Technical Medicine, Furtwangen University, Germany
Other Collaborations
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?

41. Thank you!

42. Supplementary material

43. 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.

44. 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):
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.
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):
Sundaresan, A, Chase, JG, Shaw, GM, Chiew, YS and Desaive, T, Model-Based 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)

45. 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, Australia-New Zealand Intensive Care Society (ANZICS) ASM, Taupo, New Zealand, March 31 – April 1, 1-page, (2011).