optimal peep – the final solution (model-based mechanical ventilation for intensive care) geoffrey...

D epartm ent of Intensive C are

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 Zealand2Mechanical Engineering, University of Canterbury, New

Zealand


Christchurch

Melbourne


Universities of Canterbury, Otago, and Christchurch Hospital


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


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 "babylung“Ferguson, N. D. et al(2005). Airway pressures, tidalvolumes, and mortality in patients with acute respiratorydistress syndrome.


Treatment for ALI/ ARDS


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


Model-based Mechanical Ventilation Part 1

A Minimal Model of Lung Mechanics


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


Minimal Model – Recruitment

Alveoli do not behave like balloons


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


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

BERSTEN, A. D. 1998. Measurement of overinflation by multiple linear regression analysis in patients with acute lung injury. Eur Respir J, 12, 526-532.

Original data sourced form:


Model Basics

Mean Threshold Opening Pressure

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:


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?


Change in TOP or SD

• Monitor the Change of TOP or Standard Deviation• Potential to group Patients based on TOP and SD

information


Disease State Grouping (DSG)

• 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

Normal

ARDS

H1N1

ALI

How does lung injury progress?

How does the lung recover?


Disease State Grouping (DSG)

Bird/Swine flu

E.g. “Bad cold”

vs


Possible Examples in DSG

ARDSH1N1

Healthy Beginning of ALI


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.

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.

*

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.



Continuously Monitoring Lung Elastance to Guide Mechanical

Ventilation PEEP


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. 2009. Lung Mechanics: An Inverse Modelling Approach, Cambridge University Press.

Q(t)

Rrs P(t)

Ers

V(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.


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.



Clinical Trials for Proof of concept


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.


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 M 55 Aspiration 223 0.35

4 M 88 Pneumonia, COPD 165 0.405 M 59 Pneumonia, COPD, CHF 285 0.406 M 69 Intra-abdominal sepsis, MOF 280 0.35

7 M 56 Legionnaires 265 0.55

8 F 54 Aspiration 303 0.40

9 M 37 H1N1, COPD* 193 0.40

10 M 56 Legionnaires, COPD* 237 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.


Example – Variable PEEP with Respiratory System Elastance

Pt 2: (Trauma)

Minimal Elastance PEEP= 15cmH2O

Inflection PEEP= 6~9cmH2O

Pt 8: (Aspiration)



Pt 6:(Intra-abdominal sepsis, CHF)


Inflection PEEP= 7.5~10cmH2O

Pt 10: (Legionnaires, COPD)




• 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

Patient 6 (Intra-abdominal sepsis, CHF)


New Concept - Variable PEEP with Edrs

Pt 2: (Trauma)

Pt 8: (Aspiration)

Pt 6: (Trauma)

Pt 10: Legionnaires, COPD


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?

10 15 20 25 300

10

20

30

40

Time (seconds)

Pre

ssur

e an

d E

drs


Comparing Lower and Higher PEEP in a Patient

10 15 20 25 300

10

20

30

40

Time (seconds)

Pre

ssur

e an

d E

drs

10 15 20 25 300

10

20

30

40

Time (seconds)

Pre

ssur

e an

d E

drs

• Ventilated at Lower PEEP

• Edrs within a breath drops, suggesting recruitment

• Ventilated at Higher PEEP

• Edrs within a breath increases, suggesting over distension



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


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 ModelSundaresan, 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 ModelChiew, 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


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 otherComputer Methods estimating Ers was able to reproduce the findings in ventilator. Change in Elastance was observed with the development of ARDS

20 40 60 80 100 120 140 160 180 2000

50

100

150

Time (minutes)

Ela

sta

nc

e

Integral Based Constant ElungIntegral based Median EdrsMultiple Linear Regression EdrsVent Dynamic ElastanceVent Static Elastance


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


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?


Thank you!


Supplementary material


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.


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, 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): 135-143.

• 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)


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).

optimal peep – the final solution (model-based mechanical ventilation for intensive care) geoffrey...

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