CHAPTER 10 FUTURE WORK
10.2 Modelling Pathway
10.2.4 Automation
The analysis of the data in this research occurred after the trials had been completed. To be viable in the clinical scenario, the model requires a full or semi automated system which would incorporate data acquisition, processing and analysis. In addition, the process must be easy to use for the intensive care staff, and should incorporate all the processes that were highlighted in the recruitment manoeuvre and have the option of correcting for endotracheal tube resistance. Finally, the data must be easy to read and implement if necessary.
159
Appendix A – FRC Sensitivity Plots
Figure A. 1 - FRC as a function of varying α for Patient 1. Thick dotted line indicates estimated FRC when mean ELspecis used. (A) Normal distribution range - Thin lines show the +/- 1 and 2 SD from mean as reported by Chiumello et al [Chiumello et al., 2008]. (B) Lognormal distribution range Thin dotted lines show the +/- 2 SD
using lognormal distribution
Figure A. 2 - FRC as a function of varying α for Patient 2
159
Appendix A – FRC Sensitivity Plots
Figure A. 1 - FRC as a function of varying α for Patient 1. Thick dotted line indicates estimated FRC when mean ELspecis used. (A) Normal distribution range - Thin lines show the +/- 1 and 2 SD from mean as reported by Chiumello et al [Chiumello et al., 2008]. (B) Lognormal distribution range Thin dotted lines show the +/- 2 SD
using lognormal distribution
Figure A. 2 - FRC as a function of varying α for Patient 2
159
Appendix A – FRC Sensitivity Plots
Figure A. 1 - FRC as a function of varying α for Patient 1. Thick dotted line indicates estimated FRC when mean ELspecis used. (A) Normal distribution range - Thin lines show the +/- 1 and 2 SD from mean as reported by Chiumello et al [Chiumello et al., 2008]. (B) Lognormal distribution range Thin dotted lines show the +/- 2 SD
using lognormal distribution
Figure A. 3 - FRC as a function of varying α for Patient 3
Figure A. 4 - FRC as a function of varying α for Patient 4
Figure A. 5 - FRC as a function of varying α for Patient 5 Figure A. 3 - FRC as a function of varying α for Patient 3
Figure A. 4 - FRC as a function of varying α for Patient 4
Figure A. 5 - FRC as a function of varying α for Patient 5 Figure A. 3 - FRC as a function of varying α for Patient 3
Figure A. 4 - FRC as a function of varying α for Patient 4
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Figure A. 6 - FRC as a function of varying α for Patient 6
Figure A. 7 - FRC as a function of varying α for Patient 7
Figure A. 8 - FRC as a function of varying α for Patient 8
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Figure A. 6 - FRC as a function of varying α for Patient 6
Figure A. 7 - FRC as a function of varying α for Patient 7
Figure A. 8 - FRC as a function of varying α for Patient 8
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Figure A. 6 - FRC as a function of varying α for Patient 6
Figure A. 7 - FRC as a function of varying α for Patient 7
Figure A. 9 - FRC as a function of varying α for Patient 9
Figure A. 10 - FRC as a function of varying α for Patient 10
Figure A. 11 - FRC as a function of varying α for Patient 11 Figure A. 9 - FRC as a function of varying α for Patient 9
Figure A. 10 - FRC as a function of varying α for Patient 10
Figure A. 11 - FRC as a function of varying α for Patient 11 Figure A. 9 - FRC as a function of varying α for Patient 9
Figure A. 10 - FRC as a function of varying α for Patient 10
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Figure A. 12 - FRC as a function of varying α for Patient 12
163
Figure A. 12 - FRC as a function of varying α for Patient 12
163
Appendix B – Model Fitting Errors
Table B. 1 - Model fitting errors for all patients
PATIENT 1 Number of Units 144000 Inflation SD 15 Deflation SD 7 Auto-PEEP [cmH2O] 10 Inflation Deflation
PEEP [cmH2O] Mean Error [ml] Error [%] Mean Error [ml] Error [%]
10 30.97 22.17 7.86 15.57 15.48 4.61 15 28.07 22.50 4.44 17.68 5.61 1.02 20 27.12 22.27 3.11 19.88 5.72 0.63 25 26.41 21.71 2.05 22.43 7.47 0.68 27 26.18 15.11 1.34 23.39 2.73 0.24 PATIENT 2 Number of Units 171000 Inflation SD 11 Deflation SD 7 Auto-PEEP [cmH2O] 2 Inflation Deflation
PEEP [cmH2O] Mean Error [ml] Error [%] Mean Error [ml] Error [%]
5 22.15 50.26 16.50 12.40 23.68 8.98 10 21.53 37.78 6.14 13.86 7.08 1.28 15 21.53 41.85 4.78 16.10 7.47 0.78 20 22.69 35.41 3.15 18.24 4.82 0.41 22 23.60 33.79 2.76 19.48 7.65 0.60 PATIENT 3 Number of Units 220000 Inflation SD 12 Deflation SD 8 Auto-PEEP [cmH2O] 0 Inflation Deflation
PEEP [cmH2O] Mean Error [ml] Error [%] Mean Error [ml] Error [%]
0 26.07 51.08 151.80 14.38 45.59 116.89 5 22.54 55.09 16.17 13.97 40.19 11.08 10 21.25 12.79 1.97 14.57 18.31 2.76 15 21.25 25.68 2.43 16.34 20.53 1.92 20 21.25 36.99 2.49 18.11 23.34 1.58 25 21.25 48.72 2.65 19.98 13.26 0.76 28 22.37 32.58 1.69 21.86 12.88 0.69
165 PATIENT 4 Number of Units 220000 Inflation SD 25 Deflation SD 10 Auto-PEEP [cmH2O] 9 Inflation Deflation
PEEP [cmH2O] Mean Error [ml] Error [%] Mean Error [ml] Error [%]
10 55.56 9.37 5.52 20.04 4.28 1.16 15 44.45 32.86 7.02 21.10 8.24 1.31 20 37.09 35.19 4.47 21.58 1.95 0.19 25 33.18 39.34 3.52 23.13 1.39 0.11 30 30.77 24.31 1.77 24.75 5.10 0.32 PATIENT 5 - TRIAL 1 Number of Units 198000 Inflation SD 16 Deflation SD 10 Auto-PEEP [cmH2O] 13 Inflation Deflation
PEEP [cmH2O] Mean Error [ml] Error [%] Mean Error [ml] Error [%]
15 44.89 25.88 12.31 27.83 14.49 6.04 20 43.26 9.93 2.73 29.83 10.80 2.31 25 43.26 53.48 7.86 31.61 20.67 3.12 PATIENT 5 - TRIAL 2 Number of Units 161000 Inflation SD 15 Deflation SD 8 Auto-PEEP [cmH2O] 8 Inflation Deflation
PEEP [cmH2O] Mean Error [ml] Error [%] Mean Error [ml] Error [%]
10 36.99 31.51 19.71 19.30 15.86 7.52 15 33.05 16.44 4.16 21.09 10.88 2.75 20 31.11 15.75 2.39 22.72 2.85 0.45 25 32.42 21.27 2.52 25.73 13.99 1.55 29 32.42 30.72 2.85 26.91 10.75 1.03 PATIENT 6 - TRIAL 1 Number of Units 96000 Inflation SD 11 Deflation SD 7 Auto-PEEP [cmH2O] 10 Inflation Deflation
PEEP [cmH2O] Mean Error [ml] Error [%] Mean Error [ml] Error [%]
10 28.72 41.45 49.57 17.42 35.78 27.75 15 25.57 18.04 4.02 17.76 11.07 2.87 20 26.75 9.68 1.88 19.40 5.20 0.91 25 28.44 7.69 1.05 21.20 6.31 0.76
PATIENT 6 - TRIAL 2 Number of Units 171000 Inflation SD 14 Deflation SD 8 Auto-PEEP [cmH2O] 3 Inflation Deflation
PEEP [cmH2O] Mean Error [ml] Error [%] Mean Error [ml] Error [%]
5 23.58 27.97 7.98 12.41 11.54 3.46 10 21.89 7.07 1.17 14.09 7.25 1.28 15 20.83 9.70 1.11 15.66 2.70 0.32 20 20.83 18.99 1.56 16.49 17.35 1.37 25 20.83 20.73 1.41 18.60 2.91 0.19 PATIENT 6 - TRIAL 3 Number of Units 154000 Inflation SD 14 Deflation SD 9 Auto-PEEP [cmH2O] 2 Inflation Deflation
PEEP [cmH2O] Mean Error [ml] Error [%] Mean Error [ml] Error [%]
5 19.88 43.11 11.66 12.33 17.91 5.31 10 17.76 17.00 2.37 12.38 3.81 0.58 15 17.76 9.18 0.95 13.57 6.14 0.61 20 18.85 8.43 0.76 14.94 2.71 0.22 PATIENT 7 Number of Units 65000 Inflation SD 10 Deflation SD 5 Auto-PEEP [cmH2O] 2 Inflation Deflation
PEEP [cmH2O] Mean Error [ml] Error [%] Mean Error [ml] Error [%]
5 15.89 1.73 0.63 8.70 4.51 1.93 10 15.89 17.74 3.77 11.29 3.28 0.66 15 19.09 11.95 2.16 14.39 3.09 0.56 16 19.50 19.96 3.37 14.79 4.86 0.83 PATIENT 8 Number of Units 176000 Inflation SD 15 Deflation SD 10 Auto-PEEP [cmH2O] 0 Inflation Deflation
PEEP [cmH2O] Mean Error [ml] Error [%] Mean Error [ml] Error [%]
0 33.03 36.08 81.81 13.11 58.17 34.11 5 28.67 34.56 13.37 15.51 12.30 4.29 10 27.41 12.36 3.18 17.32 10.47 2.36 15 26.58 14.82 2.44 18.87 2.03 0.31 20 26.58 15.54 1.72 20.62 3.48 0.38 25 26.58 21.82 1.90 22.21 4.63 0.38 30 26.58 14.11 1.02 22.93 2.12 0.15
167 PATIENT 9 Number of Units 189000 Inflation SD 15 Deflation SD 9 Auto-PEEP 12 Inflation Deflation
PEEP Mean Error [ml] Error [%] Mean Error [ml] Error [%] 15 29.53 13.22 2.04 20.37 5.27 0.84 20 29.14 12.09 1.45 21.97 3.88 0.44 25 28.61 17.32 1.56 23.36 8.29 0.66 29 28.60 15.40 1.17 24.41 2.12 0.15 30 28.49 11.20 0.79 25.00 6.85 0.47 PATIENT 10 Number of Units 147000 Inflation SD 16 Deflation SD 9 Auto-PEEP 3 Inflation Deflation
PEEP Mean Error [ml] Error [%] Mean Error [ml] Error [%] 5 30.54 33.80 17.27 16.50 20.39 9.43 10 28.76 13.10 3.04 17.62 6.33 1.69 15 27.99 6.73 1.04 19.16 13.18 2.11 20 27.99 11.97 1.56 21.94 5.15 0.59 25 27.99 10.75 1.08 24.01 5.48 0.54 27 27.99 8.70 0.80 24.94 6.02 0.59
Appendix C – Recruitment Model Results
Figure C. 1 - Main plot shows TOP and TCP as a function of PEEP for Patient 1. Bottom left plot is the model fit. Bottom right indicates net recruitment
Table C. 1 - Clinically selected PEEP and Optimal PEEP indicated by TOP, TCP and net recruitment for Patient 1
Method Optimum PEEP[cm H
2O] Reason
TOP 27 Recruitment maximised - but could pose risk of VILI TCP 20 Higher PEEP results in less de-recruitment but can risk VILI Net
Recruitment 20 Lower PEEP does not maximise recruitment, while higher PEEP doesnot minimise de-recruitment Clinical
Setting 10 Clinician selected
Appendix C – Recruitment Model Results
Figure C. 1 - Main plot shows TOP and TCP as a function of PEEP for Patient 1. Bottom left plot is the model fit. Bottom right indicates net recruitment
Table C. 1 - Clinically selected PEEP and Optimal PEEP indicated by TOP, TCP and net recruitment for Patient 1
Method Optimum PEEP[cm H
2O] Reason
TOP 27 Recruitment maximised - but could pose risk of VILI TCP 20 Higher PEEP results in less de-recruitment but can risk VILI Net
Recruitment 20 Lower PEEP does not maximise recruitment, while higher PEEP doesnot minimise de-recruitment Clinical
Setting 10 Clinician selected
Appendix C – Recruitment Model Results
Figure C. 1 - Main plot shows TOP and TCP as a function of PEEP for Patient 1. Bottom left plot is the model fit. Bottom right indicates net recruitment
Table C. 1 - Clinically selected PEEP and Optimal PEEP indicated by TOP, TCP and net recruitment for Patient 1
Method Optimum PEEP[cm H
2O] Reason
TOP 27 Recruitment maximised - but could pose risk of VILI TCP 20 Higher PEEP results in less de-recruitment but can risk VILI Net
Recruitment 20 Lower PEEP does not maximise recruitment, while higher PEEP doesnot minimise de-recruitment Clinical
169
Figure C. 2 - Main plot shows TOP and TCP as a function of PEEP for Patient 2. Bottom left plot is the model fit. Bottom right indicates net recruitment
Table C. 2 - Clinically selected PEEP and Optimal PEEP indicated by TOP, TCP and net recruitment for Patient 2
Method Optimum PEEP[cm H
2O] Reason
TOP 15 Recruitment maximised - Higher PEEP resulted in circuit leak TCP 15 Higher PEEP results in less de-recruitment
Net
Recruitment 15 Lower PEEP does not maximise recruitment, while higher PEEP issub-optimal Clinical
Setting 12 Clinician selected
169
Figure C. 2 - Main plot shows TOP and TCP as a function of PEEP for Patient 2. Bottom left plot is the model fit. Bottom right indicates net recruitment
Table C. 2 - Clinically selected PEEP and Optimal PEEP indicated by TOP, TCP and net recruitment for Patient 2
Method Optimum PEEP[cm H
2O] Reason
TOP 15 Recruitment maximised - Higher PEEP resulted in circuit leak TCP 15 Higher PEEP results in less de-recruitment
Net
Recruitment 15 Lower PEEP does not maximise recruitment, while higher PEEP issub-optimal Clinical
Setting 12 Clinician selected
169
Figure C. 2 - Main plot shows TOP and TCP as a function of PEEP for Patient 2. Bottom left plot is the model fit. Bottom right indicates net recruitment
Table C. 2 - Clinically selected PEEP and Optimal PEEP indicated by TOP, TCP and net recruitment for Patient 2
Method Optimum PEEP[cm H
2O] Reason
TOP 15 Recruitment maximised - Higher PEEP resulted in circuit leak TCP 15 Higher PEEP results in less de-recruitment
Net
Recruitment 15 Lower PEEP does not maximise recruitment, while higher PEEP issub-optimal Clinical
Figure C. 3 - Main plot shows TOP and TCP as a function of PEEP for Patient 3. Bottom left plot is the model fit. Bottom right indicates net recruitment
Table C. 3 - Clinically selected PEEP and Optimal PEEP indicated by TOP, TCP and net recruitment for Patient 3
Method Optimum PEEP[cm H
2O] Reason
TOP 10 Recruitment maximised - Additional PEEP does not cause additionalrecruitment TCP 15 Higher PEEP results in less de-recruitment but can risk VILI Net
Recruitment 20 Lower PEEP does not maximise recruitment, while higher PEEP doesnot minimise de-recruitment Clinical
Setting 10 Clinician selected
Figure C. 3 - Main plot shows TOP and TCP as a function of PEEP for Patient 3. Bottom left plot is the model fit. Bottom right indicates net recruitment
Table C. 3 - Clinically selected PEEP and Optimal PEEP indicated by TOP, TCP and net recruitment for Patient 3
Method Optimum PEEP[cm H
2O] Reason
TOP 10 Recruitment maximised - Additional PEEP does not cause additionalrecruitment TCP 15 Higher PEEP results in less de-recruitment but can risk VILI Net
Recruitment 20 Lower PEEP does not maximise recruitment, while higher PEEP doesnot minimise de-recruitment Clinical
Setting 10 Clinician selected
Figure C. 3 - Main plot shows TOP and TCP as a function of PEEP for Patient 3. Bottom left plot is the model fit. Bottom right indicates net recruitment
Table C. 3 - Clinically selected PEEP and Optimal PEEP indicated by TOP, TCP and net recruitment for Patient 3
Method Optimum PEEP[cm H
2O] Reason
TOP 10 Recruitment maximised - Additional PEEP does not cause additionalrecruitment TCP 15 Higher PEEP results in less de-recruitment but can risk VILI Net
Recruitment 20 Lower PEEP does not maximise recruitment, while higher PEEP doesnot minimise de-recruitment Clinical
171
Figure C. 4 - Main plot shows TOP and TCP as a function of PEEP for Patient 4. Bottom left plot is the model fit. Bottom right indicates net recruitment
Table C. 4 - Clinically selected PEEP and Optimal PEEP indicated by TOP, TCP and net recruitment for Patient 4
Method Optimum PEEP[cm H
2O] Reason
TOP 30 Recruitment maximised - but could pose risk of VILI TCP 20 Higher PEEP results in less de-recruitment but can risk VILI Net
Recruitment 30 Lower PEEP does not maximise recruitment Clinical
Setting 10 Clinician selected
171
Figure C. 4 - Main plot shows TOP and TCP as a function of PEEP for Patient 4. Bottom left plot is the model fit. Bottom right indicates net recruitment
Table C. 4 - Clinically selected PEEP and Optimal PEEP indicated by TOP, TCP and net recruitment for Patient 4
Method Optimum PEEP[cm H
2O] Reason
TOP 30 Recruitment maximised - but could pose risk of VILI TCP 20 Higher PEEP results in less de-recruitment but can risk VILI Net
Recruitment 30 Lower PEEP does not maximise recruitment Clinical
Setting 10 Clinician selected
171
Figure C. 4 - Main plot shows TOP and TCP as a function of PEEP for Patient 4. Bottom left plot is the model fit. Bottom right indicates net recruitment
Table C. 4 - Clinically selected PEEP and Optimal PEEP indicated by TOP, TCP and net recruitment for Patient 4
Method Optimum PEEP[cm H
2O] Reason
TOP 30 Recruitment maximised - but could pose risk of VILI TCP 20 Higher PEEP results in less de-recruitment but can risk VILI Net
Recruitment 30 Lower PEEP does not maximise recruitment Clinical
Figure C. 5 - Main plot shows TOP and TCP as a function of PEEP for Patient 5, Trial 1. Bottom left plot is the model fit. Bottom right indicates net recruitment
Table C. 5 - Clinically selected PEEP and Optimal PEEP indicated by TOP, TCP and net recruitment for Patient 5, Trial 1
Method Optimum PEEP[cm H
2O] Reason
TOP 20 Recruitment maximised - Additional PEEP does not cause additionalrecruitment TCP 25 Higher PEEP results in less de-recruitment but can risk VILI
Net
Recruitment 25 Lower PEEP does not maximise recruitment Clinical
Figure C. 5 - Main plot shows TOP and TCP as a function of PEEP for Patient 5, Trial 1. Bottom left plot is the model fit. Bottom right indicates net recruitment
Table C. 5 - Clinically selected PEEP and Optimal PEEP indicated by TOP, TCP and net recruitment for Patient 5, Trial 1
Method Optimum PEEP[cm H
2O] Reason
TOP 20 Recruitment maximised - Additional PEEP does not cause additionalrecruitment TCP 25 Higher PEEP results in less de-recruitment but can risk VILI
Net
Recruitment 25 Lower PEEP does not maximise recruitment Clinical
Figure C. 5 - Main plot shows TOP and TCP as a function of PEEP for Patient 5, Trial 1. Bottom left plot is the model fit. Bottom right indicates net recruitment
Table C. 5 - Clinically selected PEEP and Optimal PEEP indicated by TOP, TCP and net recruitment for Patient 5, Trial 1
Method Optimum PEEP[cm H
2O] Reason
TOP 20 Recruitment maximised - Additional PEEP does not cause additionalrecruitment TCP 25 Higher PEEP results in less de-recruitment but can risk VILI
Net
Recruitment 25 Lower PEEP does not maximise recruitment Clinical
173
Figure C. 6 - Main plot shows TOP and TCP as a function of PEEP for Patient 5, Trial 2. Bottom left plot is the model fit. Bottom right indicates net recruitment
Table C. 6 - Clinically selected PEEP and Optimal PEEP indicated by TOP, TCP and net recruitment for Patient 5, Trial 2
Method Optimum PEEP[cm H
2O] Reason
TOP 20 Recruitment maximised - Additional PEEP is suboptimal andincreases compliance TCP 25 Higher PEEP results in less de-recruitment but can risk VILI
Net
Recruitment 20 Lower PEEP does not maximise recruitment, while higher PEEP issub-optimal Clinical
Setting 12 Clinician selected
173
Figure C. 6 - Main plot shows TOP and TCP as a function of PEEP for Patient 5, Trial 2. Bottom left plot is the model fit. Bottom right indicates net recruitment
Table C. 6 - Clinically selected PEEP and Optimal PEEP indicated by TOP, TCP and net recruitment for Patient 5, Trial 2
Method Optimum PEEP[cm H
2O] Reason
TOP 20 Recruitment maximised - Additional PEEP is suboptimal andincreases compliance TCP 25 Higher PEEP results in less de-recruitment but can risk VILI
Net
Recruitment 20 Lower PEEP does not maximise recruitment, while higher PEEP issub-optimal Clinical
Setting 12 Clinician selected
173
Figure C. 6 - Main plot shows TOP and TCP as a function of PEEP for Patient 5, Trial 2. Bottom left plot is the model fit. Bottom right indicates net recruitment
Table C. 6 - Clinically selected PEEP and Optimal PEEP indicated by TOP, TCP and net recruitment for Patient 5, Trial 2
Method Optimum PEEP[cm H
2O] Reason
TOP 20 Recruitment maximised - Additional PEEP is suboptimal andincreases compliance TCP 25 Higher PEEP results in less de-recruitment but can risk VILI
Net
Recruitment 20 Lower PEEP does not maximise recruitment, while higher PEEP issub-optimal Clinical
Figure C. 7 - Main plot shows TOP and TCP as a function of PEEP for Patient 6, Trial 1. Bottom left plot is the model fit. Bottom right indicates net recruitment
Table C. 7 - Clinically selected PEEP and Optimal PEEP indicated by TOP, TCP and net recruitment for Patient 6, Trial 1
Method Optimum PEEP[cm H
2O] Reason
TOP 15 Recruitment maximised - Additional PEEP is suboptimal andincreases compliance TCP 20 Higher PEEP results in less de-recruitment but can risk VILI
Net
Recruitment 20 Lower PEEP does not maximise recruitment, while higher PEEP issub-optimal Clinical
Figure C. 7 - Main plot shows TOP and TCP as a function of PEEP for Patient 6, Trial 1. Bottom left plot is the model fit. Bottom right indicates net recruitment
Table C. 7 - Clinically selected PEEP and Optimal PEEP indicated by TOP, TCP and net recruitment for Patient 6, Trial 1
Method Optimum PEEP[cm H
2O] Reason
TOP 15 Recruitment maximised - Additional PEEP is suboptimal andincreases compliance TCP 20 Higher PEEP results in less de-recruitment but can risk VILI
Net
Recruitment 20 Lower PEEP does not maximise recruitment, while higher PEEP issub-optimal Clinical
Figure C. 7 - Main plot shows TOP and TCP as a function of PEEP for Patient 6, Trial 1. Bottom left plot is the model fit. Bottom right indicates net recruitment
Table C. 7 - Clinically selected PEEP and Optimal PEEP indicated by TOP, TCP and net recruitment for Patient 6, Trial 1
Method Optimum PEEP[cm H
2O] Reason
TOP 15 Recruitment maximised - Additional PEEP is suboptimal andincreases compliance TCP 20 Higher PEEP results in less de-recruitment but can risk VILI
Net
Recruitment 20 Lower PEEP does not maximise recruitment, while higher PEEP issub-optimal Clinical
175
Figure C. 8 - Main plot shows TOP and TCP as a function of PEEP for Patient 6, Trial 2. Bottom left plot is the model fit. Bottom right indicates net recruitment
Table C. 8 - Clinically selected PEEP and Optimal PEEP indicated by TOP, TCP and net recruitment for Patient 6, Trial 2
Method Optimum PEEP[cm H
2O] Reason
TOP 15 Recruitment maximised - Additional PEEP does not cause additionalrecruitment TCP 15 Higher PEEP results in less de-recruitment but can risk VILI
Net
Recruitment 20 Lower PEEP does not maximise recruitment, while higher PEEP doesnot minimise de-recruitment Clinical
Setting 13 Clinician selected
175
Figure C. 8 - Main plot shows TOP and TCP as a function of PEEP for Patient 6, Trial 2. Bottom left plot is the model fit. Bottom right indicates net recruitment
Table C. 8 - Clinically selected PEEP and Optimal PEEP indicated by TOP, TCP and net recruitment for Patient 6, Trial 2
Method Optimum PEEP[cm H
2O] Reason
TOP 15 Recruitment maximised - Additional PEEP does not cause additionalrecruitment TCP 15 Higher PEEP results in less de-recruitment but can risk VILI
Net
Recruitment 20 Lower PEEP does not maximise recruitment, while higher PEEP doesnot minimise de-recruitment Clinical
Setting 13 Clinician selected
175
Figure C. 8 - Main plot shows TOP and TCP as a function of PEEP for Patient 6, Trial 2. Bottom left plot is the