Componentes basados en el COSO III

To accurately apply the recruitment model, the patient requires a deflation to ZEEP so that dFRC can be measured. In reality, the deflation to ZEEP can prove to be harmful to patients who are severely ARDS affected, as it may cause sudden de-recruitment. During the clinical trials, all patients were deflated to ZEEP with the exception of the first trial for Patient 6. For this trial, the lowest PEEP was set at 10 cmH2O, with PEEP being incremented to 15, 20 and 25 cmH2O, shown in Figure 7.7.

Figure 7.6 - Medianβacross combined dataset compared with the medianβof Group 1

The difference between the median ß for the initial validation on Group 1 and the combined dataset (Group 1 & Group 2) shows a small difference, as shown in Figure 7.6. However, this difference is assumed to occur due to the limited size of the dataset of Group 1. Hence, there is a strong indication of a global population constant. As the size of the dataset increases, ß should also change. More specifically, as the number of data points increase, the assumption ofßrepresenting a population constant will become more valid.

7.2.3 DFRC & RECRUITMENT MODELS

To accurately apply the recruitment model, the patient requires a deflation to ZEEP so that dFRC can be measured. In reality, the deflation to ZEEP can prove to be harmful to patients who are severely ARDS affected, as it may cause sudden de-recruitment. During the clinical trials, all patients were deflated to ZEEP with the exception of the first trial for Patient 6. For this trial, the lowest PEEP was set at 10 cmH2O, with PEEP being incremented to 15, 20 and 25 cmH2O, shown in Figure 7.7.

Figure 7.6 - Medianβacross combined dataset compared with the medianβof Group 1

The difference between the median ß for the initial validation on Group 1 and the combined dataset (Group 1 & Group 2) shows a small difference, as shown in Figure 7.6. However, this difference is assumed to occur due to the limited size of the dataset of Group 1. Hence, there is a strong indication of a global population constant. As the size of the dataset increases, ß should also change. More specifically, as the number of data points increase, the assumption ofßrepresenting a population constant will become more valid.

7.2.3 DFRC & RECRUITMENT MODELS

To accurately apply the recruitment model, the patient requires a deflation to ZEEP so that dFRC can be measured. In reality, the deflation to ZEEP can prove to be harmful to patients who are severely ARDS affected, as it may cause sudden de-recruitment. During the clinical trials, all patients were deflated to ZEEP with the exception of the first trial for Patient 6. For this trial, the lowest PEEP was set at 10 cmH2O, with PEEP being incremented to 15, 20 and 25 cmH2O, shown in Figure 7.7.

123

Figure 7.7 - PV curves for Patient 6, Trial 1. No measurements of dFRC were taken as patient was not deflated to ZEEP

Although the recruitment model can be fitted to these PV loops, the model works best when dFRC measurements are known. If the deflation to ZEEP is deemed unsafe for the patient, then estimating dFRC could be used as an alternative to actual measurements. Figure 7.8 shows dFRC being estimated and the corresponding PV curves being shifted for Patient 6, Trial 1. The re-calculated PV curves can be fitted to the recruitment models to evaluate new threshold opening (TOP) and threshold closing pressure (TCP) parameters, which are shown in Figure 7.9. This process is only valid whenβis a population constant, which is a function of PEEP.

Figure 7.8 - PV curves for Patient 6, Trial 1. dFRC is estimated using the linear compliance for Patient 6, Trial 1.

123

Figure 7.7 - PV curves for Patient 6, Trial 1. No measurements of dFRC were taken as patient was not deflated to ZEEP

Although the recruitment model can be fitted to these PV loops, the model works best when dFRC measurements are known. If the deflation to ZEEP is deemed unsafe for the patient, then estimating dFRC could be used as an alternative to actual measurements. Figure 7.8 shows dFRC being estimated and the corresponding PV curves being shifted for Patient 6, Trial 1. The re-calculated PV curves can be fitted to the recruitment models to evaluate new threshold opening (TOP) and threshold closing pressure (TCP) parameters, which are shown in Figure 7.9. This process is only valid whenβis a population constant, which is a function of PEEP.

Figure 7.8 - PV curves for Patient 6, Trial 1. dFRC is estimated using the linear compliance for Patient 6, Trial 1.

123

Figure 7.7 - PV curves for Patient 6, Trial 1. No measurements of dFRC were taken as patient was not deflated to ZEEP

Although the recruitment model can be fitted to these PV loops, the model works best when dFRC measurements are known. If the deflation to ZEEP is deemed unsafe for the patient, then estimating dFRC could be used as an alternative to actual measurements. Figure 7.8 shows dFRC being estimated and the corresponding PV curves being shifted for Patient 6, Trial 1. The re-calculated PV curves can be fitted to the recruitment models to evaluate new threshold opening (TOP) and threshold closing pressure (TCP) parameters, which are shown in Figure 7.9. This process is only valid whenβis a population constant, which is a function of PEEP.

Figure 7.9 - Comparison of TOP and TCP when dFRC is estimated for Patient 6, Trial 1

Figure 7.9 shows the TOP and TCP when dFRC is estimated in this way for that patient. Although there is a decrease in absolute values of TOP and TCP, the trend of both seems to remain relatively constant. In addition, when dFRC is not measured, TCP predicts optimum PEEP at 20 cmH2O. In contrast, when dFRC is estimated, TCP selected PEEP is 15 cmH2O.

7.3 SUMMARY

The results discussed in this chapter further highlight the potential for β to be used as a global population constant (as a function of PEEP) for estimating dFRC. The preliminary validation performed in Chapter 4 provided evidence on the variation inβwhen PEEP was varied between 5 and 15 cmH2O, and showed a linear relationship between PEEP and β. By increasing the dataset, the linear relationship was also shown to hold at higher PEEP values up to 30 cmH2O.

The increased dataset has also highlighted a limitation to the dFRC estimation model. For a given patient, when compliance changes significantly with PEEP, the model fails to accurately estimate true dFRC. Thus, the model is primarily valid when compliance is constant across all PEEP for a given patient. For patients where compliance did vary significantly at low and high PEEP, this limitation may not necessarily pose a major problem as these PEEP values are outside the clinically useful range. In addition, although

Figure 7.9 - Comparison of TOP and TCP when dFRC is estimated for Patient 6, Trial 1

Figure 7.9 shows the TOP and TCP when dFRC is estimated in this way for that patient. Although there is a decrease in absolute values of TOP and TCP, the trend of both seems to remain relatively constant. In addition, when dFRC is not measured, TCP predicts optimum PEEP at 20 cmH2O. In contrast, when dFRC is estimated, TCP selected PEEP is 15 cmH2O.

7.3 SUMMARY

The results discussed in this chapter further highlight the potential for β to be used as a global population constant (as a function of PEEP) for estimating dFRC. The preliminary validation performed in Chapter 4 provided evidence on the variation inβwhen PEEP was varied between 5 and 15 cmH2O, and showed a linear relationship between PEEP and β. By increasing the dataset, the linear relationship was also shown to hold at higher PEEP values up to 30 cmH2O.

The increased dataset has also highlighted a limitation to the dFRC estimation model. For a given patient, when compliance changes significantly with PEEP, the model fails to accurately estimate true dFRC. Thus, the model is primarily valid when compliance is constant across all PEEP for a given patient. For patients where compliance did vary significantly at low and high PEEP, this limitation may not necessarily pose a major problem as these PEEP values are outside the clinically useful range. In addition, although

Figure 7.9 - Comparison of TOP and TCP when dFRC is estimated for Patient 6, Trial 1

Figure 7.9 shows the TOP and TCP when dFRC is estimated in this way for that patient. Although there is a decrease in absolute values of TOP and TCP, the trend of both seems to remain relatively constant. In addition, when dFRC is not measured, TCP predicts optimum PEEP at 20 cmH2O. In contrast, when dFRC is estimated, TCP selected PEEP is 15 cmH2O.

7.3 SUMMARY

The results discussed in this chapter further highlight the potential for β to be used as a global population constant (as a function of PEEP) for estimating dFRC. The preliminary validation performed in Chapter 4 provided evidence on the variation inβwhen PEEP was varied between 5 and 15 cmH2O, and showed a linear relationship between PEEP and β. By increasing the dataset, the linear relationship was also shown to hold at higher PEEP values up to 30 cmH2O.

The increased dataset has also highlighted a limitation to the dFRC estimation model. For a given patient, when compliance changes significantly with PEEP, the model fails to accurately estimate true dFRC. Thus, the model is primarily valid when compliance is constant across all PEEP for a given patient. For patients where compliance did vary significantly at low and high PEEP, this limitation may not necessarily pose a major problem as these PEEP values are outside the clinically useful range. In addition, although

125 predicted and measured dFRC may show high percentage errors, within the linear region, the trends are still the same.

The dFRC model can be used in conjunction with the recruitment model when dFRC measurements are not available. When the deflation to ZEEP is deemed to hazardous to the patient, estimating dFRC provides a means to improve the results of the recruitment model and provide a more accurate set of PEEP results and potentially improved decision support.

Chapter 8 - Cardiac Output Estimation