Volume was calculated by integrating the flow data over time for each data set. A specialised program was written using MATLAB to discretise the PV data at each pressure. The 2 PV loops from each set are then combined into a single loop by taking an average at each pressure point. Finally, the data was smoothed using a 3 point moving average method to eliminate the low level of noise.
Note that the overall process of ventilating, gathering, and analysing the data is designed to mimic, as closely as possible, what might or could occur clinically.
Hence, a second goal is to ensure the clinical reality of the conditions and thus the clinical robustness of the methods.
6.2 Simulation Results and Physiological Relevance
Figure 6.3 illustrates an example of raw pressure and flow data. This data is for a data set with PEEP of 5 cmH2O and flow rate of 10 LPM. The red line shows the pressure and blue line shows flow. The data captures 2 complete cycles of ventilation. Figure 6.4 illustrates the resulting volume, as calculated from the data in Figure 6.3. The resulting PV curve is illustrated in Figure 6.5. The black solid line shows the original raw data and blue and green dashed lines shows processed inflation and deflation data, respectively.
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Time [Sec]
Pressure [cmH 2O] / Flow [LPM]
Flow Pressure
Figure 6.3 An example of raw pressure and flow data. Pressure and flow are measured and recorded for each set of data. These plot shows data set for carina measurement at PEEP=5 cmH2O and flow rate=10 LPM.
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Figure 6.4 An example of calculated volume data. The volume is calculated by integrating the flow. The plot shows data set for carina measurement at PEEP=5 cmH2O and flow rate=10 LPM. The volume is thus a result of integrating the data shown in Figure 6.3.
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Figure 6.5 An example of resulting raw and processed PV data. The pressure and volume data were discretised, combined into a single loop, and smoothed for ease of fitting process.
The black solid line shows the original raw data and blue and green dashed lines show processed inflation and deflation data, respectively. This plot shows the data set for PEEP=5 cmH2O and flow rate=10 LPM.
The relatively high noise level at lower volume in the raw PV data is caused by the concentration of sampled points and the diminishing sensitivity of the flow sensor at low values. Due to the nature of inflation and deflation mechanics, and the relationship between pressure and volume, there are more data points at lower volume in the PV loop for data with a constant sample rate. Furthermore, the sensitivity of the flow sensor decreases at a low flow causing the noise level to increase, especially at the end of deflation, where the flow nears zero. This effect
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is noticeable even in this near perfect simulator environment, as compared to a real clinical situation where greater noise might be encountered.
Effect of ET Tube Resistance
The effect of ET tube resistance was analysed using the multiple measurements available from the mechanical simulator. Pressure measurements were taken be-fore and after the ET tube, representing the proximal and carina pressures, re-spectively. The proximal pressure includes the effect of ET tube resistance, how-ever this data is often the only data available for a mechanically ventilated ICU patient. In contrast, the carina measurement at the tracheal end of the ET tube reflects the more accurate true lung mechanics, especially at higher flow rates [Karason et al., 2001]. However, in most cases, this measurement requires addi-tional invasive sensors and equipment. Thus, this measurement is only taken in special circumstances or for research purposes [Karason et al., 2000; Sondergaard et al., 2002; Stenqvist, 2003].
Figure 6.6 illustrates the pressure and flow measurements at the carina of the mechanical simulator for the data set with PEEP of 5 cmH2O and a flow rate of 60 LPM. Compared to the 10 LPM flow rate in Figure 6.3, the inspiratory time is significantly shorter because the target volume is reached more quickly due to the increased flow rate. Figure 6.7 illustrates proximal pressure and flow data from the same data set. The inspiratory pressure in Figure 6.7 is almost twice as high as the carina measurement in Figure 6.6, indicating the significant contribution of the resistive effect at this higher flow rate.
Finally, the effect of the resistance is also clearly illustrated in the PV loops in Figure 6.8, where the outer loops are the proximal measurements and the inner loops are the carina measurements for each plot. The figure includes PV loops for all 4 flow rates. The proximal PV loops were similarly and significantly different for other flow rates. For example, the data for 10 LPM illustrated in the top left plot, shows minimal differences in PV data during inflation as the lower flow rate induces lower flow resistance based on standard fluid mechanics [Zamir, 2000]. This lesser difference during the inflation limb increased as the flow rate increased, as illustrated in the top right plot for 20 LPM, the bottom left plot for 40 LPM, and the bottom right plot for 60 LPM in Figure 6.8. This trend clearly indicates the strong dependence of resistance on the flow rate.
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Pressure [cmH 2O] / Flow [LPM]
Flow Pressure
Figure 6.6 An example of raw pressure and flow data at carina. This plot shows the data set for PEEP=5 cmH2O and flow rate=60 LPM.
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Pressure [cmH 2O] / Flow [LPM]
Flow Pressure
Figure 6.7 An example of raw pressure and flow data at proximal. This plot shows the data set for PEEP=5 cmH2O and flow rate=60 LPM.
In contrast, the deflation limbs essentially do not vary for different flow rates, as illustrated in Figure 6.9. This lack of difference is caused by the uncontrolled passive nature of the deflation process. The varying flow rates only control the flow rate for the inflation portion. During deflation, the applied pressure is simply reduced to PEEP at the beginning of deflation and the air flows out passively.
Since the volume inside the simulator and the carina pressure is similar at the end of inflation for all flow rates, the deflation proceeds at the same rate for all flow rates as a function primarily of the outlet size and pressure. However, there are significant differences between proximal and carina deflation measurements, especially at the beginning of deflation where the flow is the highest, as best illustrated in Figure 6.8.
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Figure 6.8 The resistive effect of the ET tube. These plots illustrate the differences between carina and proximal measurement for all flow rates. The outer PV loop is the proximal and the inner loop is from the carina measurement. The significant difference in pressure between the data are a direct result of the resistance in the ET tube.
Proximal Carina
Figure 6.9 Carina and proximal PV data for all 4 flow rates. In contrast to proximal PV data in the left plot, the carina PV data on the right show significantly less differences between different flow rates.
Karason et al. [2000] have conducted a similar experiment on both a mechani-cal lung model and actual patients. They reported similar results and conclusions on flow and resistance through an ET tube. Figure 6.10 illustrates an example of the resulting plots from the study. These plots show PV data for a patient who is ventilated at 20 breaths/min, PEEP of 8 cmH2O, I:E ratio of 1:2, and
the ventilator is volume controlled. Different tidal volumes of 4, 8, and 12 ml/kg were used in this particular data set. Because the respiration rate and the I:E ratio is kept constant, higher tidal volume leads to a higher flow. Thus, this re-sult is comparable to the CPAP simulator rere-sults where different flow rates were simulated.
The plot for 4 ml/kg shows a relatively small difference between the prox-imal Y-piece and tracheal carina loops during inflation, while at 12 ml/kg, the difference is significant. On the deflation limb, the overall relative shape between proximal and carina loops is unchanged. These features are closely matched by the CPAP model, as illustrated in Figure 6.8. Thus, this comparison further demonstrates the model’s validity to simulate realistic lung mechanics.
Figure 6.10 An example result from study conducted by Karason et al. [2000]. The plot shows PV loops for Y-piece and tracheal measurement for different tidal volumes. Because respiratory rate was kept constant, the flow rates were changed between different tidal volumes.
The CPAP model closely matches this clinical data.
The carina PV data shows very small differences between different flow rates compared to the proximal PV data. Figure 6.9 clearly illustrates this minimal difference. This similarity in carina PV data suggests that the mechanism of the simulator is relatively stable. It can also be concluded that, in this experiment, most of the differences are caused by the resistive force in the ET tube.