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Background High-flow oxygen therapy (HFOT) is increasingly utilized in clinical settings due to its potential to improve oxygenation, patient comfort and possibly outcomes in different respiratory failure states. The potential benefits of HFOT include matching the patients’ flow to reduce the work of breathing, creating airway pressure, humidification, and reducing dead space. However, it has some shortcomings including the difficulty of setting the flow to match patients’ efforts, the continuous flow does not represent normal physiologic spontaneous breathing, and the lack of traditional airway pressure monitoring. We are testing a new adaptive high-flow oxygen delivery technique “Auto Positive Airway Pressure” (Auto-PAP) where the flow delivered from the ventilator adapts to patients’ efforts taking into consideration the patients’ dead space Methods A bench study using ASL 5000 simulator, we created a single-compartment active model of a male with IBW 70 kg, with compliance of 40 ml/cmH20 and resistance of 10 cmH2o/L/s. The respiratory rate was set at 20 bpm, with inspiratory time of 1 second. Muscle pressure (Pmus) was gradually increased by increments of 5 cmH2O from 5 to 50. Adaptive high flow mode (Auto PAP) using a Bellavista 1000e ventilator (Zoll MA, USA) using a large bore nasal cannula to an adult-sized mannequin nose that was connected to the lung simulator. Pearson correlation coefficient was used in correlating the Pmus to the maximum, mean flow and pressure, and the simulator to the ventilator flow. Results There was significant strong correlation between the Pmus and the maximum flow R: 0.949, CI (0.794, 0.988) P < 0.001, and mean flow R: 0.955, P < 0.001, CI (0.816, 0.989). There was a significant strong correlation with Pmus and the maximum pressure R: 0.972, P < 0.001, CI (0.883, 0.993) and mean pressure R: 0.942, P < 0.001, CI (0.768, 0.986). There was significant strong correlation between the simulator and the ventilator flow R: 0.961, P < 0.001. There was significant strong correlation between the simulator and the ventilator mean pressure R: 0.951, P < 0.001, CI (0.799, 0.988). There was significant strong correlation between the mean flow from the ventilator to the mean airway pressure measured at the simulator R: 0.936, P < 0.001 CI (0.747, 0.985). Conclusion Results suggest a significant correlation between the flow and pressure from adaptive HFOT and the patient muscle effort, indicating that the flow and pressure increase in response to the patients’ effort. This may reduce respiratory muscle workload compared to traditional high flow oxygen therapies and reduce the need for multiple manual adjustment of the flow. These findings underscore the potential of this technology to enhance respiratory support while minimizing patient effort. Further research is warranted to validate these findings in real patient cohorts in diverse clinical scenarios. |
