Mechanical Ventilation Guided by Driving Pressure Optimizes Local Pulmonary Biomechanics in an Ovine Model
Optimization of Respiratory Pressure Under Mechanical Ventilation Guidance - A Study on Improving Local Lung Biomechanics
Mechanical ventilation is commonly used in clinical practice to treat acute respiratory distress syndrome (ARDS) and prevent pulmonary complications after general anesthesia. However, mechanical ventilation can cause harmful stress and deformation to the lungs, increasing the complexity of clinical treatment and even leading to death. Studies have shown that increased driving pressure of the respiratory system is directly related to mortality associated with mechanical ventilation. Therefore, this study aims to explore the microscopic biomechanical factors of these associations and their spatial heterogeneity in the lungs, in order to optimize mechanical ventilation strategies.
Paper Background
Studying how to optimize the local biomechanical state of lung tissue by adjusting the positive end-expiratory pressure (PEEP) of mechanical ventilation under mechanical ventilation operations, thereby reducing ventilator-induced lung injury (VILI), is of great significance for improving the treatment effect of patients in intensive care units and operating rooms. Currently, there is limited research on how to adjust PEEP to homogenize lung air supply and reduce driving pressure, so this topic has potential clinical application value.
Research Source
This research paper was published by David Lagier’s team on August 14, 2024, in the journal “Science Translational Medicine”. The main participating institutions include the Experimental Interventional Imaging Laboratory of Aix Marseille University in France, the Departments of Anesthesia and Radiology at the University of Iowa in the United States, Guizhou University in China, Harvard Medical School, and others.
Research Design and Methods
The study used a high-resolution four-dimensional computed tomography (4D CT) and a multi-resolution convolutional neural network (CNN) whole-lung imaging segmentation technique to dynamically measure lung aeration and tidal volume deformation at the discrete voxel level.
Research Process and Experimental Steps
Experimental Subjects and Grouping:
- The experimental subjects were healthy and damaged sheep (ovine model), which were mechanically ventilated in the range of 20 to 2 cmH2O PEEP.
Imaging and Data Processing:
- Used 4D CT with a voxel resolution of 2.4 cubic millimeters, and CNN segmentation technology to dynamically measure lung aeration status and tidal volume deformation at the voxel level.
Gradual Adjustment of PEEP:
- Gradually reduced from 20 cmH2O to 2 cmH2O to assess the effects of different PEEP on healthy and injured lungs.
Biomechanical Analysis:
- Used dynamic gas fraction and voxel deformation data at the voxel level under different PEEP to compare lung mass changes under tidal and tidal overdistension (TOD) with different PEEP.
Data Statistics and Analysis:
- Used methods such as mixed effects analysis and linear regression analysis to quantify the effects of PEEP on different biomechanical processes in the lungs.
Research Results
Relationship between Respiratory System Driving Pressure and Local Lung Mechanical Properties:
- Experimental results showed that PEEP adjustment can optimize local lung mechanical properties by reducing driving pressure. Below PEEPdp, PEEP showed a decreasing relationship with changes in lung resistance, while above PEEPdp, it showed an increasing trend. Damaged lungs showed greater heterogeneity in lung ventilation deformation than normal lungs at high PEEP.
Lung Volume and Gas Filling Conditions:
- Through PEEP adjustment, the percentage of non-aerated mass (volume fraction <0.1) in damaged lungs increased significantly, while the mass of excessive gas expansion decreased significantly above PEEPdp. This showed that increasing PEEP under low PEEP pressure conditions helps to homogenize gas distribution and reduce excessive lung volume expansion.
Characteristic Action Point of PEEPdp:
- PEEPdp is the optimal PEEP value for reducing whole-lung mechanical injury. The study found that by adjusting to PEEPdp, airway resistance and tidal pressure in injured lungs were significantly reduced, and local physiological factors of lung injury were significantly improved.
Conclusion
The PEEP optimization strategy of mechanical ventilation can effectively reduce lung mechanical injury while improving the effectiveness of lung gas exchange. Specifically, PEEPdp represents the optimal adjustment point for reducing lung contraction, decreasing tidal overdistension, and homogenizing lung tidal volume deformation. This provides new biomechanical basis and imaging evidence support for achieving individualized mechanical ventilation treatment.
Scientific and Application Value of the Research
This research has important guiding significance for lung management in intensive care and post-anesthesia. Identifying PEEPdp as a marker for optimizing mechanical ventilation helps to formulate more effective ARDS treatment strategies, while laying the foundation for further clinical and animal model studies. By combining high-resolution imaging technology and AI algorithms, this study provides detailed lung function assessment methods, offering a powerful tool for future pulmonary pathology research.
Highlights of the Research
Technological Innovation:
- Adopted high-resolution 4DCT and deep learning technology to accurately measure lung aeration and tidal volume deformation at the voxel level.
Strong Practicality:
- Provided a PEEPdp-based optimal solution for lung injury, conducted detailed analysis based on actual clinical situations, with important significance for practical clinical operations.
Detailed Data Support:
- Revealed the effects of PEEP on local lung biomechanical states through various biomechanical analysis methods.
Additional Information
The study also revealed different responses of normal and damaged lungs at different PEEP levels, providing valuable reference data for future lung mechanical ventilation strategies under different health conditions.