Abstract
Recent research results provide new incentives to recognize and prevent ventilator-induced lung injury (VILI) and create targeting schemes for new modes of mechanical ventilation. For example, minimization of breathing power, inspiratory power, and inspiratory pressure are the underlying goals of optimum targeting schemes used in the modes called adaptive support ventilation (ASV), adaptive ventilation mode 2 (AVM2), and MID-frequency ventilation (MFV). We describe the mathematical models underlying these targeting schemes and present theoretical analyses for minimizing tidal volume, tidal pressure (also known as driving pressure), or tidal power as functions of ventilatory frequency. To go beyond theoretical equations, these targeting schemes were compared in terms of expected tidal volumes using different patient models. Results indicate that at the same ventilation efficiency (same PaCO2 level), we expect tidal volume dosage in the range of 7.4 mL/kg (for ASV), 6.2 mL/kg (for AVM2), and 6.7 mL/kg (for MFV) for adult ARDS simulation. For a neonatal RDS model, we expect 5.5 mL/kg (for ASV), 4.6 mL/kg (for AVM2), and 4.5 (for MFV).
Highlights
Adaptive ventilation modes are designed to automate some of the basic actions of clinicians as they attempt to identify the best settings, the definition of “best” continues to be a matter of debate
Minimization of breathing power, inspiratory power, and inspiratory pressure are the underlying goals of optimum targeting schemes used in the modes called adaptive support ventilation (ASV), adaptive ventilation mode 2 (AVM2), and MID-frequency ventilation (MFV)
Theoretical comparison of ASV, MFV, and AVM2 The optimal targeting schemes described above have one thing in common: for a given required alveolar minute ventilation and set of lung mechanics, they all suggest a ventilatory frequency that is optimal in some way
Summary
Adaptive ventilation modes are designed to automate some of the basic actions of clinicians as they attempt to identify the best settings, the definition of “best” continues to be a matter of debate. One strategy to incorporate clinical knowledge into machine design is to use what is called an optimum targeting scheme [1], a term adapted from engineering control theory. That model is called a cost function This function tells the machine how much a ventilation pattern “costs” in terms of predefined criteria. These criteria are based on actual patient characteristics (e.g., the cost function could describe tidal volume dosage). The goal of an optimum targeting scheme is to find the ventilation pattern with the lowest cost. If this optimum pattern is found, it can be used to set values van der Staay and Chatburn Intensive Care Medicine Experimental (2018) 6:30
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