The aim of mechanical ventilation (MV) is to provide sufficient breathing support for critically ill patients with respiratory failure in the intensive care unit (ICU). However, the application of inappropriate MV settings can result in ventilator induced lung injury (VILI) and exacerbate respiratory dysfunction. To prevent VILI, respiratory mechanics properties such as elastance and resistance can be estimated at the bedside to guide MV settings. Different models or methods provide different information and have unique advantages and disadvantages. In this study, the respiratory mechanics of 25 respiratory failure patients were determined using the first order model (FOM) and a viscoelastic model (VEM). The patients underwent different respiratory manoeuvres and their identified respiratory mechanics using these models are studied and compared with a standard clinical method in estimating respiratory mechanics. The results show that both models were able to capture patient-specific mechanics and responses. The FOM was able to provide higher correlation to the standard clinical method while the VEM provides a physiologically more plausible representation.
The use of mathematical models can aid in optimizing therapy settings in ventilated patients to achieve certain therapy goals. Especially when multiple goals have to be met, the use of individualized models can be of great help. The presented work shows the potential of using models of respiratory mechanics and gas exchange to optimize minute ventilation and oxygen supply to achieve a defined oxygenation and carbon dioxide removal in a patient while guaranteeing lung protective ventilation. The ventilator settings are optimized using respiratory mechanics models to compute a respiration rate and tidal volume that keeps the maximum airway pressure below the critical limit of 30 cm H 2 O while ensuring a sufficient expiration. A three-parameter gas exchange model is then used to optimize both minute ventilation and oxygen supply to achieve defined arterial partial pressures of oxygen and carbon dioxide in the patient. The presented approach was tested using a JAVA based patient simulator that uses various model combinations to compute patient reactions to changes in the ventilator settings. The simulated patient reaction to the optimized ventilator settings showed good agreement with the desired goals.
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