Mechanical ventilation is a life-saving intervention, which despite its use on a routine basis, poses the risk of in icting further damage to the lung tissue if ventilator settings are chosen inappropriately. Medical decision support systems may help to prevent such injuries while providing the optimal settings to reach a de ned clinical goal. In order to develop and verify decision support algorithms, a test bench simulating a patient's behaviour is needed. We propose a Java based system that allows simulation of respiratory mechanics, gas exchange and cardiovascular dynamics of a mechanically ventilated patient. The implemented models are allowed to interact and are interchangeable enabling the simulation of various clinical scenarios. Model simulations are running in real-time and show physiologically plausible results.
ZusammenfassungDie Automatisierung der klinischen Intensivtherapie ist eine Entwicklung mit enormem ökonomischem Potential. Die immer komplexer werdenden Algorithmen für eine solche Automatisierung müssen während der Entwicklung aber auch zur Evaluierung und Validierung systematisch getestet werden. Besonders in der Entwicklungsphase eignen sich hierzu Simulationssysteme, die die physiologischen Reaktionen des Patienten abbilden und eine realitätsnahe Evaluierung ermöglichen. Im Folgenden soll ein solcher Patientensimulator vorgestellt werden, der einen mechanisch beatmeten Patienten abbilden kann. Die Ergebnisse der Patientensimulation zeigen in ihrer Gesamtheit physiologisch plausibles Verhalten und können in Echtzeit oder schneller berechnet werden.
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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