Critically ill, mechanically ventilated patients require a substantial amount of energy to meet their basic metabolic demands, as well as for their recovery, spontaneous breathing, gaining of strength, and weaning from the ventilator. It is important for patients to use their energy as efficient as possible at all stages of the disease and recovery. Physicians need to estimate and match the energy cost of the treatment regimen with the patient’s ability for energy expenditure. If the demands of the treatment regimen, such as weaning from the ventilator, are too high, it will lead to exertion and fatigue. On the other hand, if the patient is not challenged enough, it could lead to a slower recovery and prolonged admission to the Intensive Care Unit (ICU). For example, it is known that a too slow reduction in ventilator support can lead to atrophy of the diaphragm, resulting in prolonged ventilation time.
Predicting successful extubation is crucial for mechanically ventilated ICU patients. Prolonged intubation leads to prolonged mechanical ventilation, which is known to have adverse effects. Up to 20 % of all ICU patients fail to wean from mechanical ventilation. Therefore, it is crucial to investigate different parameters of effort. This will be elaborated on in Chapter 1 of the thesis. The oxygen consumption seems to be a good new parameter to add in standard of care.

To measure oxygen consumption (VO2) breath-by-breath, it is necessary to synchronize flow and oxygen concentration data. To create this algorithm, it was necessary to design an in vitro test setup that correctly mimics the mechanically ventilated ICU patient. This setup and the plan of requirements is described in Chapter 2. In the test setup a cylinder was used with N2 and CO2 to simulate oxygen consumption and CO2 production.

The algorithm developed using the data generated with the test setup using the Hamilton Mechanical Ventilator C6 flow data and the Masimo ISA OR+ oxygen concentration data, is used to synchronize the data from the two devices and to determine the VO2. As the rise time of the oxygen sensor in the Masimo was slow, this signal needed some adaptions. Furthermore, the time stamps were not the same, as with the sample frequency. After the synchronization, the VO2 was derived by taking the integral of the two synchronized signals per breath. These values were compared to predicted values, derived from breath-by-breath parameter data of the two devices. All the preprocessing, synchronization and eventual oxygen consumption measurements are described in Chapter 3.

As there were too many assumptions in the design of the in vitro test setup, it was necessary to create a new setup with minimized number of assumptions. This new test setup had two extra flow sensors in order to monitor all leaks and flows. Next to the more reliable data from the setup, some simulation data was created using Python. Both data were used to validate the algorithm. These steps were explained in Chapter 4.

In Chapter 5 some perspectives for the future were explained. This includes the integration of the VO2 in clinical practice and the steps that still need to be taken to come that far. These include testing on patient data and performing more clinical trials.