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AUTOPILOT-BT: an approach towards automatic mechanical ventilation

Introduction

The clinical use of ventilators is limited due to a huge variety of different ventilation methods. The clinician – often under high cognitive load from the complicated technical equipment on an ICU – just uses a small subset of available parameter settings. The aim of the present study was to develop a closed-loop ventilation controller based on mathematical models and fuzzy logic.

Methods

The system was designed to track a desired end-tidal CO2 pressure (PaCO2), to find a PEEP leading to maximum estimated respiratory system compliance and to maintain the arterial oxygen saturation (SaO2) at an optimal level. We developed a program in LabView (National Instruments, Austin, TX, USA) on a laptop that is able to read the internal data of a ventilator (Evita 4; Dräger Medical, Germany) in real time. Respiratory signals (for example, SaO2) are acquired from monitoring. Discrete measurements (for example, PaO2) are either assumed constant until next measurement or are interpolated using a model-based approach evaluating, for example, the etCO2 data. The course of etCO2 following the setting of optimal frequency was evaluated to calculate the time required for equilibration of etCO2.

Results

A module automating the initial settings of the ventilator according to local ICU rules is realized. Modules were added that optimize breathing frequency with respect to PaCO2/etCO2 and FiO2 according to SO2 whenever no PaO2 is available. A lung simulator (Michigan Instruments Inc., Grand Rapids, MI, USA) connected with the LS4000 (Dräger Medical) was used to evaluate the system. Exemplary results are presented in the figures, which show the minute volume/etCO2 relationship (Figure 1) and a parametric fit of etCO2 data (Figure 2). The adjustment of the frequency is based on the current etCO2 model.

Figure 1
figure1

Minute volume/etCO2 relationship.

Figure 2
figure2

Parametric fit of etCO2 data.

Conclusion

Automation is a 'sine qua non' to achieve optimal patient individualized ventilation support. Our system is enabled to evaluate a therapeutic strategy and to base the settings of the ventilator on current trends/drifts observed in the data.

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Lozano, S., Moeller, K., Stahl, C. et al. AUTOPILOT-BT: an approach towards automatic mechanical ventilation. Crit Care 11, P166 (2007). https://0-doi-org.brum.beds.ac.uk/10.1186/cc5326

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Keywords

  • Cognitive Load
  • Arterial Oxygen Saturation
  • Respiratory Signal
  • Breathing Frequency
  • Optimal Patient