Robust closed-loop control of propofol administration using WAVCNS index as the controlled variable.

Area of Science:

• Anesthesiology
• Biomedical Engineering
• Control Systems

Background:

• Accurate control of hypnosis depth is crucial for patient safety during anesthesia.
• Existing closed-loop systems often struggle with inter-individual variability and surgical disturbances.
• Electroencephalogram (EEG)-derived indices offer objective measures of hypnotic state.

Purpose of the Study:

• To develop and evaluate a robust closed-loop control strategy for propofol administration to regulate hypnosis depth.
• To enhance controller stability and robustness by incorporating pharmacokinetic (PK) and pharmacodynamic (PD) variability.
• To ensure reliable performance across diverse patient populations and surgical conditions.

Main Methods:

• Utilized an EEG-derived WAVCNS index to monitor hypnosis depth.
• Implemented a closed-loop control system manipulating intravenous propofol infusion.
• Modeled patient PK/PD variability and simulated responses to surgical stimuli for controller design and evaluation.
• Validated the controller using 44 simulated patient models derived from clinical data.

Main Results:

• The proposed controller demonstrated robust performance in maintaining target hypnosis levels.
• The system effectively managed induction and maintenance phases across a wide range of simulated patient PK/PD profiles.
• The controller showed stability and tolerance to simulated unpredictable surgical stimuli.

Conclusions:

• The developed closed-loop control strategy offers a reliable method for precise hypnosis management.
• Accounting for individual PK/PD variability significantly improves controller robustness and adaptability.
• This approach holds promise for enhancing anesthetic delivery and patient outcomes.