Anesthesia care providers routinely deliver supplemental oxygen during monitored anesthesia care to prevent hemoglobin desaturation. The existing method of delivery, however, contributes to complications including on-patient fires and undetected respiratory depression. On-patient operating room fires occur approximately 600 times per year in the U.S. and may result in severe head and neck burns. Respiratory depression occurs in < 1% of patients but may lead to brain damage and death. Patient variability and pulse oximetry limitations make measuring saturation using existing methods difficult. Maintaining sufficient levels of saturation is challenging because the response of a given individual to a particular oxygen flow rate is unpredictable. The accuracy of pulse oximetry, which ranges from +/-2% to 4%, limits its utility as an indicator of alveolar oxygen concentration. Delivering oxygen and measuring hemoglobin saturation with existing methods may be difficult but an alternative approach could overcome difficulties and customize oxygen.
We propose to develop a model-based demand delivery method that measures and maintains hemoglobin saturation during monitored anesthesia care. Our model will predict saturation by generating patient-specific oxyhemoglobin dissociation and simulating breath-by-breath changes in alveolar oxygen concentration. Our model must automatically adapt to patient variability. Consequently, we also propose developing an automated oxygen delivery system. The system will supply oxygen on-demand thereby reducing fire hazard and facilitating respiratory monitoring.
Our demand delivery system will increase detection of respiratory depression while reducing on-patient fire hazard. Our model based delivery method will provide insight into which factors contribute to desaturation. Characterizing patient specific oxyhemoglobin dissociation will provide physiological insight into the factors contributing to patient variability. The results of this proposal will lead to a working prototype oxygen delivery system. We could test the model-based system further and eventually use the system clinically to monitor respiratory rate, reduce fire hazard, and deliver patient-specific oxygen flow rates.