A model to simulate human cardio-respiratory responses to airway obstruction.

Airway obstruction is the second leading cause of potentially survivable death on the battlefield. Managing airway obstruction resulting from severe traumatic injuries to the head and neck, which can distort the airway anatomy, poses significant challenges to combat medics. The medic's ability to make quick and effective interventions to secure the airway in austere, tactical environments is also highly dependent on their training and experience as well as the availability of advanced medical equipment. Artificial intelligence (AI) algorithms can help augment the competency and capability of medics to care for combat casualties by enhancing their training, assessing their skills, and helping identify the most appropriate medical interventions in real time that are likely to result in desired clinical outcomes. However, the training and assessment of AI algorithms require massive amounts of real-world, vital-sign data. Because such data are not currently available for casualties with airway obstruction, an alternative approach is to rely on relevant synthetic data generated by high-fidelity computational physiological models. Here, by adding new respiratory control and respiratory mechanics components, we extended our previously developed and validated human cardio-respiratory (CR) model for representing hemorrhagic injury to account for the physiological effects of airway obstruction on vital signs. We calibrated and validated the extended CR model using data from six human studies and two pig studies, which reported vital-sign changes in response to airway obstruction, changes in arterial oxygen (O