While mechanical ventilators provide life-saving respiratory support, prolonged acute mechanical ventilation (PAMV) can lead to severe complications (e.g. , pneumonia) and increased healthcare costs - predicted to be over $32 billion in 2020 and accounting for over 10% of all hospital costs. Thus, mechanical ventilation needs to be discontinued as early as possible, often by using a process known as weaning. However, the best approach to weaning remains an open question and is subject to controversy, where estimated 170,000 preventable deaths per year in US intensive care units are a result of inappropriate ventilator weaning. Consequently, the Emergency Care Research Institute (ECRI) lists improper ventilator settings as a Top 10 Health Technology Hazard in 2019. Autonomous mechanical ventilation weaning exemplifies a medical cyber-physical systems (MCPS) that requires collections of interconnected medical devices (e.g., ventilators and patient monitors) that are coordinated for treating a patient (i.e. , performing safe autonomous weaning). This proposal aims to develop fundamental advances in safe and effective data-driven context-aware human-in-the-loop control that will enable autonomous mechanical ventilation weaning . While closed-loop control and data-driven techniques (e.g., system identification and machine learning) have been applied to MCPS, assuring the safety and reliability of using data-driven components that adapt in-the-loop with a human operator remains a challenge. We will demonstrate the impact of our closed-loop design and analysis techniques in autonomous MCPS for mechanical ventilation weaning . The proposed project directly aligns with the mission of the National Institute of Biomedical Imaging and Bioengineering (NIBIB) through the research and development of new technologies to advance medical care through medical device interoperability and clinical decision support for enabling autonomous mechanical ventilation weaning. Also relevant to the NIBIB mission, achieving the aims of this project requires a multidisciplinary approach and complementary advances in computer science and clinical care. From the healthcare perspective, this project will offer evidence-based and personalized decision support to caregivers for better mechanical ventilation weaning. The long-term impact of autonomous mechanical ventilation weaning pain will lead to shorter hospital stays, reduce the economic burden of health care, and reduce the deadly side effects of inappropriate weaning.
This project concerns development of technologies to quantify and enable autonomous mechanical ventilation weaning , where, currently, the best approach to weaning remains an open question and is subject to controversy. An estimated 170,000 preventable deaths per year in US intensive care units are a result of inappropriate ventilator weaning . Consequently, the Emergency Care Research Institute (ECRI) lists improper ventilator settings as a Top 10 Health Technology Hazard in 2019.
