The novel Coronavirus (COVID-19) is one of four infectious diseases caused by the SARS-CoV-2 virus. Although the clinical signs and patient symptoms of this complicated disease vary in presentation and severity, clinicians and investigators have reported constitutional symptoms (cough and fever), upper and lower respiratory tract symptoms, as well as gastrointestinal symptoms. Among the most concerning is the life threatening acute respiratory distress syndrome (ARDS) in patients. The pathophysiology of severe ARDS results from a rapid decline in pulmonary function and requires intubation of patients in critical condition for invasive mechanical ventilation to combat lung recruitability, reduced peripheral capillary oxygen saturation (SpO2) and risks of organ failure and death. Ventilator settings to increase SpO2 and oxygen delivery is achieved with positive end-expiratory pressure (PEEP). However, controlling ventilation at a high PEEP for extended periods of time significantly increases risk for ventilator-associated lung injury (VALI). This RAPID project will develop novel engineering strategies for optimal ventilator control to maximize SpO2 in minimal time, while minimizing PEEP and the duration of ventilator use are needed to minimize VALI and subsequent complications, and to improve favorable patient outcomes. In the management of patients with COVID-19, these strategies are significant to optimize oxygen delivery, minimal invasive ventilator use and mechanical lung injury. Further, the understanding of ventilator requirements and operative settings highlights the need for available ventilators. The management of severe ARDS is complicated and strategies and protocols are desperately needed.
To achieve this goal, we will develop data-driven linear parameter-varying (LPV) dynamical systems models that relate patient clinical state and ventilator inputs to the output variable patient SpO2. Patient state will be characterized using data from the electronic health record (EHR) and minute-by-minute physiological time-series (PTS) data (e.g., heart rate, respiratory rate, SpO2) acquired from patient monitoring. We will first develop the LPV model using retrospective data from non-COVID-19 patients who are on ventilators to help treat conditions such as pneumonia and ARDS. Then, we will test the predictive capabilities of the LPV model in COVID-19 patients who are placed on ventilators. Finally, we will develop an optimal ventilator control strategy for COVID-19 patients to regulate SpO2 levels in ICU patients based on the LPV model. Attempting to control a complex biological system using control strategies based on mechanistic models is generally intractable. However, the LPV framework allows for sophisticated optimal strategies to be implemented that not only allow for better performance than other classical methods, but also provides stability and performance guarantees.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
