Physicians face enormous challenges in making decisions to resuscitate and stabilize critically ill and injured patients based on rapidly changing vital signs. This project will investigate automated processing of physiological measurements, using high-speed computational models to provide near-instantaneous treatment recommendations to stabilize blood pressure, cardiac output and renal function. Additionally, feedback control algorithms will be developed capable of automatically regulating the administration of drugs and fluids to optimize critical care, subject to the specific physiological state of the patient. This advance in continually monitored and adjusted personalized treatment is expected to benefit patients suffering from trauma, burn, infection, and shock. The resulting decision assistance system and adaptive closed-loop drug and fluid delivery system will greatly benefit the accuracy and reliability of critical care treatments, resulting in increased survival rates and improved therapeutic outcomes.
The project will develop adaptive models of cardio-vascular and fluid response, subject to the injection of vasoactive drugs and fluid therapy. The corresponding drug and fluid administration problem is challenged by a changing physiological dynamic response and a significant time-delay in the response due to drug absorption. Multi-model observers will be investigated to provide instantaneous dosage recommendations to doctors to achieve targeted values of blood pressure, cardiac output and urinary output. The models will compute the patient's responsiveness to various drugs and fluids, and will self-adapt to varying responses to treatment from patient-to-patient and within a single patient over time (intra-patient and inter-patient variability). Detection algorithms will be developed to identify potential sudden changes in the patient's physiological response, such as the presence of an internal hemorrhage, and alert the doctors. Additionally, model-based adaptive and robust closed-loop drug infusion algorithms will be developed to automate the drug administration process for optimized patient resuscitation. The research team will collaborate with medical experts that will provide physiological data from animal experiments and will assist in the evaluation of the developed models and decision support algorithms.
