SBIR Phase I: Objective Assessment of Agitation and Sedation

This Small Business Innovation Research Phase I project is will investigate the feasibility of developing an objective agitation and sedation assessment algorithm for patients in the intensive care unit (ICU). Use of machine learning to identify clinically relevant information to characterize the agitation and sedation state of the patients is investigated. Furthermore, a plan to ?calibrate? and validate the agitation and sedation score provided by the algorithm will be designed. Agitation and sedation assessment is a challenging problem for patients undergoing critical care. Agitation, which is primarily characterized by excessive gross motor movement, is experienced by 74% of adults during ICU stay. Agitated patients may do physical harm to themselves by dislodging vital life support and monitoring devices with excessive musculoskeletal activity. Oversedation increases risk to the patient since liberation from mechanical ventilation may not be possible due to a diminished level of consciousness and respiratory depression from sedative. Currently, the assessment is performed by the clinical staff and no technology exists for such assessment. It is anticipated that through this research, novel algorithms for reliable detection of a patient?s agitation and sedation state using their physiological signals will be developed. The broader impact/commercial potential of this project includes reduction in clinical staff workload and healthcare costs. Current clinical practice in patient critical care requires the nursing staff to assess the patient's agitation and sedation state and provide sedatives to ameliorate the patient's agitation. The process relies on subjective assessments and may result in oversedation, which in turn increases the number of interventions, length of mechanical ventilation, and duration of stay in the ICU, and hence, increases healthcare costs. Development of an objective agitation and sedation assessment system can have a great impact on the quality of care in a critical care setting. Such a system can enable continuous patient monitoring and increase quality of care. Currently, clinical staff need to attend to multiple patients and continuous monitoring of patients is not feasible. In addition, in the absence of an automated agitation and sedation assessment algorithm, early indications of undersedation or oversedation can be overlooked due to the complex nature of the patient critical care problem and clinical staff's workload.