Grant: Collaborative Health Research Projects (NSERC partnered)

Critically ill children are at risk of cardio-pulmonary arrest (CPA). These devastating events occur 300-700 times per year in Canadian pediatric intensive care units (PICUs) and are associated with high mortality (45 to 85%), disability and higher costs of care. Effective management of CPA requires immediate reaction from clinicians. However, changes in a patient's state that precede the CPA are subtle. If these were appreciated, interventions would be possible, potentially preventing CPA and the associated poor outcomes and costs. The objective of this project is to use artificial intelligence to develop and implement a tool to predict CPA in critically ill children. We have been collecting and storing continuous physiologic signals in our PICU since April 2013. We have undertaken preliminary work using machine learning to analyze these data in patients who suffered a CPA. Our results indicate that earlier prediction of CPA is possible. We will use funding from this grant to: 1) Further validate, train and test the model predicting CPA, utilizing multidisciplinary expertise in a team that includes clinicians, engineers, computer scientists, and 2) Translate the model to the bedside using a visualization platform and implementation plan developed with human factors engineers and ethicists. This project is part of the new field of predictive, personalized medicine, and is associated with novel ethical dilemmas related to communicating the significance of predictions to patients and their families, which we will explore and define. An accurate model for CPA will save the lives of critically ill Canadian children and reduce the burden associated with these events. Its development will create opportunities for Canadian researchers to extend predictive modelling and redefine the healthcare team. This initiative will reduce healthcare costs, and positions Canada as a leader in using artificial intelligence to solve crucial healthcare problems.