Grant: Collaborative Health Research Projects (NSERC partnered)

Unmanaged pain in hospitalized infants has serious long term complications. However, to manage pain, one must have accurate infant pain assessment. Infants cannot self-report their pain and current infant pain assessment tools used by health professionals have major problems because: 1) Different pain indicators (heart rate, oxygen levels, facial activity) are treated the same in measures, with no attempts to optimize them to improve pain assessment (e.g. weighting them differently). 2) None of the "gold standard" tools can discriminate distress from invasive procedures (like a heel lance) from distress due to non-invasive procedures (like a soiled diaper). 3) Caregivers can have biases that impact their pain judgments. Our international team of knowledge users and health/natural science/engineering/social science researchers have come together to build a machine learning algorithm that will learn how to discriminate invasive and non-invasive distress. A sample of 300 preterm infants and their mothers will be followed during 2 painful procedures (heel lance) approximately 1-week apart. Pain indicators (facial grimacing, heart rate, brain electrical activity, oxygen levels) during the first painful procedure will be used to train the algorithm to discriminate between the different types of distress during the second procedure. Our team will also measure the infant heart rate and oxygen level patterns longer before the painful procedure to better contextualize infant responses during and after the painful procedure. Also, this will be the first time brain activity is being considered in a clinical tool. We assert this increases our ability to discriminate between invasive and non-invasive distress. The complexity of pain requires a machine learning solution that is capable of modelling individual patterns of brain, behaviour, and physiology during pain. In essence, this will finally allow preterm infants to 'self-report' pain for themselves.