Using sensor-fusion and machine-learning algorithms to assess acute pain in non-verbal infants: a study protocol

Jean-Michel Roué, Iris Morag, Wassim M Haddad, Behnood Gholami, Kanwaljeet J S Anand, Jean-Michel Roué, Iris Morag, Wassim M Haddad, Behnood Gholami, Kanwaljeet J S Anand

Abstract

Introduction: Objective pain assessment in non-verbal populations is clinically challenging due to their inability to express their pain via self-report. Repetitive exposures to acute or prolonged pain lead to clinical instability, with long-term behavioural and cognitive sequelae in newborn infants. Strong analgesics are also associated with medical complications, potential neurotoxicity and altered brain development. Pain scores performed by bedside nurses provide subjective, observer-dependent assessments rather than objective data for infant pain management; the required observations are labour intensive, difficult to perform by a nurse who is concurrently performing the procedure and increase the nursing workload. Multimodal pain assessment, using sensor-fusion and machine-learning algorithms, can provide a patient-centred, context-dependent, observer-independent and objective pain measure.

Methods and analysis: In newborns undergoing painful procedures, we use facial electromyography to record facial muscle activity-related infant pain, ECG to examine heart rate (HR) changes and HR variability, electrodermal activity (skin conductance) to measure catecholamine-induced palmar sweating, changes in oxygen saturations and skin perfusion, and electroencephalography using active electrodes to assess brain activity in real time. This multimodal approach has the potential to improve the accuracy of pain assessment in non-verbal infants and may even allow continuous pain monitoring at the bedside. The feasibility of this approach will be evaluated in an observational prospective study of clinically required painful procedures in 60 preterm and term newborns, and infants aged 6 months or less.

Ethics and dissemination: The Institutional Review Board of the Stanford University approved the protocol. Study findings will be published in peer-reviewed journals, presented at scientific meetings, taught via webinars, podcasts and video tutorials, and listed on academic/scientific websites. Future studies will validate and refine this approach using the minimum number of sensors required to assess neonatal/infant pain.

Trial registration number: ClinicalTrials.gov Registry (NCT03330496).

Keywords: neonatology; paediatrics; pain management.

Conflict of interest statement

Competing interests: J-MR reports an international mobility scholarship from Chiesi Pharmaceuticals; WMH and BG have equity ownership in Autonomous Healthcare; WMH, BG and KJSA have proprietary interests in the potential devices that may be developed from these studies. Some equipment used in this study was provided by Autonomous Healthcare.

© Author(s) (or their employer(s)) 2021. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.

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