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

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...

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Autores principales: Roué, Jean-Michel, Morag, Iris, Haddad, Wassim M, Gholami, Behnood, Anand, Kanwaljeet J S
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BMJ Publishing Group 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7789448/
https://www.ncbi.nlm.nih.gov/pubmed/33408199
http://dx.doi.org/10.1136/bmjopen-2020-039292
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author Roué, Jean-Michel
Morag, Iris
Haddad, Wassim M
Gholami, Behnood
Anand, Kanwaljeet J S
author_facet Roué, Jean-Michel
Morag, Iris
Haddad, Wassim M
Gholami, Behnood
Anand, Kanwaljeet J S
author_sort Roué, Jean-Michel
collection PubMed
description 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).
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spelling pubmed-77894482021-01-14 Using sensor-fusion and machine-learning algorithms to assess acute pain in non-verbal infants: a study protocol Roué, Jean-Michel Morag, Iris Haddad, Wassim M Gholami, Behnood Anand, Kanwaljeet J S BMJ Open Paediatrics 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). BMJ Publishing Group 2021-01-06 /pmc/articles/PMC7789448/ /pubmed/33408199 http://dx.doi.org/10.1136/bmjopen-2020-039292 Text en © 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. http://creativecommons.org/licenses/by-nc/4.0/ http://creativecommons.org/licenses/by-nc/4.0/This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, appropriate credit is given, any changes made indicated, and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/.
spellingShingle Paediatrics
Roué, Jean-Michel
Morag, Iris
Haddad, Wassim M
Gholami, Behnood
Anand, Kanwaljeet J S
Using sensor-fusion and machine-learning algorithms to assess acute pain in non-verbal infants: a study protocol
title Using sensor-fusion and machine-learning algorithms to assess acute pain in non-verbal infants: a study protocol
title_full Using sensor-fusion and machine-learning algorithms to assess acute pain in non-verbal infants: a study protocol
title_fullStr Using sensor-fusion and machine-learning algorithms to assess acute pain in non-verbal infants: a study protocol
title_full_unstemmed Using sensor-fusion and machine-learning algorithms to assess acute pain in non-verbal infants: a study protocol
title_short Using sensor-fusion and machine-learning algorithms to assess acute pain in non-verbal infants: a study protocol
title_sort using sensor-fusion and machine-learning algorithms to assess acute pain in non-verbal infants: a study protocol
topic Paediatrics
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7789448/
https://www.ncbi.nlm.nih.gov/pubmed/33408199
http://dx.doi.org/10.1136/bmjopen-2020-039292
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