Rebooting Infant Pain Assessment: Using Machine Learning to Exponentially Improve Neonatal Intensive Care Unit Practice

Acute Pain Clinical Trial

— BabyAI  

Official title:

Rebooting Infant Pain Assessment: Using Machine Learning to Exponentially Improve Neonatal Intensive Care Unit Practice

Clinical Trial Details — Status: Recruiting

Administrative data

• NCT number: NCT05579496
• Other study ID #: 19-0252-A
• Status: Recruiting
• Start date: November 1, 2020
• Est. completion date: December 2026

Study information

• Verified date: October 2022
• Source: York University
• Contact: Rebecca Pillai Riddell, PhD
• Phone: 416736200
• Email: rpr[@]yorku.ca
• Is FDA regulated: No
• Study type: Observational

Clinical Trial Summary

A multi-national multidisciplinary team will be working collaboratively to build a machine learning algorithm to distinguish between preterm infant distress states in the Neonatal Intensive Care Unit.

Description:

Unmanaged pain in hospitalized infants has serious long-term complications. 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 400 preterm infants (300 from Mount Sinai Hospital and 100 from University College London Hospital [UCLH]) and their mothers will be followed during a routine painful procedure (heel lance). Pain indicators (facial grimacing [behavioural indicators], heart rate, oxygen saturation levels [physiologic indicators], brain electrical activity) during the painful procedure will be used to train the algorithm to discriminate between different types of distress (pain-related and non-pain related).

Recruitment information / eligibility

• Status: Recruiting
• Enrollment: 400
• Est. completion date: December 2026
• Est. primary completion date: December 2025
• Accepts healthy volunteers: No
• Gender: All
• Age group: 27 Weeks to 33 Weeks

Eligibility

• QUALITATIVE INTERVIEWS
• Inclusion Criteria:
• parents of a child currently in the NICU or
• health professionals currently working in the NICU.
• Exclusion Criteria:
• Participants who cannot communicate fluently in English
• QUANTITITATIVE DATA CAPTURE (video, eeg, ecg, SPo2)
• Inclusion Criteria:
• Infants born between 28 0/7 weeks 32 6/7 weeks gestational age
• Infants who are within 6 weeks postnatal age
• Infants who are undergoing a routine heel lance
• Exclusion Criteria:
• Infants with congenital malformations
• Infants receiving analgesics or sedatives at the time of study (aside from sucrose),
• Infants with history of perinatal hypoxia/ischemia at the time of study.
• Infants with diaper rash or excoriated buttocks

Related Conditions & MeSH terms

• Acute Pain

Locations

Country	Name	City	State
Canada	Mount Sinai Hospital	Toronto	Ontario
United Kingdom	University College London Hospital	London	No Province

Sponsors (6)

Lead Sponsor	Collaborator
York University	McMaster University, MOUNT SINAI HOSPITAL, University College London Hospitals, University College, London, University of Calgary

Countries where clinical trial is conducted

Canada,  United Kingdom, 

Outcome

Type	Measure	Description	Time frame	Safety issue
Primary	Behavioural Correlate of Distress	To be analyzed using machine learning via bedside videography: Facial Grimacing using Neonatal Facial Coding System(NFCS-P subset; Bucsea et al., in preparation)	NFCS-P coded in 1-5 minute epochs, over 2 hour surrounding painful procedure (time locked to heel lance; approximately 1 hour before to 1 hour after heel lance)
Primary	Cortical Correlate of Distress	To be analyzed using machine learning via bedside monitoring: Continuous EEG data capture	For 2 hours surrounding Painful procedure (time locked to heel lance; approximately 1 hour before to 1 hour after heel lance)
Primary	Cardiac Correlates of Distress	To be analyzed using machine learning via bedside monitoring: Heart Rate, Heart Rate Variability	Over 2 hours surrounding Painful procedure (time locked to heel lance)
Primary	Oxygen Saturation Correlate of Distress	To be analyzed using machine learning via bedside monitoring: amount of oxygen-carrying hemoglobin in the blood relative to the amount of hemoglobin not carrying oxygen	Over 2 hours surrounding Painful procedure (time locked to heel lance; approximately 1 hour before to 1 hour after heel lance)
Secondary	Semi-Structured Interview	Health Professionals and Caregivers will be asked about their thoughts on using AI for infant pain assessment	These interviews are occurring at the beginning of the study and will be qualitatively analyzed. They are not linked to infants whose data we are collecting primary outcomes.