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

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.

Study Overview

• Status: Recruiting
• Conditions: Acute Pain

Detailed 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).

• Study Type: Observational
• Enrollment (Anticipated): 400

Contacts and Locations

• Study Contact: Name: Rebecca Pillai Riddell, PhD; Phone Number: 416736200; Email: rpr@yorku.ca
• Study Contact Backup: Name: Lorenzo Fabrizi, PhD; Phone Number: 51888) 02031081888; Email: l.fabrizi@ucl.ac.uk

Study Locations

• Canada
  • Ontario
    • Toronto, Ontario, Canada, M5G 1X5
      • Recruiting
      • Mount Sinai Hospital
      • Contact: Vibhuti Shah, MD; Phone Number: 416-586-4816; Email: vibhuti.shah@sinaihealthsystem.ca
      • Contact: Carol Cheng, MSc; Phone Number: 416-586-4816; Email: carol.cheng@sinaihealthsystem.ca
• United Kingdom
  • No Province
    • London, No Province, United Kingdom, N1 2EP
      • Recruiting
      • University College London Hospital
      • Contact: Judith Meek, MD; Phone Number: 020 3447 8094; Email: judith.meek@nhs.net
      • Contact: Pureza Laudano-Dray, BScN; Phone Number: 020 3447 8094; Email: Pureza m.laudiano-dray@ucl.ac.uk

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: 6 months to 7 months (CHILD)
• Accepts Healthy Volunteers: No
• Genders Eligible for Study: All

• Sampling Method: Non-Probability Sample

Study Population

Preterm infants

Description

• 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

Study Plan

How is the study designed?

Number of groups / cohorts

1

Cohorts and Interventions

Group / Cohort
Infants Hospitalized in the NICU; Infants born between 28 0/7 weeks 32 6/7 weeks gestational age, who are within 6 weeks postnatal age, and their caregiver and/or health professional will be recruited for qualitative interview.

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
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)
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)
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)
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 Outcome Measures

Outcome Measure	Measure Description	Time Frame
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.

Collaborators and Investigators

• Sponsor: York University
• Collaborators: University College London Hospitals; University College, London; McMaster University; University of Calgary; MOUNT SINAI HOSPITAL
• Investigators: Principal Investigator: Rebecca Pillai Riddell, PhD, York University/Mount Sinai Hospital

Study record dates

Study Major Dates

• Study Start (ACTUAL): November 1, 2020
• Primary Completion (ANTICIPATED): December 1, 2025
• Study Completion (ANTICIPATED): December 1, 2026

Study Registration Dates

• First Submitted: December 24, 2020
• First Submitted That Met QC Criteria: October 11, 2022
• First Posted (ACTUAL): October 13, 2022

Study Record Updates

• Last Update Posted (ACTUAL): October 13, 2022
• Last Update Submitted That Met QC Criteria: October 11, 2022
• Last Verified: October 1, 2022

More Information

Terms related to this study

• Keywords: Artificial Intelligence; Pain Assessment; NICU (Neonatal Intensive Care Unit)
• Additional Relevant MeSH Terms: Pain; Neurologic Manifestations; Acute Pain
• Other Study ID Numbers: 19-0252-A

Plan for Individual participant data (IPD)

• Plan to Share Individual Participant Data (IPD)?: NO

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No