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

Study Overview
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Brief Summary
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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.

Detailed Description
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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).

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

Conditions
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Acute Pain

Participation Criteria
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Eligibility Criteria
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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

Ages Eligible for Study
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Minimum Age: 27 Weeks
Maximum Age: 33 Weeks

Sexes Eligible for Study
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All

Accepts Healthy Volunteers
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No

Study Plan
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How is the study designed?
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Arms and Interventions

| Participant Group/Arm | Intervention/Treatment |
| --- | --- |
| Infants Hospitalized in the NICU<br>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?
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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. | 

 Terms related to the study
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Keywords Provided by Centre Hospitalier Valida
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NICU (Neonatal Intensive Care Unit), Artificial Intelligence, Pain Assessment