Rebooting Infant Pain Assessment: Using Machine Learning to Exponentially Improve Neonatal Intensive Care Unit Practice
Overview
- Phase
- Not Applicable
- Intervention
- Not specified
- Conditions
- Acute Pain
- Sponsor
- York University
- Enrollment
- 400
- Locations
- 2
- Primary Endpoint
- Cortical Correlate of Distress
- Status
- Recruiting
- Last Updated
- 3 years ago
Overview
Brief 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.
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).
Investigators
RRiddell
Full Professor
York University
Eligibility Criteria
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.
Outcomes
Primary Outcomes
Cortical Correlate of Distress
Time Frame: For 2 hours surrounding Painful procedure (time locked to heel lance; approximately 1 hour before to 1 hour after heel lance)
To be analyzed using machine learning via bedside monitoring: Continuous EEG data capture
Oxygen Saturation Correlate of Distress
Time Frame: Over 2 hours surrounding Painful procedure (time locked to heel lance; approximately 1 hour before to 1 hour after heel lance)
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
Cardiac Correlates of Distress
Time Frame: Over 2 hours surrounding Painful procedure (time locked to heel lance)
To be analyzed using machine learning via bedside monitoring: Heart Rate, Heart Rate Variability
Behavioural Correlate of Distress
Time Frame: 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)
To be analyzed using machine learning via bedside videography: Facial Grimacing using Neonatal Facial Coding System(NFCS-P subset; Bucsea et al., in preparation)
Secondary Outcomes
- Semi-Structured Interview(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.)