To address the current limitations related to infant pain assessment in the NICU, 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. Furthermore, to improve the use of current pain management practices, our team seeks to better understand the developmental mechanisms underlying skin-to-skin contact over time and factors that may influence its efficacy in mitigating pain responses in preterm infants. This is an ongoing naturalistic observational study.
Eligibility
Sex
ALL
Min age
25 Weeks
Max age
33 Weeks
Healthy volunteers
No
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, RR, SPo2)
Inclusion Criteria:
* Infants born between 25 0/7 weeks 32 6/7 weeks gestational age
* Infants who are within 8 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
* Parents who are not fluent in English
Primary outcome measure(s)
Behavioural Correlate of Distress — 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., 2022, 10.1097/j.pain.0000000000002798)
Cortical Correlate of Distress — 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
Cardiac Correlates of Distress — 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
Oxygen Saturation Correlate of Distress — 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
Respiration Rate 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: Respiratory patterns
This page summarises publicly available registry data for informational purposes — not medical advice. Eligibility is determined by each study team; patients should discuss participation with their clinician.
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