Overview
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).
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
