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

Acute Pain

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.

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Conditions de participation

  • Sexe:

    ALL

  • Âges admissibles:

    27 to 33

Critères de participation

* 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

Lieu de l'étude

Mount Sinai Hospital
Mount Sinai Hospital
Toronto, Ontario
Canada

Contactez l'équipe d'étude

Backup Contact

Carol Cheng, MSc

[email protected]
416-586-4816
Primary Contact

Vibhuti Shah, MD

[email protected]
416-586-4816
Étude parrainée par
York University
Participants recherchés
Plus d'informations
ID de l'étude: NCT05579496