NCT05579496

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

• Status: Recruiting
• Phase: —
• Study type: Observational
• Enrollment: 400 (estimated)
• Sponsor: York University · Academic / Other
• Sex: All
• Age: 27 Weeks – 33 Weeks
• Healthy volunteers: Not accepted

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).

Conditions

• Acute Pain

Timeline

• Start date: 2020-11-01
• Primary completion: 2025-12-01
• Completion: 2026-12-01
• First posted: 2022-10-13
• Last updated: 2022-10-13

Locations

2 sites across 2 countries: Canada, United Kingdom