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Rebooting Infant Pain Care: Using Machine Learning and Skin-to-Skin Contact to Exponentially Improve Neonatal Intensive Care Unit Practice

Study acronym: BabyAIBabyCalm
StatusRecruiting
PhaseNot specified
Started
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Record History

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  1. Notable Primary completion date: Dec 2025 to Mar 2030v2 Show 8 more changesShow less
    • Primary endpoints: 4 to 5
    • Study completion date: Dec 2026 to Mar 2031
    • Eligibility criteria expanded, about 8 words
    • Secondary endpoints: 1 to 6
    • Study description expanded, about 200 words
    • Site contacts updated at 2 of 2 sites
    • Collaborators: 5 to 3
    • Study title expanded, about 3 words
  2. Minor Study First Submitted
  • Stopped: an ongoing trial was terminated, suspended or withdrawn.
  • Notable: the trial’s plan changed such as a big move in its timeline or size, a new primary endpoint, sponsor or phase, or recruiting opened or resumed.
  • Minor: routine progress and housekeeping, such as sites added or enrollment closed.
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.
Trial Details
NCT Number NCT05579496
Lead Sponsor York University
Collaborators: MOUNT SINAI HOSPITAL, University College, London, University College London Hospitals
Conditions
(from sponsor)
Acute Pain
Related terms Acute Pain, Agnosia
Enrollment 400 participants
Start Date
Primary Completion (estimated)
Study Completion (estimated)
Updated on ClinicalTrials.gov