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

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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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).

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Age

27 - 33

Gender

All

NCT ID

NCT05579496

Phase

-1

Status

Recruiting Now

Medical Condition

Pain

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Medical Condition

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The Study

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About Clinical Trials

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Your Journey

01
Receiving the medication

You would receive etanercept (Enbrel) twice a week for 12 weeks and then once a week for 12 weeks. Etanercept, as well as study related medical care, is provided at no cost.

02
Visiting the study site

Study participation involves approximately 8 visits to your local study center over 6 to 7 months.

03
Follow-up

There would also be a follow-up telephone call 30days after completing the study. No visits are required after participation is complete.

Call 1800-9860-568 now to find out if you are eligible.