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| Name | Class |
|---|---|
| MOUNT SINAI HOSPITAL | OTHER |
| University College, London | OTHER |
| University College London Hospitals | OTHER |
| University of Calgary |
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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.
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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| Label | Type | Description | Intervention Names |
|---|---|---|---|
| Infants Hospitalized in the NICU | Infants born between 28 0/7 weeks 32 6/7 weeks gestational age, who are within 6 weeks postnatal age, and their caregiver and/or health professional will be recruited for qualitative interview. |
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| Measure | Description | Time Frame |
|---|---|---|
| Behavioural Correlate of Distress | To be analyzed using machine learning via bedside videography: Facial Grimacing using Neonatal Facial Coding System(NFCS-P subset; Bucsea et al., in preparation) | NFCS-P coded in 1-5 minute epochs, over 2 hour surrounding painful procedure (time locked to heel lance; approximately 1 hour before to 1 hour after heel lance) |
| Cortical Correlate of Distress | To be analyzed using machine learning via bedside monitoring: Continuous EEG data capture | For 2 hours surrounding Painful procedure (time locked to heel lance; approximately 1 hour before to 1 hour after heel lance) |
| Cardiac Correlates of Distress | To be analyzed using machine learning via bedside monitoring: Heart Rate, Heart Rate Variability | Over 2 hours surrounding Painful procedure (time locked to heel lance) |
| Oxygen Saturation Correlate of Distress | To be analyzed using machine learning via bedside monitoring: amount of oxygen-carrying hemoglobin in the blood relative to the amount of hemoglobin not carrying oxygen | Over 2 hours surrounding Painful procedure (time locked to heel lance; approximately 1 hour before to 1 hour after heel lance) |
| Measure | Description | Time Frame |
|---|---|---|
| Semi-Structured Interview | Health Professionals and Caregivers will be asked about their thoughts on using AI for infant pain assessment | These interviews are occurring at the beginning of the study and will be qualitatively analyzed. They are not linked to infants whose data we are collecting primary outcomes. |
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Preterm infants
| Name | Role | Phone | Extension | |
|---|---|---|---|---|
| Rebecca Pillai Riddell, PhD | Contact | 416736200 | rpr@yorku.ca | |
| Lorenzo Fabrizi, PhD | Contact | 02031081888 | 51888) | l.fabrizi@ucl.ac.uk |
| Name | Affiliation | Role |
|---|---|---|
| Rebecca Pillai Riddell, PhD | York University/Mount Sinai Hospital | Principal Investigator |
| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| Mount Sinai Hospital | Recruiting | Toronto | Ontario | M5G 1X5 | Canada |
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| ID | Term |
|---|---|
| D059787 | Acute Pain |
| ID | Term |
|---|---|
| D010146 | Pain |
| D009461 | Neurologic Manifestations |
| D012816 | Signs and Symptoms |
| D013568 | Pathological Conditions, Signs and Symptoms |
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| OTHER |
| McMaster University | OTHER |
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| University College London Hospital | Recruiting | London | No Province | N1 2EP | United Kingdom |
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