Preterm infant discomfort during intensive care Neonatal Diseases
Conditions
Interventions
The study uses computer learning to evaluate a video of the baby during routine procedures and learns as more cases are observed.
No changes to routine care are involved. Participatin
Sponsors
Newcastle upon Tyne Hospitals NHS Foundation Trust
Eligibility
Sex/Gender
All
Inclusion criteria
Inclusion criteria: 1. Born at <36 weeks gestation 2. Medically stable 3. Signed parental consent
Exclusion criteria
Exclusion criteria: 1. Infants with significant brain, spine or facial congenital abnormality 2. Parents unwilling to provide consent 3. Infants with postmenstrual age >36 weeks
Design outcomes
Primary
| Measure | Time frame |
|---|---|
| How well the final learning model performs, assessed using machine learning metrics (confusion matrix, accuracy, precision, recall/sensitivity, F1 score, specificity and area under the curve) at the time of the video recording | — |
Secondary
| Measure | Time frame |
|---|---|
| 1. What signal or mixture of signals is most informative of preterm babies’ state (face, body, sound and physiological data) at the time of the video 2. What algorithms are best suited for each data stream and why 3. Which methods are best for combining different data streams to make the most accurate estimation of preterm babies’ comfort levels 4. Are any of these factors different for some babies e.g. the most immature, those with ventilator devices on their faces 5. Can these models detect prolonged pain (e.g., chronic pain post-surgery), can they distinguish this from acute procedural pain 6. What medical and contextual factors affect behavioural responses to pain 7. How many recordings are required to achieve the best model performance All will be measured at the time of the video recording and assessed using performance metrics for machine learning outlined in the primary outcome measure | — |
Countries
England, United Kingdom
Outcome results
None listed