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.
Parents of a child currently in the NICU or
Health professionals currently working in the NICU.
Infants born between 25 0/7 weeks 32 6/7 weeks gestational age
Infants who are within 8 weeks postnatal age
Infants who are undergoing a routine heel lance
Infants with congenital malformations
Preterm infants
Cohort
Prospective
Not listed
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., 2022, 10.1097/j.pain.0000000000002798)
Cortical Correlate of Distress
To be analyzed using machine learning via bedside monitoring: Continuous EEG data capture
Cardiac Correlates of Distress
To be analyzed using machine learning via bedside monitoring: Heart Rate, Heart Rate Variability
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
Birthing Parent Heart Rate and Respiration Rate
We will measure parent cardiac stress in order to control for the effect of the parent context on infant pain processing. We will collect ECG data with 3 leads and a thin belt for respiration from the CNS monitor.
Chart review of general health indicators and other NICU experiences (control as potential confounders), such as:
* Gestational age at birth, post menstrual age at study, and postnatal age * Birthweight * Current weight * Sex * Birth order * Type of delivery and any resuscitation required * Appearance, Pulse, Grimace, Activity, and Respiration (APGAR) scores at birth (1 and 5 minutes) * SNAP II scores * Ventilation requirements * Type and time of last feed * What indwelling devices are present during the study * Area or location of study * Diagnoses (active problems in the last 72h and any diagnosis that have now resolved \[\>72h\]) * Antenatal history * Brain imaging (cranial ultrasound) * Acute procedure history (type, per day, number of attempts) * Amount of skin to skin per day * Hours since last skin-breaking procedure \& number of skin-breaking procedures in the 48-hours preceding the heel lance * Total doses of sucrose \& sucrose at time of study * Drug classes at time of heel lance
Chart Review of Cumulative Painful Procedures
We will collect data on the cumulative exposure of painful and/or invasive procedures that the infant experienced since birth. A published tool will be used to calculate total pain burden during their NICU stay (Laudiano-Dray et al., 2020; 10.1097/j.pain.0000000000001814).
Parent Self-Report Questionnaires
Four questionnaires will be completed to understand the perspectives and experiences of birthing parents at time of study: 1. Participant Information Sheet (PIS) - quantifies socio-economic status, ethnic background, and familial characteristics of the sample. 2. Parental Stress Scale: NICU (PSS:NICU) - measures NICU specific stress 3. Experiences in Close Relationships Scale (ECR) - measures adult attachment patterns 4. Perceived Stress Scale (PSS) - measures general stress levels
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York University
Lead sponsor
MOUNT SINAI HOSPITAL
Collaborator
University College, London
Collaborator
University College London Hospitals
Collaborator
