Rebooting Infant Pain Care: Using Machine Learning and Skin-to-Skin Contact to Exponentially Improve Neonatal Intensive Care Unit Practice
Rebooting Infant Pain Care: Using Machine Learning and Skin-to-Skin Contact to Exponentially Improve Neonatal Intensive Care Unit Practice
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.
Unmanaged pain in hospitalized infants has serious long-term complications. Existing infant pain assessment approaches demonstrate several key flaws (e.g., dependent on human cognitive capacity to simply combine data from multiple indicators into a total pain score, none have passed a critical discriminant validity test, bias introduced from human caregivers). Thus, the complexity of preterm pain assessment necessitates a machine learning approach. 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, better understanding how skin-to-skin contact works in caregiver-infant dyads and factors that influence the effectiveness of this pain management strategy is a critical step in improving infant pain care in NICUs. Relatedly, the design and sample of our current study (acute pain paradigm while infant is either in skin-to-skin contact with the birthing parent or in the cot) allows us to not only test the influence of skin-to-skin contact vs. cot on preterm newborn pain responding, but also interrogate potential mechanisms underlying the effectiveness of skin-to-skin contact (i.e., cardiac regulation attunement between caregivers and their infants during the procedure; influence of birthing parent perceived stress given the particularly elevated stress levels of NICU parents). A sample of 400 preterm infants (300 from Mount Sinai Hospital and 100 from University College London Hospital [UCLH]) and their birthing parents (if available) will be followed during a routine painful procedure (heel lance). Pain indicators (facial grimacing [behavioural indicators], heart rate, respiration 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). Heart rate and respiration rate, as well as maternal-reported perceived stress levels, will be collected from the birthing parent to examine factors impacting the effectiveness of skin-to-skin contact.
QUALITATIVE INTERVIEWS
Inclusion Criteria:
Exclusion Criteria:
*Participants who cannot communicate fluently in English
QUANTITITATIVE DATA CAPTURE (video, eeg, ecg, RR, SPo2)
Inclusion Criteria:
Exclusion Criteria:
rpr@yorku.ca4167362100 ext. ext. 20177
Vibhuti.Shah@sinaihealth.ca4165864816
l.fabrizi@ucl.ac.uk02031081888 ext. Ext. 51888
judith.meek@nhs.net020 3447 8094