Retrospective Multicenter Validation of a Conventional EEG-Based Decision-Support Algorithm to Discriminate Mild Versus Moderate/Severe Hypoxic-Ischemic Encephalopathy in Term Newborns (Newborn Neuro Digital Project)
This retrospective, multicenter, observational study evaluates how well a decision-support algorithm can tell apart mild forms of hypoxic-ischemic encephalopathy (HIE) from moderate or severe forms in full-term newborns born after a lack of oxygen around birth (perinatal asphyxia). The algorithm reads the raw (non-compressed) EEG signal. Its output is compared with the reference reading of the full conventional EEG made by a panel of pediatric neurophysiologists together with the baby's clinical information. The study uses only medical data that already exists and asks nothing of the babies or their families.
Build a Decision Aid Tool to Help Emergency Intensive Care Specialists in the Context of Hypoxic Ischemic Encephalopathy
Long Term Prognostic of Neonatal Hypoxic Ischemic Encephalopathy With Hypothermia Treatment
Predictive Value of aEEG and Cerebral Oxygenation on Neurological Outcomes in Newborns With Mild Hypoxic-Ischemic Encephalopathy
Early Neurological Outcome in Newborns With Mild Encephalopathy:a Regional Network
Neurodevelopmental Outcome in Newborn With Hypoxic-ischemic Encephalopathy Treated With Therapeutic Hypothermia