Using Vital Signs for the Early Prediction of Necrotizing Enterocolitis in Preterm Neonates with Machine Learning.

Background/objectives: Necrotizing enterocolitis (NEC), a devastating neonatal gastrointestinal disease mostly seen in preterm infants, lacks accurate prediction despite known risk factors. This hinders the possibility of applying targeted preventive therapies. This study explores the use of vital signs, including cerebral and splanchnic oxygenation, measured with near-infrared spectroscopy in early NEC prediction.

Development of a parent decision support tool for surgical necrotising enterocolitis: a study protocol.

Introduction: Necrotising enterocolitis is a devastating gastrointestinal disease predominantly affecting preterm infants. In 40% of cases, its rapid progression renders conservative treatment insufficient, necessitating laparotomy as the sole viable option for survival. However, high perioperative and postoperative mortality rates, along with severe future potential disabilities and suffering, can complicate the decision of whether surgery is still in the infant's best interest.