Background: Post-hemorrhagic hydrocephalus (PHH) is a severe complication in premature infants following intraventricular hemorrhage (IVH). It is characterized by abnormal cerebrospinal fluid (CSF) accumulation, disrupted CSF dynamics, and elevated intracranial pressure (ICP), leading to significant neurological impairments.
Objective: This review provides an overview of recent molecular insights into the pathophysiology of PHH and evaluates emerging therapeutic approaches aimed at addressing its underlying mechanisms.
Purpose: This Hydrocephalus Clinical Research Network (HCRN) study had two aims: (1) to compare the predictive performance of the original ETV Success Score (ETVSS) using logistic regression modeling with other newer machine learning models and (2) to assess whether inclusion of imaging variables improves prediction performance using machine learning models.
Methods: We identified children undergoing first-time ETV for hydrocephalus that were enrolled prospectively at HCRN sites between 200 and 2020. The primary outcome was ETV success 6 months after index surgery.