Fetal intraventricular hemorrhage and periventricular hemorrhagic venous infarction: time for a new classification

To report on the development of a novel classification system for fetal IVH that will allow the use of unified terminology and definitions and may serve as a potentially reliable prognostic tool. Intraventricular hemorrhage (IVH) and periventricular hemorrhagic venous infarction are major causes of mortality and morbidity in preterm newborns. Currently, fetal IVH grading is based on the Papile and Volpe Classifications used for preterm infants, which are based on the amount of intraventricular blood and ventricular size as measured by cranial ultrasound. However, in contrast to the acute IVH diagnosis in preterm newborns, timing of diagnosis in fetuses is usually delayed to a subacute or chronic phase; therefore, assessing the amount of blood in the ventricles as a main criterion for grading appears less relevant. Moreover, the currently used grading system does not include other parameters that may significantly affect neurological outcome. A multidisciplinary, multicentre fetal neurology consortium developed a new classification of fetal IVH. Initial internal validation will be performed, using ultrasound and magnetic resonance images from 20 cases of fetal IVH which will be reviewed and rated by 6 obstetricians and 6 pediatric neuroradiologists, respectively. The classification parameters included ventricular size, parenchymal involvement (including location, extent and laterality), involvement of the basal ganglia, the posterior fossa, and the extra-axial spaces. It also distinguishes between acute/subacute hemorrhage to chronic changes and relates to evolution of the findings. The suggested novel fetal IVH classification method will offer standardised nomenclature, and has the potential to provide more accurate prognostication, and to assist in the implementation of optimal clinical practices. Prospective multicentre trials including postnatal follow-up studies are required to validate this classification system and convert it into a predictive tool.