Genetic insights and clinical outcomes in fetal ventriculomegaly: A retrospective analysis

Fetal ventriculomegaly (VM) is a common ultrasound finding with potential implications for pregnancy outcomes. There are few studies on the prevalence of genetic abnormalities in fetal VM. We aim to investigate the chromosomal and monogenic abnormalities risk and evaluate outcomes of fetal VM.

We analyzed data from 211 pregnancies with fetal VM. Chromosomal abnormalities were assessed using karyotyping and/or CMA/CNV-Seq, and monogenic variations were detected through whole exome sequencing (WES) in cases with negative initial results. Pregnancy outcomes were also evaluated.

The mean gestational age at VM detection was 27.0 weeks. Chromosomal abnormalities were detected in 13.3 % of cases overall, with a refined positive rate of 9.0 % after excluding polymorphisms and VUS CNVs. Subgroup analysis of 106 isolated VM cases revealed no significant differences in chromosomal abnormalities between unilateral and bilateral VM or between mild and moderate VM. Futhermore, 55 fetuses underwent WES, revealing pathogenic or likely pathogenic variants in 20 % of cases. Non-isolated VM cases showed a significantly higher rate of monogenic abnormalities (P = 0.01). Pregnancy outcomes indicated that isolated VM cases had a higher rate of normal development (89.2 %) compared to non-isolated cases (58.6 %), which also had higher rates of induced labor, stillbirths, and neonatal deaths. Ultrasound follow-up of 123 fetuses without chromosomal abnormalities showed that mild VM predominantly resolved or remained stable, while severe VM had a significantly higher progression rate compared to moderate VM.

The study highlights the importance of distinguishing between isolated and non-isolated VM in prenatal diagnostics. Severe VM is associated with higher progression rates, while WES provides valuable insights into monogenic abnormalities, particularly in non-isolated cases. These findings underscore the need for comprehensive prenatal evaluation to better inform clinical decision-making and predict pregnancy outcomes.