Predictors of Abnormal Transfontanelle Ultrasound Findings in Neonates with Congenital Heart Disease
Liviu Moraru, Petra-Caroline Mayaya, Elena Hanganu, Raluca Moraru, Anca Bivoleanu, Simona Irina DamianABSTRACT
Background
Advances in neonatal cardiac care have improved survival in infants with congenital heart disease (CHD), shifting clinical attention toward early neurological morbidity. While cardiac anatomical complexity has traditionally been considered a major determinant of brain injury, emerging evidence suggests that genetic and systemic factors may play a more important role. Transfontanellar ultrasound (TFUS) is widely used as a bedside screening tool for early detection of neonatal brain abnormalities.
Aim
To assess the incidence of abnormal TFUS findings in neonates with CHD and identify clinical and genetic predictors of early neurological vulnerability independent of cardiac anatomy.
Materials and Methods
We conducted an observational cohort study including 138 neonates with CHD who underwent TFUS evaluation at a tertiary care center in Iași, Romania. Demographic, perinatal, clinical, genetic, and therapeutic data were collected retrospectively. CHD complexity was classified using the Bethesda classification. Univariate and multivariate logistic regression analyses were performed to identify predictors of abnormal TFUS findings.
Results
Abnormal TFUS findings were identified in 42 neonates (30.4%), most commonly ventricular dilatation, intracranial hemorrhage, and choroid plexus cysts. In multivariate analysis, positive genetic testing was the strongest independent predictor (OR 3.9, 95% CI 1.5–10.2), followed by mechanical ventilation dependence (OR 2.7, 95% CI 1.1–6.5) and high Bethesda risk category (OR 2.2, 95% CI 1.0–4.8). Cardiac anatomical classification and gestational age were not independently associated with abnormal TFUS findings.
Conclusions
In neonates with CHD, early TFUS abnormalities appear to be driven mainly by genetic and systemic factors rather than cardiac anatomy alone, supporting integrative risk stratification approaches for targeted neurological surveillance.