Clinical, Biochemical, and Neuroimaging Findings Predict Long-Term Neurodevelopmental Outcome in Symptomatic Congenital Cytomegalovirus Infection

ObjectiveTo evaluate clinical, biochemical, and neuroimaging findings as predictors of neurodevelopmental outcome in patients with symptomatic congenital cytomegalovirus (CMV).Study designThe study cohort comprised 26 patients with symptomatic congenital CMV born between 1993 and 2009 in a single center. Absolute and weight deficit-adjusted head circumference were considered. Cerebrospinal fluid (CSF) investigations included standard cytochemical analysis, determination of beta2-microglobulin (β2-m), neuron-specific enolase, and CMV DNA detection. Neuroimaging was classified according to a validated scoring system comprising calcifications, ventriculomegaly, and atrophy, with findings graded from 0 to 3. Systematic long-term neurodevelopmental assessment included motor function, cognition, behavior, hearing, vision, and epilepsy. Sequelae were graded as mild/absent, moderate, or severe; adverse outcome was defined as death or moderate to severe disability.ResultsThree children died. The mean age at follow-up of the survivors was 8.7 ± 5.3 years (range, 19 months to 18.0 years). Neonatal findings showing a significant association with adverse outcome were relative microcephaly, CSF β2-m concentrations, and grade 2-3 neuroimaging abnormalities (P < .05). Receiver operator characteristic curve analysis indicated that the most accurate single factor for predicting unfavorable outcome was CSF β2-m >7.9 mg/L (area under the curve, 0.84 ± 0.08; sensitivity, 69%; specificity, 100%). The combination of CSF β2-m >7.9 mg/L and moderate-severe neuroimaging alterations improved predictive ability (area under the curve, 0.92 ± 0.06; sensitivity, 87%; specificity, 100%).ConclusionAdjusted head circumference, CSF β2-m level, and neuroimaging studies have prognostic significance for neurodevelopmental outcome in newborns with congenital CMV. A combination of early findings improves the predictive value.