Early Multimodal Assessment for Prediction of Cerebral Palsy in Neonatal Hypoxic-Ischemic Encephalopathy.

Abstract

Objective: The objective of this study is to determine the predictive value of magnetic resonance imaging (MRI), amplitude-integrated electroencephalography (aEEG), the Hammersmith Neonatal Neurological Examination (HNNE), and the General Movements Assessment (GMA) for cerebral palsy (CP) in neonates with hypoxic-ischemic encephalopathy (HIE), and to evaluate whether combining these modalities improves diagnostic accuracy.

Study design: In this prospective two-center cohort study, 53 term or late-preterm infants with HIE treated with standardized therapeutic hypothermia (33.5°C for 72 hours) were evaluated. aEEG and MRI findings were compared with concurrent HNNE and GMA results. CP was diagnosed during follow-up by a pediatric neurologist blinded to neonatal data. Diagnostic performance was analyzed using receiver-operating characteristic curves and multivariable logistic regression according to the STROBE (Strengthening the Reporting of Observational Studies in Epidemiology) guidelines.

Results: CP developed in 11 infants (20.8%). aEEG showed the highest predictive accuracy (area under the curve = 0.816 [95% confidence interval: 0.65-0.98]), and abnormal aEEG independently predicted CP (odds ratio = 18.5, p = 0.004). The combined "MRI or aEEG abnormal" model achieved the best overall accuracy (86.8%), with sensitivity = 90.9% and negative predictive value = 97.3%. MRI and HNNE had moderate predictive value, whereas GMA showed high specificity but low sensitivity.

Conclusion: aEEG emerged as a robust and independent early biomarker for CP prediction after HIE. Combining aEEG with MRI substantially enhanced diagnostic precision, reflecting complementary functional and structural brain injury mechanisms. Although HNNE and GMA add screening value, they are insufficient alone. Standardized multimodal protocols integrating structural (MRI), functional (aEEG), and clinical (HNNE-GMA) assessments should be incorporated into clinical practice to improve early prognostication and guide neuroprotective interventions.

Key points: · Early multimodal tools improve CP prediction.. · MRI and aEEG show higher diagnostic accuracy.. · Low HNNE scores indicate increased CP risk.. · Abnormal GMA supports early neurological impairment.. · Combining MRI + GMA + HNNE enhances prediction..