Development of a model to predict electroencephalographic seizures in neonates with hypoxic ischemic encephalopathy treated with therapeutic hypothermia.

Objective: Electroencephalographic seizures (ES) are common in neonates with hypoxic-ischemic encephalopathy (HIE), but identification with continuous electroencephalographic (EEG) monitoring (CEEG) is resource-intensive. We aimed to develop an ES prediction model.

Methods: Using a prospective observational study of 260 neonates with HIE undergoing CEEG, we identified clinical and EEG risk factors for ES, evaluated model performance with area under the receiver operating characteristic curve (AUROC), and calculated test characteristics emphasizing high sensitivity. We assessed ES incidence and timing in neonates subdivided by ES risk group (low, moderate, high) as determined by EEG risk factors.

Results: ES occurred in 32% (83/260) of neonates. Performing CEEG for only 24 h would fail to identify the 7% (17/260) of neonates with later onset ES (20% of all neonates experiencing ES). Identifying 90% or 95% of neonates with ES would require CEEG for 63 or 74 h, respectively. The optimal model included continuity and epileptiform discharges, both assessed in the initial 1 h of CEEG. It yielded an AUROC of .80, and at a cutoff that emphasized sensitivity, had sensitivity of 94%, specificity of 45%, positive predictive value of 44%, and negative predictive value of 95%. The model would avoid CEEG beyond 1 h in 32% (84/260) of neonates, but 6% (5/83) of neonates with ES would not have ES identified. ES incidence was significantly different (p < .01) across ES risk groups (6% low, 40% moderate, and 83% high). Only ~6 h of CEEG would identify all neonates with ES in the low-risk group, whereas 75 and 63 h of CEEG would be required to identify 95% of neonates with ES in the moderate-risk and high-risk groups, respectively.

Significance: Among neonates with HIE, a model employing two EEG variables from a 1-h screening EEG and stratifying neonates into low-, moderate-, and high-risk groups could enable evidence-based strategies for targeted CEEG use.