Massey Shavonne L, Sandoval Karamian Amanda G, Fitzgerald Mark P, Fung France W, Abramson Abigail, Salmon Mandy K, Parikh Darshana, Abend Nicholas S
Department of Pediatrics (Division of Neurology), Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, USA.
Departments of Neurology and Pediatrics, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, USA.
Epilepsia. 2025 Feb;66(2):518-530. doi: 10.1111/epi.18196. Epub 2024 Dec 16.
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.
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.
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.
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.
脑电图癫痫发作（ES）在患有缺氧缺血性脑病（HIE）的新生儿中很常见，但通过持续脑电图（EEG）监测（CEEG）进行识别需要大量资源。我们旨在开发一种ES预测模型。
通过对260例接受CEEG的HIE新生儿进行前瞻性观察研究，我们确定了ES的临床和EEG危险因素，使用受试者操作特征曲线下面积（AUROC）评估模型性能，并计算强调高敏感性的检验特征。我们评估了根据EEG危险因素划分的ES风险组（低、中、高）新生儿的ES发生率和发作时间。
32%（83/260）的新生儿发生了ES。仅进行24小时的CEEG将无法识别7%（17/260）发作较晚的ES新生儿（占所有发生ES新生儿的20%）。识别90%或95%的ES新生儿分别需要63或74小时的CEEG。最佳模型包括连续性和癫痫样放电，两者均在CEEG的最初1小时内进行评估。其AUROC为0.80，在强调敏感性的临界值时，敏感性为94%，特异性为45%，阳性预测值为44%，阴性预测值为95%。该模型可使32%（84/260）的新生儿避免超过1小时的CEEG，但6%（5/83）的ES新生儿将无法被识别出患有ES。ES风险组之间的ES发生率有显著差异（p<0.01）（低风险组为6%，中风险组为40%，高风险组为83%）。仅约6小时的CEEG就能识别低风险组中所有患有ES的新生儿，而分别需要75小时和63小时的CEEG才能识别中风险组和高风险组中95%的ES新生儿。
在患有HIE的新生儿中，一种采用来自1小时筛查EEG的两个EEG变量并将新生儿分为低、中、高风险组的模型，可以为有针对性地使用CEEG提供循证策略。
