Lagacé Micheline, Montazeri Saeed, Kamino Daphne, Mamak Eva, Ly Linh G, Hahn Cecil D, Chau Vann, Vanhatalo Sampsa, Tam Emily W Y
Faculty of Medicine, Clinician Investigator Program, University of British Columbia, Vancouver, British Columbia, Canada.
Division of Neurology, Department of Paediatrics, The Hospital for Sick Children, University of Toronto, Toronto, Ontario, Canada.
Ann Clin Transl Neurol. 2024 Dec;11(12):3267-3279. doi: 10.1002/acn3.52233. Epub 2024 Nov 14.
Assess the capacity of brain state of the newborn (BSN) to predict neurodevelopment outcomes in neonatal encephalopathy.
Trends of BSN, a deep learning-based measure translating EEG background to a continuous trend, were studied from a three-channel montage long-term EEG monitoring from a prospective cohort of 92 infants with neonatal encephalopathy and neurodevelopmental outcomes assessed by Bayley Scales of Infant Development, 3rd edition (Bayley-III) at 18 months. Outcome prediction used categories "Severe impairment" (Bayley-III composite score ≤70 or death) or "Any impairment" (score ≤85 or death).
"Severe impairment" was predicted best for motor outcomes (24 h area under the curve (AUC) = 0.97), followed by cognitive (36 h AUC = 0.90), overall (24 h AUC = 0.84), and language (24 h AUC = 0.82). "Any impairment" was best predicted for motor outcomes (12 h AUC = 0.95), followed by cognitive (24 h AUC = 0.85), overall (12 h AUC = 0.75), and language (12 and 24 h AUC = 0.68). Optimal BSN cutoffs for outcome predictions evolved with the postnatal age. Low BSN scores reached a 100% positive prediction of poor outcomes at 24 h of age.
BSN is an excellent predictor of adverse neurodevelopmental outcomes in survivors of neonatal encephalopathy after therapeutic hypothermia, even at 24 h of life. The trend provides a fully automated, objective, quantified, and reliable interpretation of EEG background. The high temporal resolution supports continuous bedside brain assessment and early prognostication during the initial dynamic recovery phase.
评估新生儿脑状态（BSN）预测新生儿脑病神经发育结局的能力。
对92例患有新生儿脑病的婴儿进行前瞻性队列研究，通过三通道导联进行长期脑电图监测，研究基于深度学习将脑电图背景转化为连续趋势的BSN趋势，并在18个月时采用贝利婴儿发育量表第三版（Bayley-III）评估神经发育结局。结局预测采用“严重损伤”（Bayley-III综合评分≤70或死亡）或“任何损伤”（评分≤85或死亡）类别。
对于运动结局，“严重损伤”的预测最佳（24小时曲线下面积（AUC）=0.97），其次是认知（36小时AUC=0.90）、总体（24小时AUC=0.84）和语言（24小时AUC=0.82）。对于“任何损伤”，运动结局的预测最佳（12小时AUC=0.95），其次是认知（24小时AUC=0.85）、总体（12小时AUC=0.75）和语言（12和24小时AUC=0.68）。结局预测的最佳BSN临界值随出生后年龄而变化。低BSN评分在出生后24小时对不良结局的阳性预测率达到100%。
即使在出生后24小时，BSN也是治疗性低温后新生儿脑病幸存者不良神经发育结局的优秀预测指标。该趋势为脑电图背景提供了完全自动化、客观、量化和可靠的解读。高时间分辨率支持在初始动态恢复阶段进行连续的床边脑评估和早期预后判断。
