Western Institute of Neuroscience, Western Interdisciplinary Research Building, Western University, 1151 Richmond Street, London, ON, N6A 3K7, Canada.
Temerty Faculty of Medicine, University of Toronto, Toronto, Canada.
J Neurol. 2023 Dec;270(12):6071-6080. doi: 10.1007/s00415-023-11941-6. Epub 2023 Sep 4.
There is a lack of reliable tools used to predict functional recovery in unresponsive patients following a severe brain injury. The objective of the study is to evaluate the prognostic utility of resting-state functional magnetic resonance imaging for predicting good neurologic recovery in unresponsive patients with severe brain injury in the intensive-care unit.
Each patient underwent a 5.5-min resting-state scan and ten resting-state networks were extracted via independent component analysis. The Glasgow Outcome Scale was used to classify patients into good and poor outcome groups. The Nearest Centroid classifier used each patient's ten resting-state network values to predict best neurologic outcome within 6 months post-injury.
Of the 25 patients enrolled (mean age = 43.68, range = [19-69]; GCS ≤ 9; 6 females), 10 had good and 15 had poor outcome. The classifier correctly and confidently predicted 8/10 patients with good and 12/15 patients with poor outcome (mean = 0.793, CI = [0.700, 0.886], Z = 2.843, p = 0.002). The prediction performance was largely determined by three visual (medial: Z = 3.11, p = 0.002; occipital pole: Z = 2.44, p = 0.015; lateral: Z = 2.85, p = 0.004) and the left frontoparietal network (Z = 2.179, p = 0.029).
Our approach correctly identified good functional outcome with higher sensitivity (80%) than traditional prognostic measures. By revealing preserved networks in the absence of discernible behavioral signs, functional connectivity may aid in the prognostic process and affect the outcome of discussions surrounding withdrawal of life-sustaining measures.
目前缺乏可靠的工具用于预测严重脑损伤后无反应患者的功能恢复。本研究旨在评估静息态功能磁共振成像对预测重症监护病房无反应性严重脑损伤患者良好神经恢复的预后价值。
每位患者接受 5.5 分钟的静息态扫描，并通过独立成分分析提取 10 个静息态网络。格拉斯哥预后量表用于将患者分为预后良好和预后不良组。最近质心法使用每位患者的 10 个静息态网络值来预测伤后 6 个月内的最佳神经预后。
25 名入组患者（平均年龄 43.68 岁，范围 [19-69]；GCS≤9；女性 6 名）中，10 名预后良好，15 名预后不良。分类器正确且自信地预测了 10 名预后良好的患者中的 8 名和 15 名预后不良的患者中的 12 名（平均=0.793，CI=[0.700, 0.886]，Z=2.843，p=0.002）。预测性能主要由三个视觉网络（内侧：Z=3.11，p=0.002；枕极：Z=2.44，p=0.015；外侧：Z=2.85，p=0.004）和左侧额顶网络（Z=2.179，p=0.029）决定。
我们的方法正确地识别了良好的功能预后，其敏感性（80%）高于传统的预后指标。通过在没有可识别的行为迹象的情况下显示保留的网络，功能连接可能有助于预后过程，并影响围绕停止生命维持措施的讨论结果。
