BackgroundBetter outcome prediction could assist in reliable quantification and classification of traumatic brain injury (TBI) severity to support clinical decision-making. We developed a multifactorial model combining quantitative electroencephalography (qEEG) measurements and clinically relevant parameters as proof of concept for outcome prediction of patients with moderate to severe TBI.MethodsContinuous EEG measurements were performed during the first 7 days of ICU admission. Patient outcome at 12 months was dichotomized based on the Extended Glasgow Outcome Score (GOSE) as poor (GOSE 1–2) or good (GOSE 3–8). Twenty-three qEEG features were extracted. Prediction models were created using a Random Forest classifier based on qEEG features, age, and mean arterial blood pressure (MAP) at 24, 48, 72, and 96 h after TBI and combinations of two time intervals. After optimization of the models, we added parameters from the International Mission for Prognosis And Clinical Trial Design (IMPACT) predictor, existing of clinical, CT, and laboratory parameters at admission. Furthermore, we compared our best models to the online IMPACT predictor.ResultsFifty-seven patients with moderate to severe TBI were included and divided into a training set (n = 38) and a validation set (n = 19). Our best model included eight qEEG parameters and MAP at 72 and 96 h after TBI, age, and nine other IMPACT parameters. This model had high predictive ability for poor outcome on both the training set using leave-one-out (area under the receiver operating characteristic curve (AUC) = 0.94, specificity 100%, sensitivity 75%) and validation set (AUC = 0.81, specificity 75%, sensitivity 100%). The IMPACT predictor independently predicted both groups with an AUC of 0.74 (specificity 81%, sensitivity 65%) and 0.84 (sensitivity 88%, specificity 73%), respectively.ConclusionsOur study shows the potential of multifactorial Random Forest models using qEEG parameters to predict outcome in patients with moderate to severe TBI.

中文翻译：

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使用脑电图预测中重度颅脑损伤患者的预后

背景更好的结果预测可以帮助可靠地量化和分类创伤性脑损伤 (TBI) 的严重程度，以支持临床决策。我们开发了一个结合定量脑电图 (qEEG) 测量和临床相关参数的多因素模型，作为中重度 TBI 患者预后预测的概念证明。方法 在入住 ICU 的前 7 天内进行连续 EEG 测量。12 个月时的患者结果根据扩展格拉斯哥结局评分 (GOSE) 分为差 (GOSE 1-2) 或良好 (GOSE 3-8)。提取了二十三个 qEEG 特征。预测模型是使用随机森林分类器创建的，该分类器基于 qEEG 特征、年龄和 24、48、72 岁的平均动脉血压 (MAP)，TBI 后 96 小时和两个时间间隔的组合。在优化模型后，我们添加了来自国际预后和临床试验设计任务 (IMPACT) 预测器的参数，以及入院时现有的临床、CT 和实验室参数。此外，我们将我们的最佳模型与在线 IMPACT 预测器进行了比较。结果 57 名中度至重度 TBI 患者被纳入并分为训练集（n = 38）和验证集（n = 19）。我们的最佳模型包括 8 个 qEEG 参数和 TBI 后 72 小时和 96 小时的 MAP、年龄和其他 9 个 IMPACT 参数。该模型在使用留一法（接受者操作特征曲线下面积（AUC）= 0.94，特异性100％，灵敏度75％）和验证集（AUC = 0.81，特异性 75%，灵敏度 100%）。IMPACT 预测器以 0.74（特异性 81%，敏感性 65%）和 0.84（敏感性 88%，特异性 73%）分别独立预测两组。结论我们的研究显示了使用 qEEG 参数进行预测的多因素随机森林模型的潜力中重度 TBI 患者的预后。