Department of Biomedical Engineering, Maastricht University, PO Box 616, 6200 MD, Maastricht, The Netherlands.
MHeNS School for Mental Health and Neuroscience, Maastricht University, PO Box 616, 6200 MD, Maastricht, The Netherlands.
Neurocrit Care. 2020 Oct;33(2):542-551. doi: 10.1007/s12028-020-00930-6.
BACKGROUND/OBJECTIVE: Current severe traumatic brain injury (TBI) outcome prediction models calculate the chance of unfavourable outcome after 6 months based on parameters measured at admission. We aimed to improve current models with the addition of continuously measured neuromonitoring data within the first 24 h after intensive care unit neuromonitoring.
Forty-five severe TBI patients with intracranial pressure/cerebral perfusion pressure monitoring from two teaching hospitals covering the period May 2012 to January 2019 were analysed. Fourteen high-frequency physiological parameters were selected over multiple time periods after the start of neuromonitoring (0-6 h, 0-12 h, 0-18 h, 0-24 h). Besides systemic physiological parameters and extended Corticosteroid Randomisation after Significant Head Injury (CRASH) score, we added estimates of (dynamic) cerebral volume, cerebral compliance and cerebrovascular pressure reactivity indices to the model. A logistic regression model was trained for each time period on selected parameters to predict outcome after 6 months. The parameters were selected using forward feature selection. Each model was validated by leave-one-out cross-validation.
A logistic regression model using CRASH as the sole parameter resulted in an area under the curve (AUC) of 0.76. For each time period, an increased AUC was found using up to 5 additional parameters. The highest AUC (0.90) was found for the 0-6 h period using 5 parameters that describe mean arterial blood pressure and physiological cerebral indices.
Current TBI outcome prediction models can be improved by the addition of neuromonitoring bedside parameters measured continuously within the first 24 h after the start of neuromonitoring. As these factors might be modifiable by treatment during the admission, testing in a larger (multicenter) data set is warranted.
背景/目的：目前的严重创伤性脑损伤（TBI）预后预测模型是基于入院时测量的参数，计算 6 个月后不良结局的概率。我们的目的是通过在重症监护病房神经监测开始后 24 小时内连续测量神经监测数据来改进当前的模型。
对 2012 年 5 月至 2019 年 1 月期间来自两家教学医院的颅内压/脑灌注压监测的 45 例严重 TBI 患者进行了分析。在神经监测开始后的多个时间段内选择了 14 个高频生理参数（0-6 小时、0-12 小时、0-18 小时、0-24 小时）。除了全身生理参数和扩展皮质类固醇随机分组治疗严重颅脑损伤（CRASH）评分外，我们还将（动态）脑容量、脑顺应性和脑血管压力反应性指数的估计值添加到模型中。针对每个时间段，使用向前特征选择对选定参数进行逻辑回归模型训练，以预测 6 个月后的结局。使用留一交叉验证对每个模型进行验证。
仅使用 CRASH 作为唯一参数的逻辑回归模型的曲线下面积（AUC）为 0.76。对于每个时间段，使用多达 5 个额外参数可以提高 AUC。在 0-6 小时期间，使用 5 个描述平均动脉血压和生理脑指数的参数可获得最高 AUC（0.90）。
通过在神经监测开始后 24 小时内连续测量神经监测床边参数，可以改进当前的 TBI 预后预测模型。由于这些因素可能通过入院期间的治疗而改变，因此在更大的（多中心）数据集进行测试是必要的。
