Improving Prediction of Favourable Outcome After 6 Months in Patients with Severe Traumatic Brain Injury Using Physiological Cerebral Parameters in a Multivariable Logistic Regression Model.
Improving Prediction of Favourable Outcome After 6 Months in Patients with Severe Traumatic Brain Injury Using Physiological Cerebral Parameters in a Multivariable Logistic Regression Model.
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DOI：
10.1007/s12028-020-00930-6
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发表时间：
2020-10
影响因子：
3.5
通讯作者：
Aries MJ
中科院分区：
文献类型：
--
作者：
Bennis FC;Teeuwen B;Zeiler FA;Elting JW;van der Naalt J;Bonizzi P;Delhaas T;Aries MJ
目前的严重创伤性脑损伤（TBI）结局预测模型根据入院时测量的参数计算6个月后不利结局的可能性。我们的目的是通过增加重症监护室神经监测后24小时内连续测量的神经监测数据来改进当前模型。分析了2012年5月至2019年1月期间来自两家教学医院的45例颅内压/脑灌注压监测的重度TBI患者。在神经监测开始后的多个时间段（0-6 h、0-12 h、0-18 h、0-24 h）选择14个高频生理参数。除了全身生理参数和重大头部损伤后扩展的皮质类固醇随机化（CRASH）评分外，我们还在模型中增加了（动态）脑容量、脑顺应性和脑血管压力反应性指数的估计值。针对每个时间段的选定参数训练逻辑回归模型，以预测6个月后的结果。使用前向特征选择来选择参数。每个模型均通过留一交叉验证进行验证。使用CRASH作为唯一参数的逻辑回归模型得到的曲线下面积（AUC）为0.76。对于每个时间段，使用多达5个额外参数发现AUC增加。使用描述平均动脉血压和生理脑指数的5个参数，发现0-6 h期间的AUC最高（0.90）。目前的TBI预后预测模型可以通过增加在神经监测开始后的前24小时内连续测量的神经监测床旁参数来改进。由于这些因素可能在入院期间通过治疗进行修改，因此需要在更大的（多中心）数据集中进行测试。本文的在线版本（10.1007/s12028-020-00930-6）包含补充材料，可供授权用户使用。
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. The online version of this article (10.1007/s12028-020-00930-6) contains supplementary material, which is available to authorized users.
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影响因子：
2.2
作者：
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通讯作者：
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