Novel Methods to Predict Increased Intracranial Pressure During Intensive Care and Long-Term Neurologic Outcome After Traumatic Brain Injury: Development and Validation in a Multicenter Dataset
Novel Methods to Predict Increased Intracranial Pressure During Intensive Care and Long-Term Neurologic Outcome After Traumatic Brain Injury: Development and Validation in a Multicenter Dataset
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DOI：
10.1097/ccm.0b013e3182742d0a
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发表时间：
2013-02-01
影响因子：
8.8
通讯作者：
Meyfroidt, Geert
中科院分区：
文献类型：
--
作者：
Guiza, Fabian;Depreitere, Bart;Meyfroidt, Geert
目的：颅压监测是重型颅脑损伤后的标准护理。颅内压升高是与不良预后相关的继发性损伤。我们利用连续的颅内压和平均动脉压监测的动态特征，开发了一个提前30分钟预测颅内压升高的模型。此外，我们假设，通过在ICU的前24小时增加连续的颅内压和平均动脉压监测的动态特征，当前模型预测长期神经结果的性能可以得到显著改善。设计：预后建模。非干预性、观察性、回顾性研究。设置和患者：信息技术脑监测数据集由来自11个欧洲国家的22个神经ICU收治的264名创伤性脑损伤患者组成。干预：无。测量：使用多变量Logistic回归和机器学习技术高斯过程建立预测模型。预测属性是严重颅脑损伤后皮质类固醇的随机化-基础和国际预后和临床试验设计-核心预测因子，以及每分钟平均动脉压和颅内压的时间序列汇总统计。主要结果：在外部验证数据集上进行良好的校准(Hosmer-Lemeshowp值0.12，校准斜率1.02，整体校准-0.02)和区分度(接收器操作曲线下的面积=0.87)，可以提前30分钟预测颅内压升高的发作。仅基于静态入院数据预测6个月神经预后不良(格拉斯哥预后评分1-2)的模型在接收器操作曲线下的面积为0.72；添加前24小时内的动态颅内压和平均动脉压信息将性能提高到0.90。同样，当包含动态信息时，格拉斯哥预后评分1-3分的预测从0.68提高到0.87。结论：连续平均动脉压和颅内压监测的动态信息可以准确预测神经ICU中的颅内压升高。将前24小时的颅内压和平均动脉压监测信息添加到已知的基线危险因素中，可以非常准确地预测6个月后的长期神经学结果。(Crit Care Med 2013；41：554-564)
Objective: Intracranial pressure monitoring is standard of care after severe traumatic brain injury. Episodes of increased intracranial pressure are secondary injuries associated with poor outcome. We developed a model to predict increased intracranial pressure episodes 30 mins in advance, by using the dynamic characteristics of continuous intracranial pressure and mean arterial pressure monitoring. In addition, we hypothesized that performance of current models to predict long-term neurologic outcome could be substantially improved by adding dynamic characteristics of continuous intracranial pressure and mean arterial pressure monitoring during the first 24 hrs in the ICU.Design: Prognostic modeling. Noninterventional, observational, retrospective study.Setting and Patients: The Brain Monitoring with Information Technology dataset consisted of 264 traumatic brain injury patients admitted to 22 neuro-ICUs from 11 European countries.Interventions: None.Measurements: Predictive models were built with multivariate logistic regression and Gaussian processes, a machine learning technique. Predictive attributes were Corticosteroid Randomisation After Significant Head Injury-basic and International Mission for Prognosis and Clinical Trial design in TBI-core predictors, together with time-series summary statistics of minute-by-minute mean arterial pressure and intracranial pressure.Main Results: Increased intracranial pressure episodes could be predicted 30 mins ahead with good calibration (Hosmer-Lemeshow p value 0.12, calibration slope 1.02, calibration-in-the-large -0.02) and discrimination (area under the receiver operating curve = 0.87) on an external validation dataset. Models for prediction of poor neurologic outcome at six months (Glasgow Outcome Score 1-2) based only on static admission data had 0.72 area under the receiver operating curve; adding dynamic information of intracranial pressure and mean arterial pressure during the first 24 hrs increased performance to 0.90. Similarly, prediction of Glasgow Outcome Score 1-3 was improved from 0.68 to 0.87 when including dynamic information.Conclusion: The dynamic information in continuous mean arterial pressure and intracranial pressure monitoring allows to accurately predict increased intracranial pressure in the neuro-ICU. Adding information of the first 24 hrs of intracranial pressure and mean arterial pressure monitoring to known baseline risk factors allows very accurate prediction of long-term neurologic outcome at 6 months. (Crit Care Med 2013; 41: 554-564)