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Novel Methods to Predict Increased Intracranial Pressure During Intensive Care and Long-Term Neurological Outcome After Traumatic Brain Injury: Development and Validation in a Multicenter Dataset

机译:预测重症监护期间颅内压升高和颅脑外伤后长期神经功能预后的新方法:在多中心数据集中的开发和验证

摘要

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.
机译:目的:颅内压监测是严重颅脑外伤后的护理标准。颅内压升高是继发性损伤,伴有不良预后。我们开发了一个模型,通过使用连续颅内压的动态特征和平均动脉压监测来提前30分钟预测颅内压增高。此外,我们假设通过在ICU的前24小时内增加连续颅内压的动态特征和平均动脉压监测,可以大大改善当前模型预测长期神经系统预后的性能。设计::预后建模。非介入性,观察性,回顾性研究。地点和患者::信息技术的大脑监测数据集由来自11个欧洲国家的22位神经性ICU入院的264位创伤性脑损伤患者组成。干预措施::无。测量:预测模型是使用多元逻辑回归和高斯过程(一种机器学习技术)构建的。预测属性为TBI核心预测因素中发生重大颅脑损伤后的皮质类固醇随机分组和国际预后及临床试验设计任务,以及每分钟平均动脉压和颅内压的时间序列汇总统计数据。主要结果:可以预言良好的校准(Hosmer-Lemeshow p值0.12,校准斜率1.02,大范围校准-0.02)和辨别力(接受者工作曲线下的面积= 0.87)可在30分钟前预测颅内压增高。 )上的外部验证数据集。仅基于静态入院数据的六个月后神经系统预后不良的预测模型(格拉斯哥结果得分1-2)在接受者操作曲线下的面积为0.72;在头24小时内添加颅内压和平均动脉压的动态信息可将性能提高到0.90。同样,当包含动态信息时,格拉斯哥成果得分1-3的预测也从0.68提高到0.87。结论:连续平均动脉压和颅内压监测中的动态信息可以准确预测神经性ICU中颅内压的升高。将颅内压的前24小时信息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个月的长期神经系统预后。

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