目标:在神经重症监护中,来自多个生物传感器的数据被连续测量,但只是偶尔得到主治医生的承认。相比之下,机器学习(ML)工具可以连续分析大量数据,利用底层信息。然而,这种基于机器学习的解决方案的性能受到不同因素的限制,例如,通过病人的运动,操纵,或者,如在外部心室引流(EVDs)的情况下,脑脊液引流控制颅内压(ICP)。作者旨在开发一种基于ML的算法,自动对正常信号进行分类,神器,和来自EVD的高分辨率ICP监测数据的排水,使数据适用于实时伪影去除和未来的ML应用。
方法:在他们的2中心回顾性队列研究中,作者使用第一个神经重症监护病房(苏黎世大学医院)40例患者的标记ICP数据进行模型开发.作者创建了94个用于训练模型的描述性特征。他们通过主成分分析构建管道后,将基于直方图的梯度提升与极端随机的树进行了比较,通过网格搜索进行超参数优化,和顺序特征选择。通过嵌套的5倍交叉验证和接收器工作特征曲线(AUROC)下的多类别面积来测量性能。一秒钟内20名患者的数据,使用独立的神经重症监护病房(Charité-UniversityätsmedizinBerlin)通过自举技术和AUROC进行外部验证.
结果:在交叉验证中,在开发数据集上,表现最好的模型的平均AUROC为0.945(95%CI0.92-0.969).在外部验证数据集上,该模型在100个自举验证周期中以平均AUROC为0.928(95%CI0.908-0.946)对正常信号进行分类,神器,和排水系统。
结论:这里,作者开发了一个性能良好的监督模型,具有外部验证,可以检测正常信号,神器,以及神经重症监护病房患者ICP信号的引流。为了将来的分析,这是丢弃伪影或检测ICP监测信号中的引流事件的强大工具。
OBJECTIVE: In neurocritical care, data from multiple biosensors are continuously measured, but only sporadically acknowledged by the attending physicians. In contrast, machine learning (ML) tools can analyze large amounts of data continuously, taking advantage of underlying information. However, the performance of such ML-based solutions is limited by different factors, for example, by patient motion, manipulation, or, as in the case of external ventricular drains (EVDs), the drainage of CSF to control intracranial pressure (ICP). The authors aimed to develop an ML-based algorithm that automatically classifies normal signals, artifacts, and drainages in high-resolution ICP monitoring data from EVDs, making the data suitable for real-time artifact removal and for future ML applications.
METHODS: In their 2-center retrospective cohort study, the authors used labeled ICP data from 40 patients in the first neurocritical care unit (University Hospital Zurich) for model development. The authors created 94 descriptive features that were used to train the model. They compared histogram-based gradient boosting with extremely randomized trees after building pipelines with principal component analysis, hyperparameter optimization via grid search, and sequential feature selection. Performance was measured with nested 5-fold cross-validation and multiclass area under the receiver operating characteristic curve (AUROC). Data from 20 patients in a second, independent neurocritical care unit (Charité - Universitätsmedizin Berlin) were used for external validation with bootstrapping technique and AUROC.
RESULTS: In cross-validation, the best-performing model achieved a mean AUROC of 0.945 (95% CI 0.92-0.969) on the development dataset. On the external validation dataset, the model performed with a mean AUROC of 0.928 (95% CI 0.908-0.946) in 100 bootstrapping validation cycles to classify normal signals, artifacts, and drainages.
CONCLUSIONS: Here, the authors developed a well-performing supervised model with external validation that can detect normal signals, artifacts, and drainages in ICP signals from patients in neurocritical care units. For future analyses, this is a powerful tool to discard artifacts or to detect drainage events in ICP monitoring signals.