目的:颅内压(ICP)的治疗越来越多地被机器学习(ML)衍生的ICP波形特征所了解。有差距,然而,由于外部心室引流(EVD)和实质内监测仪(IPM)衍生的波形之间的差异尚未得到很好的解释,因此理解ICP监测仪类型可能会使用于这些预测工具的波形特征产生偏差.
目的:我们试图开发一种概念验证ML模型,用于区分源自EVD或IPM的ICP波形。
方法:我们在前瞻性转化研究和创伤性脑损伤临床知识多中心研究中检查了来自ICU生理学队列的原始ICP波形数据。
方法:使用套式患者五折交叉验证和带袋装决策树(BDT)的分组分析和线性判别分析进行特征选择和公平评价。9名患者被保留为看不见的坚持,以进行进一步评估。
结果:共14,110小时的ICP波形数据来自82例患者(EVD,47;IPM,26;两者,9).平均年龄,格拉斯哥昏迷量表(GCS)总计,和GCS运动成绩,以及中线移位的存在和数量,组之间是相似的。受试者工作特征曲线下的模型平均面积(AU-ROC)在所有折叠上都超过0.874。在额外的严格的基于聚类的子群分析中,旨在测试模型与较小的子集的交叉验证的弹性,这些子集被构造为在一个混淆集中开发模型并在另一个子集中测试它们,AU-ROC超过0.811。在使用基于倾向得分而不是基于聚类的子群分析的类似分析中,AU-ROC平均值超过0.827。在842个提取的ICP特征中,62在每次分析中都是不变的,代表ICP监测器类型之间最准确和最可靠的差异。对于九个病人的坚持,使用BDT获得0.826的AU-ROC。
结论:开发的概念验证ML模型识别了EVD和IPM衍生的ICP信号的差异,它可以为大规模回顾性数据集提供缺失的上下文数据,防止不加区别地摄取ICP数据的计算模型中的偏差,和控制混杂使用我们的模型输出作为倾向评分,通过调整临床指示的监测方法。此外,不变特征可以被利用作为用于异常检测的ICP特征。
OBJECTIVE: Treatment for intracranial pressure (ICP) has been increasingly informed by machine learning (ML)-derived ICP waveform characteristics. There are gaps, however, in understanding how ICP monitor type may bias waveform characteristics used for these predictive tools since differences between external ventricular drain (EVD) and intraparenchymal monitor (IPM)-derived waveforms have not been well accounted for.
OBJECTIVE: We sought to develop a proof-of-concept ML model differentiating ICP waveforms originating from an EVD or IPM.
METHODS: We examined raw ICP waveform data from the ICU physiology cohort within the prospective Transforming Research and Clinical Knowledge in Traumatic Brain Injury multicenter study.
METHODS: Nested patient-wise five-fold cross-validation and group analysis with bagged decision trees (BDT) and linear discriminant analysis were used for feature selection and fair evaluation. Nine patients were kept as unseen hold-outs for further evaluation.
RESULTS: ICP waveform data totaling 14,110 hours were included from 82 patients (EVD, 47; IPM, 26; both, 9). Mean age, Glasgow Coma Scale (GCS) total, and GCS motor score upon admission, as well as the presence and amount of midline shift, were similar between groups. The model mean area under the receiver operating characteristic curve (AU-ROC) exceeded 0.874 across all folds. In additional rigorous cluster-based subgroup analysis, targeted at testing the resilience of models to cross-validation with smaller subsets constructed to develop models in one confounder set and test them in another subset, AU-ROC exceeded 0.811. In a similar analysis using propensity score-based rather than cluster-based subgroup analysis, the mean AU-ROC exceeded 0.827. Of 842 extracted ICP features, 62 were invariant within every analysis, representing the most accurate and robust differences between ICP monitor types. For the nine patient hold-outs, an AU-ROC of 0.826 was obtained using BDT.
CONCLUSIONS: The developed proof-of-concept ML model identified differences in EVD- and IPM-derived ICP signals, which can provide missing contextual data for large-scale retrospective datasets, prevent bias in computational models that ingest ICP data indiscriminately, and control for confounding using our model\'s output as a propensity score by to adjust for the monitoring method that was clinically indicated. Furthermore, the invariant features may be leveraged as ICP features for anomaly detection.