多模态神经监测数据的向量角度分析用于延迟性脑缺血的连续预测。
Vector Angle Analysis of Multimodal Neuromonitoring Data for Continuous Prediction of Delayed Cerebral Ischemia.
机构信息
Department of Neurology, Columbia University, 177 Fort Washington Ave, 8 Milstein - 300 Center, New York, NY, USA.
Program for Hospital and Intensive Care Informatics, Department of Neurology, Columbia University, New York, NY, USA.
出版信息
Neurocrit Care. 2022 Aug;37(Suppl 2):230-236. doi: 10.1007/s12028-022-01481-8. Epub 2022 Mar 30.
BACKGROUND
Dysfunctional cerebral autoregulation often precedes delayed cerebral ischemia (DCI). Currently, there are no data-driven techniques that leverage this information to predict DCI in real time. Our hypothesis is that information using continuous updated analyses of multimodal neuromonitoring and cerebral autoregulation can be deployed to predict DCI.
METHODS
Time series values of intracranial pressure, brain tissue oxygenation, cerebral perfusion pressure (CPP), optimal CPP (CPPOpt), ΔCPP (CPP - CPPOpt), mean arterial pressure, and pressure reactivity index were combined and summarized as vectors. A validated temporal signal angle measurement was modified into a classification algorithm that incorporates hourly data. The time-varying temporal signal angle measurement (TTSAM) algorithm classifies DCI at varying time points by vectorizing and computing the angle between the test and reference time signals. The patient is classified as DCI+ if the error between the time-varying test vector and DCI+ reference vector is smaller than that between the time-varying test vector and DCI- reference vector. Finally, prediction at time point t is calculated as the majority voting over all the available signals. The leave-one-patient-out cross-validation technique was used to train and report the performance of the algorithms. The TTSAM and classifier performance was determined by balanced accuracy, F1 score, true positive, true negative, false positive, and false negative over time.
RESULTS
One hundred thirty-one patients with aneurysmal subarachnoid hemorrhage who underwent multimodal neuromonitoring were identified from two centers (Columbia University: 52 [39.7%], Aachen University: 79 [60.3%]) and included in the analysis. Sixty-four (48.5%) patients had DCI, and DCI was diagnosed 7.2 ± 3.3 days after hemorrhage. The TTSAM algorithm achieved a balanced accuracy of 67.3% and an F1 score of 0.68 at 165 h (6.9 days) from bleed day with a true positive of 0.83, false positive of 0.16, true negative of 0.51, and false negative of 0.49.
CONCLUSIONS
A TTSAM algorithm using multimodal neuromonitoring and cerebral autoregulation calculations shows promise to classify DCI in real time.
背景
大脑自动调节功能障碍通常先于迟发性脑缺血（DCI）发生。目前，尚无利用这一信息实时预测 DCI 的数据驱动技术。我们的假设是，可以利用连续更新的多模态神经监测和大脑自动调节信息来进行预测。
方法
将颅内压、脑组织氧合、脑灌注压（CPP）、最佳 CPP（CPPOpt）、CPP 差值（CPP-CPPOpt）、平均动脉压和压力反应性指数的时间序列值组合并总结为向量。对经过验证的时间信号角度测量方法进行了修改，使其成为一种包含每小时数据的分类算法。时变时间信号角度测量（TTSAM）算法通过将测试和参考时间信号矢量化并计算它们之间的角度，从而在不同的时间点对 DCI 进行分类。如果时变测试向量与 DCI+参考向量之间的误差小于时变测试向量与 DCI-参考向量之间的误差，则将患者分类为 DCI+。最后，通过对所有可用信号进行多数投票来计算 t 时刻的预测。采用留一患者交叉验证技术来训练和报告算法的性能。通过平衡准确率、F1 评分、真阳性、真阴性、假阳性和假阴性来确定 TTSAM 和分类器的性能。
结果
从两个中心（哥伦比亚大学：52 例[39.7%]；亚琛大学：79 例[60.3%]）共确定了 131 例接受多模态神经监测的蛛网膜下腔出血患者，并纳入分析。64 例（48.5%）患者发生了 DCI，DCI 于出血后 7.2±3.3 天诊断。TTSAM 算法在出血后 165 小时（6.9 天）时的平衡准确率为 67.3%，F1 评分为 0.68，真阳性率为 0.83，假阳性率为 0.16，真阴性率为 0.51，假阴性率为 0.49。
结论
使用多模态神经监测和大脑自动调节计算的 TTSAM 算法具有实时分类 DCI 的潜力。
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