Gomez Alwyn, Sainbhi Amanjyot Singh, Stein Kevin Y, Vakitbilir Nuray, Froese Logan, Zeiler Frederick A
Section of Neurosurgery, Department of Surgery, Rady Faculty of Health Sciences, University of Manitoba, Winnipeg, MB, Canada.
Department of Human Anatomy and Cell Science, Rady Faculty of Health Sciences, University of Manitoba, Winnipeg, Canada.
Intensive Care Med Exp. 2023 Aug 28;11(1):57. doi: 10.1186/s40635-023-00541-3.
Cerebrovascular reactivity has been identified as a key contributor to secondary injury following traumatic brain injury (TBI). Prevalent intracranial pressure (ICP) based indices of cerebrovascular reactivity are limited by their invasive nature and poor spatial resolution. Fortunately, interest has been building around near infrared spectroscopy (NIRS) based measures of cerebrovascular reactivity that utilize regional cerebral oxygen saturation (rSO) as a surrogate for pulsatile cerebral blood volume (CBV). In this study, the relationship between ICP- and rSO-based indices of cerebrovascular reactivity, in a cohort of critically ill TBI patients, is explored using classical machine learning clustering techniques and multivariate time-series analysis.
High-resolution physiologic data were collected in a cohort of adult moderate to severe TBI patients at a single quaternary care site. From this data both ICP- and rSO-based indices of cerebrovascular reactivity were derived. Utilizing agglomerative hierarchical clustering and principal component analysis, the relationship between these indices in higher dimensional physiologic space was examined. Additionally, using vector autoregressive modeling, the response of change in ICP and rSO (ΔICP and ΔrSO, respectively) to an impulse in change in arterial blood pressure (ΔABP) was also examined for similarities.
A total of 83 patients with 428,775 min of unique and complete physiologic data were obtained. Through agglomerative hierarchical clustering and principal component analysis, there was higher order clustering between rSO- and ICP-based indices, separate from other physiologic parameters. Additionally, modeled responses of ΔICP and ΔrSO to impulses in ΔABP were similar, indicating that ΔrSO may be a valid surrogate for pulsatile CBV.
rSO- and ICP-based indices of cerebrovascular reactivity relate to one another in higher dimensional physiologic space. ΔICP and ΔrSO behave similar in modeled responses to impulses in ΔABP. This work strengthens the body of evidence supporting the similarities between ICP-based and rSO-based indices of cerebrovascular reactivity and opens the door to cerebrovascular reactivity monitoring in settings where invasive ICP monitoring is not feasible.
脑血管反应性已被确定为创伤性脑损伤（TBI）后继发性损伤的关键因素。基于颅内压（ICP）的脑血管反应性指标普遍存在侵入性强和空间分辨率差的局限性。幸运的是，基于近红外光谱（NIRS）的脑血管反应性测量方法越来越受到关注，该方法利用局部脑氧饱和度（rSO）作为搏动性脑血容量（CBV）的替代指标。在本研究中，使用经典机器学习聚类技术和多变量时间序列分析，探讨了一组重症TBI患者中基于ICP和基于rSO的脑血管反应性指标之间的关系。
在一个四级医疗中心收集了一组成年中重度TBI患者的高分辨率生理数据。从这些数据中得出了基于ICP和基于rSO的脑血管反应性指标。利用凝聚层次聚类和主成分分析，研究了这些指标在高维生理空间中的关系。此外，使用向量自回归模型，还研究了ICP和rSO的变化（分别为ΔICP和ΔrSO）对动脉血压变化（ΔABP）脉冲的反应是否相似。
共获得83例患者的428,775分钟独特且完整的生理数据。通过凝聚层次聚类和主成分分析，基于rSO和基于ICP的指标之间存在更高阶的聚类，与其他生理参数不同。此外，ΔICP和ΔrSO对ΔABP脉冲的模拟反应相似，表明ΔrSO可能是搏动性CBV的有效替代指标。
基于rSO和基于ICP的脑血管反应性指标在高维生理空间中相互关联。ΔICP和ΔrSO在对ΔABP脉冲的模拟反应中表现相似。这项工作加强了支持基于ICP和基于rSO的脑血管反应性指标之间相似性这一证据体系，并为在侵入性ICP监测不可行的情况下进行脑血管反应性监测打开了大门。
