Statistical properties of cerebral near infrared and intracranial pressure-based cerebrovascular reactivity metrics in moderate and severe neural injury: a machine learning and time-series analysis

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dc.contributor.authorGomez, Alwyn
dc.contributor.authorSainbhi, Amanjyot S.
dc.contributor.authorStein, Kevin Y.
dc.contributor.authorVakitbilir, Nuray
dc.contributor.authorFroese, Logan
dc.contributor.authorZeiler, Frederick
dc.date.accessioned2023-11-14T19:33:48Z
dc.date.available2023-11-14T19:33:48Z
dc.date.issued2023-08-28
dc.date.updated2023-11-03T09:19:11Z
dc.description.abstractBackground 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 (rSO2) as a surrogate for pulsatile cerebral blood volume (CBV). In this study, the relationship between ICP- and rSO2-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. Methods 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 rSO2-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 rSO2 (ΔICP and ΔrSO2, respectively) to an impulse in change in arterial blood pressure (ΔABP) was also examined for similarities. Results 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 rSO2- and ICP-based indices, separate from other physiologic parameters. Additionally, modeled responses of ΔICP and ΔrSO2 to impulses in ΔABP were similar, indicating that ΔrSO2 may be a valid surrogate for pulsatile CBV. Conclusions rSO2- and ICP-based indices of cerebrovascular reactivity relate to one another in higher dimensional physiologic space. ΔICP and ΔrSO2 behave similar in modeled responses to impulses in ΔABP. This work strengthens the body of evidence supporting the similarities between ICP-based and rSO2-based indices of cerebrovascular reactivity and opens the door to cerebrovascular reactivity monitoring in settings where invasive ICP monitoring is not feasible.
dc.identifier.citationIntensive Care Medicine Experimental. 2023 Aug 28;11(1):57
dc.identifier.doi10.1186/s40635-023-00541-3
dc.identifier.urihttp://hdl.handle.net/1993/37784
dc.language.isoeng
dc.language.rfc3066en
dc.publisherSpringerOpen
dc.rightsopen accessen_US
dc.rights.holderEuropean Society of Intensive Care Medicine and Springer Nature Switzerland AG
dc.subjectCerebrovascular reactivity
dc.subjectMulti-modal monitoring
dc.subjectNear infrared spectroscopy
dc.subjectTraumatic brain injury
dc.titleStatistical properties of cerebral near infrared and intracranial pressure-based cerebrovascular reactivity metrics in moderate and severe neural injury: a machine learning and time-series analysis
dc.typeJournal Article
local.author.affiliationRady Faculty of Health Sciences::Max Rady College of Medicine::Department of Surgery
oaire.citation.issue57
oaire.citation.titleIntensive Care Medicine Experimental
oaire.citation.volume11
project.funder.identifierhttps://doi.org/10.13039/501100000038
project.funder.nameNatural Sciences and Engineering Research Council of Canada
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