Show simple item record

dc.contributor.authorZeiler, Frederick
dc.contributor.authorSmielewski, Peter
dc.contributor.authorStevens, Andrew
dc.contributor.authorCzosnyka, Marek
dc.contributor.authorMenon, David
dc.contributor.authorErcole, Ari
dc.date.accessioned2018-10-03T04:44:26Z
dc.date.available2018-10-03T04:44:26Z
dc.date.issued2019-03-01
dc.identifier.issn0897-7151
dc.identifier.urihttps://www.repository.cam.ac.uk/handle/1810/283058
dc.description.abstractThe goal was to predict pressure reactivity index (PRx) using non-invasive transcranial Doppler (TCD) based indices of cerebrovascular reactivity, systolic flow index (Sx_a), and mean flow index (Mx_a). Continuous extended duration time series recordings of middle cerebral artery cerebral blood flow velocity (CBFV) were obtained using robotic TCD in parallel with direct intracranial pressure (ICP). PRx, Sx_a, and Mx_a were derived from high frequency archived signals. Using time-series techniques, autoregressive integrative moving average (ARIMA) structure of PRx was determined and embedded in the following linear mixed effects (LME) models of PRx: PRx ∼ Sx_a and PRx ∼ Sx_a + Mx_a. Using 80% of the recorded patient data, the LME models were created and trained. Model superiority was assessed via Akaike information criterion (AIC), Bayesian information criterion (BIC), and log-likelihood (LL). The superior two models were then used to predict PRx using the remaining 20% of the signal data. Predicted and observed PRx were compared via Pearson correlation, linear models, and Bland-Altman (BA) analysis. Ten patients had 3-4 h of continuous uninterrupted ICP and TCD data and were used for this pilot analysis. Optimal ARIMA structure for PRx was determined to be (2,0,2), and this was embedded in all LME models. The top two LME models of PRx were determined to be: PRx ∼ Sx_a and PRx ∼ Sx_a + Mx_a. Estimated and observed PRx values from both models were strongly correlated (r > 0.9; p < 0.0001 for both), with acceptable agreement on BA analysis. Predicted PRx using these two models was also moderately correlated with observed PRx, with acceptable agreement (r = 0.797, p = 0.006; r = 0.763, p = 0.011; respectively). With application of ARIMA and LME modeling, it is possible to predict PRx using non-invasive TCD measures. These are the first and as well as being preliminary attempts at doing so. Much further work is required.
dc.format.mediumPrint-Electronic
dc.languageeng
dc.publisherMary Ann Liebert Inc
dc.subjectMiddle Cerebral Artery
dc.subjectHumans
dc.subjectUltrasonography, Doppler, Transcranial
dc.subjectMonitoring, Physiologic
dc.subjectPilot Projects
dc.subjectCerebrovascular Circulation
dc.subjectIntracranial Pressure
dc.subjectRobotics
dc.subjectModels, Neurological
dc.subjectSignal Processing, Computer-Assisted
dc.subjectBrain Injuries, Traumatic
dc.titleNon-Invasive Pressure Reactivity Index Using Doppler Systolic Flow Parameters: A Pilot Analysis.
dc.typeArticle
prism.endingPage720
prism.issueIdentifier5
prism.publicationDate2019
prism.publicationNameJ Neurotrauma
prism.startingPage713
prism.volume36
dc.identifier.doi10.17863/CAM.30421
dcterms.dateAccepted2018-08-06
rioxxterms.versionofrecord10.1089/neu.2018.5987
rioxxterms.licenseref.urihttp://www.rioxx.net/licenses/all-rights-reserved
rioxxterms.licenseref.startdate2019-03
dc.contributor.orcidZeiler, Frederick [0000-0003-1737-0510]
dc.contributor.orcidSmielewski, Peter [0000-0001-5096-3938]
dc.contributor.orcidCzosnyka, Marek [0000-0003-2446-8006]
dc.contributor.orcidMenon, David [0000-0002-3228-9692]
dc.contributor.orcidErcole, Ari [0000-0001-8350-8093]
dc.identifier.eissn1557-9042
rioxxterms.typeJournal Article/Review
pubs.funder-project-idEuropean Commission (602150)
cam.issuedOnline2018-08-09
rioxxterms.freetoread.startdate2019-08-09


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record