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Pressure reactivity index for early neuroprognostication in poor-grade subarachnoid hemorrhage.

MedStar author(s):

Armonda, Rocco A

, MWHC

Citation: Journal of the Neurological Sciences. 450:120691, 2023 07 15.PMID: 37267816Institution: MedStar Washington Hospital CenterDepartment: Neurosurgery | Surgery/Surgical Critical CareForm of publication: Journal ArticleMedline article type(s): Journal ArticleSubject headings: *Stroke | *Subarachnoid Hemorrhage | Blood Pressure/ph [Physiology] | Cerebrovascular Circulation/ph [Physiology] | Humans | Intracranial Pressure/ph [Physiology] | Logistic Models | Retrospective Studies | Year: 2023 ISSN:

0022-510X

Name of journal: Journal of the neurological sciencesAbstract: BACKGROUND: Pressure reactivity index (PRx) utilizes moving correlation coefficients from intracranial pressure (ICP) and mean arterial pressures to evaluate cerebral autoregulation. We evaluated patients with poor-grade subarachnoid hemorrhage (SAH), identified their PRx trajectories over time, and identified threshold time points where PRx could be used for neuroprognostication.CONCLUSIONS: Our results suggest that by using PRx trends, early neuroprognostication in patients with SAH and poor clinical exams may start becoming apparent at post-ictus day 8 and reach adequate sensitivities by post-ictus days 12-14. Further study is required to validate this in larger poor-grade SAH populations. Copyright © 2023. Published by Elsevier B.V.METHODS: Patients with poor-grade SAH were identified and received continuous bolt ICP measurements. Dichotomized outcomes were based on ninety-day modified Rankin scores and disposition. Smoothed PRx trajectories for each patient were created to generate "candidate features" that looked at daily average PRx, cumulative first-order changes in PRx, and cumulative second-order changes in PRx. "Candidate features" were then used to perform penalized logistic regression analysis using poor outcome as the dependent variable. Penalized logistic regression models that maximized specificity for poor outcome were generated over several time periods and evaluated how sensitivities changed over time.RESULTS: 16 patients with poor-grade SAH were evaluated. Average PRx trajectories for the good (PRx < 0.25) and poor outcome groups (PRx > 0.5) started diverging at post-ictus day 8. When targeting specificities >=88% for poor outcome, sensitivities for poor outcome consistently increased to >70% starting at post-ictus days 12-14 with a maximum sensitivity of 75% occurring at day 18.All authors: Armonda RA, Aulisi EF, Chang JJ, Felbaum DR, Kepplinger D, Mai JC, Metter EJFiscal year: FY2024 Digital Object Identifier:

https://dx.doi.org/10.1016/j.jns.2023.120691

Date added to catalog: 2023-07-27

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Title notes ( 5 )

Holdings
Item type	Current library	Collection	Call number	Status	Date due	Barcode
Journal Article	MedStar Authors Catalog	Article	37267816	Available		37267816

BACKGROUND: Pressure reactivity index (PRx) utilizes moving correlation coefficients from intracranial pressure (ICP) and mean arterial pressures to evaluate cerebral autoregulation. We evaluated patients with poor-grade subarachnoid hemorrhage (SAH), identified their PRx trajectories over time, and identified threshold time points where PRx could be used for neuroprognostication.

CONCLUSIONS: Our results suggest that by using PRx trends, early neuroprognostication in patients with SAH and poor clinical exams may start becoming apparent at post-ictus day 8 and reach adequate sensitivities by post-ictus days 12-14. Further study is required to validate this in larger poor-grade SAH populations. Copyright © 2023. Published by Elsevier B.V.

METHODS: Patients with poor-grade SAH were identified and received continuous bolt ICP measurements. Dichotomized outcomes were based on ninety-day modified Rankin scores and disposition. Smoothed PRx trajectories for each patient were created to generate "candidate features" that looked at daily average PRx, cumulative first-order changes in PRx, and cumulative second-order changes in PRx. "Candidate features" were then used to perform penalized logistic regression analysis using poor outcome as the dependent variable. Penalized logistic regression models that maximized specificity for poor outcome were generated over several time periods and evaluated how sensitivities changed over time.

RESULTS: 16 patients with poor-grade SAH were evaluated. Average PRx trajectories for the good (PRx < 0.25) and poor outcome groups (PRx > 0.5) started diverging at post-ictus day 8. When targeting specificities >=88% for poor outcome, sensitivities for poor outcome consistently increased to >70% starting at post-ictus days 12-14 with a maximum sensitivity of 75% occurring at day 18.

English