Research Article: Temporal profile of intracranial pressure and cerebrovascular reactivity in severe traumatic brain injury and association with fatal outcome: An observational study

Date Published: July 25, 2017

Publisher: Public Library of Science

Author(s): Hadie Adams, Joseph Donnelly, Marek Czosnyka, Angelos G. Kolias, Adel Helmy, David K. Menon, Peter Smielewski, Peter J. Hutchinson, Martin Schreiber

Abstract: BackgroundBoth intracranial pressure (ICP) and the cerebrovascular pressure reactivity represent the dysregulation of pathways directly involved in traumatic brain injury (TBI) pathogenesis and have been used to inform clinical management. However, how these parameters evolve over time following injury and whether this evolution has any prognostic importance have not been studied.Methods and findingsWe analysed the temporal profile of ICP and pressure reactivity index (PRx), examined their relation to TBI-specific mortality, and determined if the prognostic relevance of these parameters was affected by their temporal profile using mixed models for repeated measures of ICP and PRx for the first 240 hours from the time of injury. A total of 601 adults with TBI, admitted between September 2002 to January 2016, and with high-resolution continuous monitoring from a single centre, were studied. At 6 months postinjury, 133 (19%) patients had a fatal outcome; of those, 88 (78%) died from nonsurvivable TBI or brain death. The difference in mean ICP between those with a fatal outcome and functional survivors was only significant for the first 168 hours after injury (all p < 0.05). For PRx, those patients with a fatal outcome also had a higher (more impaired) PRx throughout the first 120 hours after injury (all p < 0.05). The separation of ICP and PRx was greatest in the first 72 hours after injury. Mixed models demonstrated that the explanatory power of the PRx decreases over time; therefore, the prognostic weight assigned to PRx should similarly decrease. However, the ability of ICP to predict a fatal outcome remained relatively stable over time. As control of ICP is the central purpose of TBI management, it is likely that some of the information that is reflected in the natural history of ICP changes is no longer apparent because of therapeutic intervention.ConclusionsWe demonstrated the temporal evolution of ICP and PRx and their relationship with fatal outcome, indicating a potential early prognostic and therapeutic window. The combination of dynamic monitoring variables and their time profile improved prediction of outcome. Therefore, time-driven dynamic modelling of outcome in patients with severe TBI may allow for more accurate and clinically useful prediction models. Further research is needed to confirm and expand on these findings.

Partial Text: Traumatic brain injury (TBI) is a major worldwide cause of morbidity and mortality [1]; in Europe alone, some 7.7 million people are living with TBI-induced disabilities [2], and of those with severe TBI (sTBI), a quarter to a third will die [3]. Furthermore, rates of severe morbidity and mortality have not improved over the last 20 years [1,3]. This burden of disability and mortality highlights the urgent need for novel strategies to decrease the prevalence and improve the management of TBI [4].

In a cohort study of 601 patients with sTBI, we demonstrate that despite relatively stable ICP throughout the first 10 days postinjury, cerebral pressure reactivity is impaired early after injury. Furthermore, studying the prognostic importance of brain physiological parameters after separating neurological from non-neurological causes of death resulted in a strong relationship of both ICP and pressure reactivity with fatal outcome from neurological causes, which is clinically meaningful and arguably more relevant. Finally, the inclusion of ICP and pressure reactivity into a dynamic predictive model demonstrated the importance of the temporal profile of these parameters; inclusion of ICP or pressure reactivity significantly improved our ability to predict patient outcome when compared to static variables. Currently, the most commonly used TBI prediction models only utilise fixed variables [37,38].



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