Summary
The relationship between optimal cerebral perfusion pressure (CPPopt) and patient characteristics has yet to be defined but could have significant implications for future guidelines recommending cerebral perfusion pressure (CPP) targets.
Data from 36 traumatic brain injured patients admitted to neurological intensive care were analysed retrospectively. Linear mixed effects (LME) analysis was performed using an unadjusted-adjusted approach.
Clinical characteristics with p < 0.10 were included in the adjusted model. A second adjusted model which included all variables of interest was created. Model fit was assessed using the root-mean-square error (RMSE).
The adjusted model included time from initiation of intracranial pressure (ICP) monitoring (estimate = 0.00292, p < 0.001), age (estimate = −0.211, p = 0.0750) and the presence of diffuse axonal injury (DAI) (estimate = −35.5, p < 0.001). The RMSE of this model was 8.11 mmHg. The RMSE of the model containing all variables was 8.09 mmHg.
Time, age and the presence of DAI may be important predictors of CPPopt. The models were too inaccurate at predicting CPPopt for employment in clinical practice but warrant further investigation. CPPopt is a dynamic measurement influenced by many factors, supporting the utility of investigating the feasibility of CPPopt-guided therapy.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Rosenfeld JV, Maas AI, Bragge P, Morganti-Kossman C, Manley GT, Gruen RL (2012) Early management of severe traumatic brain injury. Lancet 380(9847):1088–1098
Carney N, Totten AM, O'Reilly C et al (2017) Guidelines for the management of severe traumatic brain injury, 4th edition. Neurosurgery 80(1):6–15
Lassen NA (1959) Cerebral blood flow and oxygen consumption in man. Physiol Rev 39(2):183–238
Payne S (2016) Cerebral autoregulation: control of blood flow in the brain. Springer
Howells T, Elf K, Jones PA et al (2005) Pressure reactivity as a guide in the treatment of cerebral perfusion pressure in patients with brain trauma. J Neurosurg 102(2):311–317
Jaeger M, Dengl M, Meixensberger J, Schuhmann MU (2010) Effects of cerebrovascular pressure reactivity-guided optimization of cerebral perfusion pressure on brain tissue oxygenation after traumatic brain injury. Crit Care Med 38(5):1343–1347
MRC CRASH Trial Collaborators (2008) Predicting outcome after traumatic brain injury: practical prognostic models based on large cohort of international patients. BMJ 336(7641):425–429
Steyerberg EW, Mushkudiani N, Perel P et al (2008) Predicting outcome after traumatic brain injury: development and international validation of prognostic scores based on admission characteristics. PLoS Med 5(8):e165
Depreitere B, Guiza F, Van den Berghe G et al (2014) Pressure autoregulation monitoring and cerebral perfusion pressure target recommendation in patients with severe traumatic brain injury based on minute-by-minute monitoring data. J Neurosurg 120(6):1451–1457
R Core Team (2018) R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. Available from: https://www.R-project.org/
Teasdale G. The Glasgow Structured Approach to Assessment of the Glasgow Coma Scale. Glasgow Coma Scale. Website by: Royal College of Physicians and Surgeons of Glasgow. Available from: https://www.glasgowcomascale.org/ [Accessed 15th March 2019]
Petkus V, Krakauskaite S, Preiksaitis A, Rocka S, Chomskis R, Ragauskas A (2016) Association between the outcome of traumatic brain injury patients and cerebrovascular autoregulation, cerebral perfusion pressure, age, and injury grades. Medicina 52(1):46–53
Petkus V, Preiksaitis A, Krakauskaite S et al (2017) Benefit on optimal cerebral perfusion pressure targeted treatment for traumatic brain injury patients. J Crit Care 41:49–55
Czosnyka M, Balestreri M, Steiner L et al (2005) Age, intracranial pressure, autoregulation, and outcome after brain trauma. J Neurosurg 102(3):450–454
Howells T, Smielewski P, Donnelly J et al (2018) Optimal cerebral perfusion pressure in centers with different treatment protocols. Crit Care Med 46(3):e235–e241
Sorrentino E, Diedler J, Kasprowicz M et al (2012) Critical thresholds for cerebrovascular reactivity after traumatic brain injury. Neurocrit Care 16(2):258–266
Smielewski P, Lavinio A, Timofeev I et al (2008) ICM+, a flexible platform for investigations of cerebrospinal dynamics in clinical practice. Acta Neurochir Suppl 102:145–151
Aries MJ, Czosnyka M, Budohoski KP et al (2012) Continuous determination of optimal cerebral perfusion pressure in traumatic brain injury. Crit Care Med 40(8):2456–2463
Donnelly J, Czosnyka M, Adams H et al (2018) Pressure reactivity-based optimal cerebral perfusion pressure in a traumatic brain injury cohort. Acta Neurochir Suppl 126:209–212
Acknowledgements
This work was financially supported by the Neuroanaesthesia and Critical Care Society of Great Britain and Ireland (NACCSGBI) through the John Snow Anaesthesia Intercalated Award.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Ethics declarations
We declare that we have no conflict of interest.
Rights and permissions
Copyright information
© 2021 Springer Nature Switzerland AG
About this chapter
Cite this chapter
Young, J., Moss, L., Shaw, M., Cahya, E., Kommer, M., Hawthorne, C. (2021). Influence of Patient Demographics on Optimal Cerebral Perfusion Pressure Following Traumatic Brain Injury. In: Depreitere, B., Meyfroidt, G., Güiza, F. (eds) Intracranial Pressure and Neuromonitoring XVII. Acta Neurochirurgica Supplement, vol 131. Springer, Cham. https://doi.org/10.1007/978-3-030-59436-7_31
Download citation
DOI: https://doi.org/10.1007/978-3-030-59436-7_31
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-59435-0
Online ISBN: 978-3-030-59436-7
eBook Packages: MedicineMedicine (R0)