Cerebral Vascular Changes During Acute Intracranial Pressure Drop
This study applied a new external ventricular catheter, which allows intracranial pressure (ICP) monitoring and cerebral spinal fluid (CSF) drainage simultaneously, to study cerebral vascular responses during acute CSF drainage.
Six patients with 34 external ventricular drain (EVD) opening sessions were retrospectively analyzed. A published algorithm was used to extract morphological features of ICP recordings, and a template-matching algorithm was applied to calculate the likelihood of cerebral vasodilation index (VDI) and cerebral vasoconstriction index (VCI) based on the changes of ICP waveforms during CSF drainage. Power change (∆P) of ICP B-waves after EVD opening was also calculated. Cerebral autoregulation (CA) was assessed through phase difference between arterial blood pressure (ABP) and ICP using a previously published wavelet-based algorithm.
The result showed that acute CSF drainage reduced mean ICP (P = 0.016) increased VCI (P = 0.02) and reduced ICP B-wave power (P = 0.016) significantly. VCI reacted to ICP changes negatively when ICP was between 10 and 25 mmHg, and VCI remained unchanged when ICP was outside the 10–25 mmHg range. VCI negatively (r = − 0.44) and VDI positively (r = 0.82) correlated with ∆P of ICP B-waves, indicating that stronger vasoconstriction resulted in bigger power drop in ICP B-waves. Better CA prior to EVD opening triggered bigger drop in the power of ICP B-waves (r = − 0.612).
This study demonstrates that acute CSF drainage reduces mean ICP, and results in vasoconstriction which can be detected through an index, VCI. Cerebral vessels actively respond to ICP changes or cerebral perfusion pressure (CPP) changes in a certain range; beyond which, the vessels are insensitive to the changes in ICP and CPP.
KeywordsCerebral autoregulation Cerebrospinal fluid drainage Cerebral vascular changes Intracranial pressure waveform ICP B-waves
The concept and study design were formed by X.H., X.Y.L., P.V., L.Z. and X.L.L. Data acquisition was conducted by P.V., N.H. and L.Z. Data analysis was conducted by X.Y.L., N.H., X.L.L and X.H. Drafting of the manuscript and figures was contributed by X.Y.L., X.H., L.Z., P.V., N.H. and X.L.L.
Source of support
This work was partially supported by the UCSF Middle Career Scientist Award, UCSF Institute for Computational Health Sciences, and National Institutes of Health awards (R01NS076738 and NS106905-01A1).
Compliance with Ethical Standards
Conflict of interest
The authors declare that they have no conflict of interest.
The institutional review board (IRB) approved the data analysis and waived the need for consenting patients because of the retrospective nature of the study.
- 9.Staykov D, Kuramatsu JB, Bardutzky J, Volbers B, Gerner ST, Kloska SP, et al. Efficacy and safety of combined intraventricular fibrinolysis with lumbar drainage for prevention of permanent shunt dependency after intracerebral hemorrhage with severe ventricular involvement: A randomized trial and individual patient data meta-analysis. Ann Neurol. 2017;81:93–103.CrossRefPubMedGoogle Scholar
- 16.Slazinkski T, Anderson T, Cattell E, Eigsti J, Heimsoth S. Care of the patient undergoing intracranial pressure monitoring/external ventricular drainage or lumbar drainage. AANN Clin Pract Guidel Ser. 2011:1–38.Google Scholar
- 17.Integra Design Verification Report for Camino Flex Ventricular Catheter. 49–51.Google Scholar
- 37.Kvandal P, Sheppard L, Landsverk SA, Stefanovska A, Kirkeboen KA. Impaired cerebrovascular reactivity after acute traumatic brain injury can be detected by wavelet phase coherence analysis of the intracranial and arterial blood pressure signals. J Clin Monit Comput. 2013;27:375–83.CrossRefPubMedPubMedCentralGoogle Scholar
- 38.Ps A. The illustrated wavelet transform handbook, introductory theory and applications in science, engineering, medicine and finance. New York: Talor and Francis; 2002.Google Scholar
- 39.Addison PS. The illustrated wavelet transform handbook: introductory theory and applications in science, engineering, medicine and finance. 2nd ed. Boca Raton: CRC Press; 2016.Google Scholar
- 49.Physics C, Hospital SG, Kingdom U. Pressure autoregulation monitoring and cerebral perfusion pressure target recommendation in patients with severe traumatic brain injury based on minute-by-minute monitoring data. 2014;120:1451–7.Google Scholar
- 55.Addison PS. Identifying stable phase coupling associated with cerebral autoregulation using the synchrosqueezed cross-wavelet transform and low oscillation Morlet wavelets. Conf Proc IEEE Eng Med Biol Soc. 2015;8:5960–3.Google Scholar
- 59.Liu X, Czosnyka M, Donnelly J, Budohoski KP, Varsos GV, Nasr N, et al. Comparison of frequency and time domain methods of assessment of cerebral autoregulation in traumatic brain injury. J Cereb Blood Flow Metab. 2014;11:1–9.Google Scholar
- 61.Depreitere B, Güiza F, Van den Berghe G, Schuhmann MU, Maier G, Piper I, et al. 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. 2014;120:1451–7.CrossRefPubMedGoogle Scholar