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Published in: Journal of Clinical Monitoring and Computing 5/2020

Open Access 01-10-2020 | Central Nervous System Trauma | Original Research

Comparison of high versus low frequency cerebral physiology for cerebrovascular reactivity assessment in traumatic brain injury: a multi-center pilot study

Authors: Eric P. Thelin, Rahul Raj, Bo-Michael Bellander, David Nelson, Anna Piippo-Karjalainen, Jari Siironen, Päivi Tanskanen, Gregory Hawryluk, Mohammed Hasen, Bertram Unger, Frederick A. Zeiler

Published in: Journal of Clinical Monitoring and Computing | Issue 5/2020

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Abstract

Current accepted cerebrovascular reactivity indices suffer from the need of high frequency data capture and export for post-acquisition processing. The role for minute-by-minute data in cerebrovascular reactivity monitoring remains uncertain. The goal was to explore the statistical time-series relationships between intra-cranial pressure (ICP), mean arterial pressure (MAP) and pressure reactivity index (PRx) using both 10-s and minute data update frequency in TBI. Prospective data from 31 patients from 3 centers with moderate/severe TBI and high-frequency archived physiology were reviewed. Both 10-s by 10-s and minute-by-minute mean values were derived for ICP and MAP for each patient. Similarly, PRx was derived using 30 consecutive 10-s data points, updated every minute. While long-PRx (L-PRx) was derived via similar methodology using minute-by-minute data, with L-PRx derived using various window lengths (5, 10, 20, 30, 40, and 60 min; denoted L-PRx_5, etc.). Time-series autoregressive integrative moving average (ARIMA) and vector autoregressive integrative moving average (VARIMA) models were created to analyze the relationship of these parameters over time. ARIMA modelling, Granger causality testing and VARIMA impulse response function (IRF) plotting demonstrated that similar information is carried in minute mean ICP and MAP data, compared to 10-s mean slow-wave ICP and MAP data. Shorter window L-PRx variants, such as L-PRx_5, appear to have a similar ARIMA structure, have a linear association with PRx and display moderate-to-strong correlations (r ~ 0.700, p < 0.0001 for each patient). Thus, these particular L-PRx variants appear closest in nature to standard PRx. ICP and MAP derived via 10-s or minute based averaging display similar statistical time-series structure and co-variance patterns. PRx and L-PRx based on shorter windows also behave similarly over time. These results imply certain L-PRx variants may carry similar information to PRx in TBI.
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Literature
1.
go back to reference Le Roux P, Menon DK, Citerio G, Vespa P, Bader MK, Brophy G, et al. The international multidisciplinary consensus conference on multimodality monitoring in neurocritical care: evidentiary tables: a statement for healthcare professionals from the neurocritical care society and the European society of intensive care medicine. Neurocrit Care. 2014;21(Suppl 2):S297–361.CrossRef Le Roux P, Menon DK, Citerio G, Vespa P, Bader MK, Brophy G, et al. The international multidisciplinary consensus conference on multimodality monitoring in neurocritical care: evidentiary tables: a statement for healthcare professionals from the neurocritical care society and the European society of intensive care medicine. Neurocrit Care. 2014;21(Suppl 2):S297–361.CrossRef
2.
go back to reference Czosnyka M, Miller C. Participants in the international multidisciplinary consensus conference on multimodality monitoring. Monitoring of cerebral autoregulation. Neurocrit Care. 2014;21(Suppl 2):S95–102.CrossRef Czosnyka M, Miller C. Participants in the international multidisciplinary consensus conference on multimodality monitoring. Monitoring of cerebral autoregulation. Neurocrit Care. 2014;21(Suppl 2):S95–102.CrossRef
3.
go back to reference Czosnyka M, Smielewski P, Kirkpatrick P, Laing RJ, Menon D, Pickard JD. Continuous assessment of the cerebral vasomotor reactivity in head injury. Neurosurgery. 1997;41:11–7 discussion 17-19.CrossRef Czosnyka M, Smielewski P, Kirkpatrick P, Laing RJ, Menon D, Pickard JD. Continuous assessment of the cerebral vasomotor reactivity in head injury. Neurosurgery. 1997;41:11–7 discussion 17-19.CrossRef
4.
go back to reference Sorrentino E, Budohoski KP, Kasprowicz M, Smielewski P, Matta B, Pickard JD, et al. Critical thresholds for transcranial Doppler indices of cerebral autoregulation in traumatic brain injury. Neurocrit Care. 2011;14:188–93.CrossRef Sorrentino E, Budohoski KP, Kasprowicz M, Smielewski P, Matta B, Pickard JD, et al. Critical thresholds for transcranial Doppler indices of cerebral autoregulation in traumatic brain injury. Neurocrit Care. 2011;14:188–93.CrossRef
5.
go back to reference Zeiler FA, Ercole A, Cabeleira M, Zoerle T, Stocchetti NN, Menon DK, et al. Univariate comparison of performance of different cerebrovascular reactivity indices for outcome association in adult TBI: a CENTER-TBI study. Acta Neurochir (Wien). 2019;161(6):1217–27.CrossRef Zeiler FA, Ercole A, Cabeleira M, Zoerle T, Stocchetti NN, Menon DK, et al. Univariate comparison of performance of different cerebrovascular reactivity indices for outcome association in adult TBI: a CENTER-TBI study. Acta Neurochir (Wien). 2019;161(6):1217–27.CrossRef
6.
go back to reference Kramer AH, Couillard PL, Zygun DA, Aries MJ, Gallagher CN. Continuous assessment of “optimal” cerebral perfusion pressure in traumatic brain injury: a cohort study of feasibility, reliability, and relation to outcome. Neurocrit Care. 2019;30:51–61.CrossRef Kramer AH, Couillard PL, Zygun DA, Aries MJ, Gallagher CN. Continuous assessment of “optimal” cerebral perfusion pressure in traumatic brain injury: a cohort study of feasibility, reliability, and relation to outcome. Neurocrit Care. 2019;30:51–61.CrossRef
7.
go back to reference Weersink CSA, Aries MJH, Dias C, Liu MX, Kolias AG, Donnelly J, et al. Clinical and physiological events that contribute to the success rate of finding “optimal” cerebral perfusion pressure in severe brain trauma patients. Crit Care Med. 2015;43:1952–63.CrossRef Weersink CSA, Aries MJH, Dias C, Liu MX, Kolias AG, Donnelly J, et al. Clinical and physiological events that contribute to the success rate of finding “optimal” cerebral perfusion pressure in severe brain trauma patients. Crit Care Med. 2015;43:1952–63.CrossRef
8.
go back to reference Donnelly J, Czosnyka M, Adams H, Cardim D, Kolias AG, Zeiler FA, et al. Twenty-Five years of intracranial pressure monitoring after severe traumatic brain injury: a retrospective, single-center analysis. Neurosurgery. 2018;85(1):E75–82.CrossRef Donnelly J, Czosnyka M, Adams H, Cardim D, Kolias AG, Zeiler FA, et al. Twenty-Five years of intracranial pressure monitoring after severe traumatic brain injury: a retrospective, single-center analysis. Neurosurgery. 2018;85(1):E75–82.CrossRef
9.
go back to reference Zeiler FA, Donnelly J, Calviello L, Smielewski P, Menon DK, Czosnyka M. Pressure autoregulation measurement techniques in adult traumatic brain injury, Part II: a scoping review of continuous methods. J Neurotrauma. 2017;34:3224–37.CrossRef Zeiler FA, Donnelly J, Calviello L, Smielewski P, Menon DK, Czosnyka M. Pressure autoregulation measurement techniques in adult traumatic brain injury, Part II: a scoping review of continuous methods. J Neurotrauma. 2017;34:3224–37.CrossRef
10.
go back to reference Brady KM, Lee JK, Kibler KK, Easley RB, Koehler RC, Shaffner DH. Continuous measurement of autoregulation by spontaneous fluctuations in cerebral perfusion pressure: comparison of 3 methods. Stroke. 2008;39:2531–7.CrossRef Brady KM, Lee JK, Kibler KK, Easley RB, Koehler RC, Shaffner DH. Continuous measurement of autoregulation by spontaneous fluctuations in cerebral perfusion pressure: comparison of 3 methods. Stroke. 2008;39:2531–7.CrossRef
11.
go back to reference Zeiler FA, Donnelly J, Calviello L, Lee JK, Smielewski P, Brady K, et al. Validation of pressure reactivity and pulse amplitude indices against the lower limit of autoregulation: Part I: experimental intracranial hypertension. J Neurotrauma. 2018;35(23):2803–11.CrossRef Zeiler FA, Donnelly J, Calviello L, Lee JK, Smielewski P, Brady K, et al. Validation of pressure reactivity and pulse amplitude indices against the lower limit of autoregulation: Part I: experimental intracranial hypertension. J Neurotrauma. 2018;35(23):2803–11.CrossRef
12.
go back to reference Zeiler FA, Lee JK, Smielewski P, Czosnyka M, Brady K. Validation of intracranial pressure-derived cerebrovascular reactivity indices against the lower limit of autoregulation, Part II: experimental model of arterial hypotension. J Neurotrauma. 2018;35(23):2812–9.CrossRef Zeiler FA, Lee JK, Smielewski P, Czosnyka M, Brady K. Validation of intracranial pressure-derived cerebrovascular reactivity indices against the lower limit of autoregulation, Part II: experimental model of arterial hypotension. J Neurotrauma. 2018;35(23):2812–9.CrossRef
13.
go back to reference Timofeev I, Czosnyka M, Carpenter KLH, Nortje J, Kirkpatrick PJ, Al-Rawi PG, et al. Interaction between brain chemistry and physiology after traumatic brain injury: impact of autoregulation and microdialysis catheter location. J Neurotrauma. 2011;28:849–60.CrossRef Timofeev I, Czosnyka M, Carpenter KLH, Nortje J, Kirkpatrick PJ, Al-Rawi PG, et al. Interaction between brain chemistry and physiology after traumatic brain injury: impact of autoregulation and microdialysis catheter location. J Neurotrauma. 2011;28:849–60.CrossRef
14.
go back to reference Timofeev I, Carpenter KLH, Nortje J, Al-Rawi PG, O’Connell MT, Czosnyka M, et al. Cerebral extracellular chemistry and outcome following traumatic brain injury: a microdialysis study of 223 patients. Brain J Neurol. 2011;134:484–94.CrossRef Timofeev I, Carpenter KLH, Nortje J, Al-Rawi PG, O’Connell MT, Czosnyka M, et al. Cerebral extracellular chemistry and outcome following traumatic brain injury: a microdialysis study of 223 patients. Brain J Neurol. 2011;134:484–94.CrossRef
15.
go back to reference Zeiler FA, Thelin EP, Helmy A, Czosnyka M, Hutchinson PJA, Menon DK. A systematic review of cerebral microdialysis and outcomes in TBI: relationships to patient functional outcome, neurophysiologic measures, and tissue outcome. Acta Neurochir (Wien). 2017;159:2245–73.CrossRef Zeiler FA, Thelin EP, Helmy A, Czosnyka M, Hutchinson PJA, Menon DK. A systematic review of cerebral microdialysis and outcomes in TBI: relationships to patient functional outcome, neurophysiologic measures, and tissue outcome. Acta Neurochir (Wien). 2017;159:2245–73.CrossRef
16.
go back to reference Fraser CD, Brady KM, Rhee CJ, Easley RB, Kibler K, Smielewski P, et al. The frequency response of cerebral autoregulation. J Appl Physiol. 1985;2013(115):52–6. Fraser CD, Brady KM, Rhee CJ, Easley RB, Kibler K, Smielewski P, et al. The frequency response of cerebral autoregulation. J Appl Physiol. 1985;2013(115):52–6.
17.
go back to reference Howells T, Johnson U, McKelvey T, Enblad P. An optimal frequency range for assessing the pressure reactivity index in patients with traumatic brain injury. J Clin Monit Comput. 2015;29:97–105.CrossRef Howells T, Johnson U, McKelvey T, Enblad P. An optimal frequency range for assessing the pressure reactivity index in patients with traumatic brain injury. J Clin Monit Comput. 2015;29:97–105.CrossRef
18.
go back to reference Santos E, Diedler J, Sykora M, Orakcioglu B, Kentar M, Czosnyka M, et al. Low-frequency sampling for PRx calculation does not reduce prognostication and produces similar CPPopt in intracerebral haemorrhage patients. Acta Neurochir (Wien). 2011;153:2189–95.CrossRef Santos E, Diedler J, Sykora M, Orakcioglu B, Kentar M, Czosnyka M, et al. Low-frequency sampling for PRx calculation does not reduce prognostication and produces similar CPPopt in intracerebral haemorrhage patients. Acta Neurochir (Wien). 2011;153:2189–95.CrossRef
19.
go back to reference Depreitere B, Güiza F, Van den Berghe G, Schuhmann MU, Maier G, Piper I, et al. Can optimal cerebral perfusion pressure in patients with severe traumatic brain injury be calculated based on minute-by-minute data monitoring? Acta Neurochir Suppl. 2016;122:245–8.CrossRef Depreitere B, Güiza F, Van den Berghe G, Schuhmann MU, Maier G, Piper I, et al. Can optimal cerebral perfusion pressure in patients with severe traumatic brain injury be calculated based on minute-by-minute data monitoring? Acta Neurochir Suppl. 2016;122:245–8.CrossRef
20.
go back to reference 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.CrossRef 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.CrossRef
21.
go back to reference Maas AIR, Menon DK, Steyerberg EW, Citerio G, Lecky F, Manley GT, et al. Collaborative European neurotrauma effectiveness research in traumatic brain injury (CENTER-TBI): a prospective longitudinal observational study. Neurosurgery. 2015;76:67–80.CrossRef Maas AIR, Menon DK, Steyerberg EW, Citerio G, Lecky F, Manley GT, et al. Collaborative European neurotrauma effectiveness research in traumatic brain injury (CENTER-TBI): a prospective longitudinal observational study. Neurosurgery. 2015;76:67–80.CrossRef
22.
go back to reference Carney N, Totten AM, O’Reilly C, Ullman JS, Hawryluk GWJ, Bell MJ, et al. Guidelines for the management of severe traumatic brain injury. Neurosurgery. 2017;80:6–15.CrossRef Carney N, Totten AM, O’Reilly C, Ullman JS, Hawryluk GWJ, Bell MJ, et al. Guidelines for the management of severe traumatic brain injury. Neurosurgery. 2017;80:6–15.CrossRef
23.
go back to reference Doiron D, Marcon Y, Fortier I, Burton P, Ferretti V. Software application profile: opal and mica: open-source software solutions for epidemiological data management, harmonization and dissemination. Int J Epidemiol. 2017;46:1372–8.CrossRef Doiron D, Marcon Y, Fortier I, Burton P, Ferretti V. Software application profile: opal and mica: open-source software solutions for epidemiological data management, harmonization and dissemination. Int J Epidemiol. 2017;46:1372–8.CrossRef
24.
go back to reference Lassen NA. Cerebral blood flow and oxygen consumption in man. Physiol Rev. 1959;39:183–238.CrossRef Lassen NA. Cerebral blood flow and oxygen consumption in man. Physiol Rev. 1959;39:183–238.CrossRef
25.
go back to reference Chatfield C. The analysis of time series: an introduction. 6th ed. Boca Raton: Chapman and Hall/CRC; 2016. Chatfield C. The analysis of time series: an introduction. 6th ed. Boca Raton: Chapman and Hall/CRC; 2016.
26.
go back to reference Zeiler FA, Smielewski P, Stevens A, Czosnyka M, Menon DK, Ercole A. Non-invasive pressure reactivity index using doppler systolic flow parameters: a pilot analysis. J Neurotrauma. 2018; Epub Ahead of Print. Zeiler FA, Smielewski P, Stevens A, Czosnyka M, Menon DK, Ercole A. Non-invasive pressure reactivity index using doppler systolic flow parameters: a pilot analysis. J Neurotrauma. 2018; Epub Ahead of Print.
27.
go back to reference Lutkepohl H. New introduction to multiple time series analysis. 2nd ed. Berlin Heidelberg: Springer; 2010. Lutkepohl H. New introduction to multiple time series analysis. 2nd ed. Berlin Heidelberg: Springer; 2010.
28.
go back to reference Killan L, Lutkepohl H. Structural vector autoregressive analysis. 1st ed. Cambridge: Cambridge University Press; 2017.CrossRef Killan L, Lutkepohl H. Structural vector autoregressive analysis. 1st ed. Cambridge: Cambridge University Press; 2017.CrossRef
Metadata
Title
Comparison of high versus low frequency cerebral physiology for cerebrovascular reactivity assessment in traumatic brain injury: a multi-center pilot study
Authors
Eric P. Thelin
Rahul Raj
Bo-Michael Bellander
David Nelson
Anna Piippo-Karjalainen
Jari Siironen
Päivi Tanskanen
Gregory Hawryluk
Mohammed Hasen
Bertram Unger
Frederick A. Zeiler
Publication date
01-10-2020
Publisher
Springer Netherlands
Published in
Journal of Clinical Monitoring and Computing / Issue 5/2020
Print ISSN: 1387-1307
Electronic ISSN: 1573-2614
DOI
https://doi.org/10.1007/s10877-019-00392-y

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