Abstract
Understanding changes in cerebral oxygenation, haemodynamics and metabolism holds the key to individualised, optimised therapy after acute brain injury. Near-infrared spectroscopy (NIRS) offers the potential for non-invasive, continuous bedside measurement of surrogates for these processes. Interest has grown in applying this technique to interpret cerebrovascular pressure reactivity (CVPR), a surrogate of the brain’s ability to autoregulate blood flow. We describe a physiological model-based approach to NIRS interpretation which predicts autoregulatory efficiency from a model parameter k_aut. Data from three critically brain-injured patients exhibiting a change in CVPR were investigated. An optimal value for k_aut was determined to minimise the difference between measured and simulated outputs. Optimal values for k_aut appropriately tracked changes in CVPR under most circumstances. Further development of this technique could be used to track CVPR providing targets for individualised management of patients with altered vascular reactivity, minimising secondary neurological insults.
Keywords
The original version of this chapter was revised. An erratum to this chapter can be found at DOI 10.1007/978-1-4614-4989-8_53
1 Introduction
Cerebral blood flow (CBF) is tightly regulated by cerebral autoregulation (CA), forming a critical link between oxygen supply and demand. Myogenic, metabolic and neurological mechanisms lead to a complex pattern of vascular reactivity over different time scales combining to maintain constant perfusion across a wide range of perfusion pressure. Following brain injury, acute disturbances of CA may lead to hyper or hypo-perfusion and secondary neurological insults; maintaining cerebral perfusion is thus a core goal during the neurointensive care treatment of brain injury. However, delivering this is not straightforward as there is no convenient means of monitoring CBF or CA continuously at the bedside.
Measures of vascular reactivity, derived using surrogates of cerebral blood volume (CBV) or CBF, may be compared with arterial blood pressure (ABP) to investigate efficiency of cerebrovascular pressure reactivity (CVPR) and CA [1]. Recently near-infrared spectroscopy (NIRS) has been investigated in this regard as different NIRS indices reflect aspects of cerebral haemodynamics [2, 3]. Specifically, cerebral tissue oxygen saturation (TOS) and total haemoglobin have been applied as surrogates of CBF and CBV, respectively. When correlated with ABP these indices agree with well-established indices of CVPR [4, 5].
While these modes of analysis are simple and easily performed at the bedside, they do not account for the non-stationary and non-linear complexity within the range of measured signals. A model-based approach might make best use of the available data combining a priori knowledge of complex cerebral physiology with multiple measured variables to establish fully informed physiological predictions. This might account for additional important contributions to our interpretation of NIRS measured signals such as changes in CO2 or O2 tension, cerebral metabolic rate (CMRO2) and arterial to venous volume ratio.
We have previously described a physiological model of cerebral haemodynamics, oxygenation and metabolism and used this to aid interpretation of NIRS signals during cerebral physiological challenges in healthy volunteers [6]. The model combines haemodynamic, metabolic and oxygenation components creating simulated outputs of a range of measured signals. Variation in the model parameters from their basal values alters simulated outputs in a way which may mirror changes in underlying physiological processes. The model parameter k_aut has been designed to represent changes in the efficiency of CA ranging from 0 with an absence of CA to 1 where it is completely intact. This work translates our model [6] into the pathophysiological context of brain injury. The aim of this work is to use a range of measured signals, including NIRS, to identify a model-derived parameter as a biomarker of CA in individual patients.
2 Methods
Three acutely brain-injured patients showing variation in CVPR were identified from an ongoing multimodal monitoring study in brain-injured patients. This study was approved by the institutional Research Ethics Committee and assent was gained from patient representatives.
For each patient dataset, CVPR was initially characterised using the pressure reactivity index (PRx) and mean velocity index (Mx) [1]. Two 30-min epochs were analysed for each patient, one with reactivity indices <0.3 suggesting intact CA and one >0.3 suggesting loss of CA. NIRS monitoring was performed with the NIRO 100 (Hammamatsu Photonics KK) ipsilateral to intraparenchymal intracranial pressure (ICP) monitoring and transcranial Doppler flow velocity of the middle cerebral artery (Vmca) (DWL Doppler Box, Compumedics, Germany). NIRS measurements included spatially resolved tissue oxygenation index (TOI) and normalised total haemoglobin index (nTHI) representing measures of TOS and total haemoglobin, respectively. Changes in concentration of oxyhaemoglobin (∆[HbO2]) and deoxyhaemoglobin (∆[HHb]) were determined by the modified Beer–Lambert method. Invasive ABP from a radial artery catheter, end tidal CO2 (ETCO2) and pulse oximetry (SpO2) were gathered through an Intellivue monitor (Philips, N.V., Amsterdam, The Netherlands). Signals were synchronised, downsampled to 1 Hz and filtered with a lowpass 0.1 Hz fifth-order Butterworth filter to remove high frequency noise and respiratory influences. Of these measured signals ABP, ETCO2 (approximating PaCO2), SpO2 and ICP were used as model inputs. These produced simulated outputs for CBF, total haemoglobin ([HbT]), [HbO2], [HHb] and TOS which were compared with their measured counterparts Vmca and NIRS (nTHI, ∆[HbO2], ∆[HHb], TOI).
Optimisation was performed by minimising the difference between measured signals and simulated outputs for Vmca and NIRS finding optimal values for parameter k_aut (representing CA) and an additional parameter u reflecting cerebral energy demand. Reduction of this additional parameter below its basal level simulating a reduction in cerebral metabolism was required to adequately fit the measured NIRS signals. This seems physiologically plausible because all patients were deeply sedated at the time of study. k_aut values produced by these different optimisation strategies were compared to index-based predictions of CVPR for consistency. The difference between simulated outputs and measured signals is expressed as the mean absolute difference between the two. The improvement in the fit following optimisation is given as the percentage difference between measured signals and simulated outputs at basal parameter settings and optimised parameter settings divided by the basal value.
3 Results
A high k_aut was associated with intact CVPR and a low value disturbed CVPR in all simulations excluding those optimised on the basis of TOI. An example dataset is shown in Fig. 13.1 demonstrating disturbed CA. It can be seen that simulation with a low value of k_aut (0.3), reflecting dramatically impaired autoregulation, simulates Vmca and nTHI most accurately.
Model simulations using different measured signals for optimisation of k_aut varied in the relationship between k_aut and predicted CVPR. When k_aut is optimised by minimising the difference between measured Vmca and simulated CBF alone in all epochs (Table 13.1), there is accurate prediction of Vmca (mean absolute difference 1.98 cm/s). Post-optimisation k_aut values are lower in the epochs with reduced CVPR (0.53), suggesting that k_aut appropriately reflects the level of CA. When measured NIRS signals are included in this strategy (Table 13.2) it is possible to account for the changes in nTHI by optimising k_aut alone. This continues to predict appropriate values of k_aut (Table 13.2, column 1). To adequately fit measured and simulated ∆[HbO2] and ∆[HHb], optimisation of u reducing cerebral metabolism was required. Again, this approach predicts lower values of k_aut (0.47) in those with reduced CVPR. However, to achieve the best fit requires a value for u that is unphysiologically low.
Inclusion of TOI within the optimisation strategy is problematic and it is not possible to fit TOI well in combination with other measured signals (Table 13.2, column 3). Optimal values for k_aut do not reflect the level of predicted CA in this final approach. The behaviour of measured TOI differs significantly from simulated outputs of TOS; large simulated changes in TOS result from large changes in CBF which are not present in the measured TOI (Fig. 13.2).
4 Discussion
We have identified a model parameter k_aut which simulates changes in CA and improves prediction of NIRS signals in brain injury. The optimal value of k_aut may thus represent a composite biomarker of cerebral autoregulatory function informed from multiple NIRS inputs. This approach aims to form cohesive physiological predictions based on prior knowledge of physiology, maximising the potential of the available data.
Fitting nTHI was least problematic probably because fewer physiological processes influence this signal. In comparison ∆[HbO2], ∆[HHb] and TOS encode metabolic components and differential effects of arterial and venous components become more influential. It was impossible to achieve an adequate fit for TOS by varying only the model parameters k_aut and u. Despite qualitative agreement, the magnitude of variation and baseline saturation showed large discrepancies. It is unlikely that further optimisation within physiological plausibility could explain the lack of variability despite the large changes in CBF observed. However, a differing baseline is more easily explained. Similar observations were made during studies in healthy volunteers [7], but in this case TOS could be explained by adjusting the extracerebral:intracerebral signal weighting to 80:20 or doubling the venous volume, both of which seem unlikely. Studies such as these indicate that accurate prediction and interpretation of TOS might require combined modelling of cerebral physiology and light transport in tissue.
This study was of limited power including only six epochs from three patients. However these datasets demonstrate an extreme of physiological dysfunction with large changes in ABP and CBF, representing an excellent challenge for our model. Further work must include large numbers of patients undergoing a range of physiological challenges to increase the quality of measured signals in patients with lesser degrees of impaired CA. Although this approach has not necessarily been followed for many established indices of CVPR it should be viewed as a prerequisite to translation into the clinic.
With further investigation, model-informed interpretation of NIRS signals might offer enhanced prediction of CA across widely varying physiological and pathophysiological contexts. Prior knowledge of population characteristics and further model simplification should improve computational efficiency and move toward bedside implementation. This form of interpretation progresses beyond simple correlation analyses by combining information from multiple NIRS and systemic measures with a priori knowledge of physiology to provide cohesive predictions of cerebral well-being. Thus, use of k_aut as a biomarker of CA efficiency could inform pathophysiology and potentially provide a target for physiological optimisations to improve outcome.
References
Czosnyka M, Smielewski P, Kirkpatrick P et al (1998) Continuous monitoring of cerebrovascular pressure-reactivity in head injury. Acta Neurochir Suppl 71:74–77
Lee J, Kibler K, Benni P et al (2009) Cerebrovascular reactivity measured by near-infrared spectroscopy. Stroke 40(5):1820–1826
Brady K, Lee J, Kibler K et al (2007) Continuous time-domain analysis of cerebrovascular autoregulation using near-infrared spectroscopy. Stroke 38(10):2818–2825
Zweifel C, Castellani G, Czosnyka M et al (2010) Noninvasive monitoring of cerebrovascular reactivity with near infrared spectroscopy in head-injured patients. J Neurotrauma 27(11):1951–1958
Zweifel C, Castellani G, Czosnyka M et al (2010) Continuous assessment of cerebral autoregulation with near-infrared spectroscopy in adults after subarachnoid hemorrhage. Stroke 41(9):1963–1968
Banaji M, Mallet A, Elwell C et al (2008) A model of brain circulation and metabolism: NIRS signal changes during physiological challenges. PLoS Comput Biol 4(11):e1000212
Moroz T, Banaji M, Tisdall M et al (2012) Development of a model to aid NIRS data interpretation: results from a hypercapnia study in healthy adults. Adv Exp Med Biol 737:293–300
Acknowledgements
This work was undertaken at University College London Hospitals and partially funded by the Department of Health’s National Institute for Health Research Centres funding scheme. Support has also been provided by the Medical Research Council and Wellcome Trust. The authors are indebted to the medical and nursing staff of the Neurocritical Care Unit at the National Hospital for Neurology & Neurosurgery and to the study patients and their families.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
This chapter is published under an open access license. Please check the 'Copyright Information' section either on this page or in the PDF for details of this license and what re-use is permitted. If your intended use exceeds what is permitted by the license or if you are unable to locate the licence and re-use information, please contact the Rights and Permissions team.
Copyright information
© 2013 The Author(s)
About this paper
Cite this paper
Highton, D. et al. (2013). Modelling Cerebrovascular Reactivity: A Novel Near-Infrared Biomarker of Cerebral Autoregulation?. In: Welch, W.J., Palm, F., Bruley, D.F., Harrison, D.K. (eds) Oxygen Transport to Tissue XXXIV. Advances in Experimental Medicine and Biology, vol 765. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-4989-8_13
Download citation
DOI: https://doi.org/10.1007/978-1-4614-4989-8_13
Published:
Publisher Name: Springer, New York, NY
Print ISBN: 978-1-4614-4771-9
Online ISBN: 978-1-4614-4989-8
eBook Packages: Biomedical and Life SciencesBiomedical and Life Sciences (R0)