SUPPLEMENTARY APPEND
Chapter 4. Preliminary guideline for transfer function analysis
Aisha SS Meel-van den Abeelen Jurgen AHR Claassen
David M Simpson Ronney B. Panerai
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ABSTRACT
The brain is highly dependent on a continuous supply of (oxygenated) blood. Cerebral autoregulation (CA) is a key mechanism to protect the brain against excessive fluctuations in blood pressure (BP) to maintain adequate cerebral blood flow. The ability to measure and monitor CA in a patient would provide clinically useful information and would possibly permit a more individualized physiologically based therapy aimed at reducing the risk of secondary brain injury.
A widely used technique to quantify CA in a non-invasive way is analysing the relationship between spontaneous BP and cerebral blood flow velocity (CBFV) using transfer function analysis (TFA). Despite the frequent use of TFA, however, settings and qualifying terms for the interpretation of outcome metrics, for example criteria for “impaired CA”, have never been strictly defined, leading to limitations of comparison between existing literature, poor standardization and postponement of potential clinical benefits.
The purpose of the present guideline is to establish a first consensus document on CA quantification using TFA with the use of BP and CBFV signals (obtained with transcranial Doppler ultrasonography (TCD)), to improve reproducibility and implementation of study results.
The development of these guidelines was initiated by (but not confined to) The Cerebral Autoregulation Research Network (CARNet - www.car-net.org). This document reflects what is currently considered best practice in awake and cooperating patients with TCD monitoring. The proposed settings are not intended to necessarily indicate what is ‘best’ (by whatever criterion) and we emphasize that the discussion is ongoing, probably leading to future revisions in the light of new evidence. As a first step towards implementation, we encourage researchers to compare their practice to the settings as they are recommended in this consensus paper.
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98 P reli mi na ry gui d eli ne f or t ra ns fe r f uncti on a na ly sisINTRODUCTION
Perfusion of the human brain is more or less maintained by a set of control systems including chemo- and autoregulation, neurovascular coupling and probably a direct autonomic neurovascular influence. Myogenic mechanisms are traditionally represented by the brain’s capacity to autoregulate its own blood flow and cerebral autoregulation (CA) is accordingly defined as the intrinsic ability of the brain to maintain cerebral perfusion in the presence of blood pressure (BP) changes. As the brain is highly dependent on a continuous supply of (oxygenated) blood, reduced effectiveness of CA renders the brain more sensitive to both hypo- and hyperperfusion. In a variety of medical conditions, such as serious hypertension, diabetes, dementia, stroke, head trauma, subarachnoid haemorrhage as well as during surgical procedures [1-5], defect CA may play an important role in the pathogenesis of brain damage. Therefore, the ability to measure and monitor CA in a patient would provide clinically useful information and would possibly permit a more individualized physiological based therapy aimed at reducing the risk of secondary brain injury.
The cerebral autoregulatory mechanism was first proposed by Lassen et al. [6]. They proposed that CA works within a certain range of BP (≈ 60 to 150 mmHg). Outside this, so-called autoregulatory range, vasomotor adjustments are exhausted and cerebral blood flow becomes pressure-passive and subjected to changes in BP. This view on CA is called ‘static autoregulation’. Over the last two decades, techniques with high temporal resolution (e.g. transcranial Doppler ultrasonography (TCD)) have shown that the relationship between BP and cerebral blood flow (CBF) is more dynamic, with short-term reactions. The dynamic relationship between BP and CBF has been shown to function as a high pass filter [7]. High frequency oscillations (> 0.20 Hz) in perfusion pressure are passed along unimpeded, while slower frequency oscillations (< 0.20 Hz) are dampened by the cerebral arterioles.
A large number of methods to assess the quality of CA have been proposed over the last 25 years. Traditional techniques to assess CA use changes in BP to challenge the cerebrovascular system. These BP changes can be induced using pharmacological means or with manoeuvres such as the Valsalva manoeuvre, squat-to-stand and/or sit to stand and the deflation of thigh cuffs [8-10]. However, these interventions are most of the time unsuitable in clinical cases, such as in the severely ill or in older or cognitively impaired persons, because of the relatively large BP change, the requirement of cooperation of patients and the uncomfortable nature of the interventions. Furthermore, other physiological subsystems (e.g. sympathetic activation with Valsalva’s manoeuvre [11] and cortical activation with visual or acoustic stimuli [12]) or
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parameters (e.g. pCO2 with squat- and/or sit to stand manoeuvres [9]) might be affected,
potentially confounding the results. Therefore, different research groups have adopted methods that use spontaneous (e.g. physiological) instead of induced slow BP fluctuations to challenge CA.
Because of the recognition of the potential clinical importance of CA, numerous methods have been developed for non-invasive assessment of CA [13], including for example correlation coefficient analysis, the autoregulatory index [14], transfer function analysis (TFA) [15], nonlinear analyses using Laguerre expansions of Volterra kernels or Principal Dynamic Modes [16], autoregressive [17], as well as multi-modal pressure-flow analysis [18].
With the existence of this multitude of methods, the choice of which method to employ to quantify CA remained a matter of personal choice. No single method has been universally accepted as the gold standard.
Of all available methods, TFA is the most frequently used in the literature to quantify CA using spontaneous fluctuations in BP and CBF [19]. Transfer function analysis is based on analysis of frequency components of oscillations in BP and the resultant degree to which these oscillations are reflected in cerebral blood flow velocity (CBFV) [7]. One of the advantages to this method is that it only takes a baseline measurement without the need for any pharmacological or physiological manipulation of BP. Transfer function analysis starts with dividing the cross spectra between blood pressure and cerebral blood flow velocity by the auto spectra of the blood pressure. With the transfer function the associated relative power (gain), timing (phase) and the linear association (coherence) can be described. Evaluation of CA by TFA is based on the concept that CA minimizes the effect of dynamic BP fluctuations on CBFV, which is reflected by reduced low-frequency gain and phase-lead of CBFV over BP. Without CA, CBFV would passively follow BP and TFA would show constant gain and zero phase across the low-frequency band. To estimate the cross and power spectra several parameter settings need to be chosen, such as sample frequency, window length, overlap percentage and filtering. Despite the frequent use of TFA, however, settings and qualifying terms for the interpretation of outcome metrics, for example criteria for “impaired CA”, have never been strictly defined, leading to limitations of comparison between existing literature, poor standardization and postponement of potential clinical benefits.
The purpose of the present guideline is to establish a first consensus document on CA quantification using TFA with the use of BP and TCD CBFV signals. The guidelines focus on TCD
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100 P reli mi na ry gui d eli ne f or t ra ns fe r f uncti on a na ly sisCBFV signals, as TCD CBFV is currently the most used non-invasive surrogate for CBF. It must be noted that the use of another surrogate, such as oxygenation index obtained with near- infrared spectroscopy [20], may require different TFA settings.
The development of these guidelines was initiated by (but not confined to) The Cerebral Autoregulation Research Network (CARNet - www.car-net.org). This document reflects what is currently considered best practice in awake and cooperating patients with TCD monitoring, although the discussion is ongoing, probably leading to future revisions in the light of new evidence.
It should be noted that the choice for TFA in this document should not be seen as a statement that TFA is considered the best available method to quantify CA.
METHOD
Related to the subject of the present paper, a systematic review of the TFA literature has been performed [21]. That paper (which includes details on search strategy and study inclusion) has identified a large diversity in the signal processing methods, experimental conditions and research protocols that have been used for TFA in previous publications, and the paper provides an overview of this heterogeneity in methods [21]. Following this review, a multi-centre study wherein a single, uniform database with healthy patients with BP and TCD recordings was analysed by different research centres, each using their own TFA settings (filters, frequencies, etc), which was initiated and carried out as part of CARNet. The results of the systematic review and multicenter study were discussed during the Second CARNet International Conference in Nijmegen, September 2012. To derive the guidelines, we then combined the results of the systematic review and the multi-centre study with other available evidence-based scientific literature and with expert opinions obtained from within CARNet. Next, the initial proposal for consensus guidelines was discussed during the 3rd CARNet International conference in Porto, May 2013. During this conference, the arguments in support of the consensus proposal were presented followed by an open discussion with all the participants, and the manuscript was amended accordingly. The CARNet consensus group on TFA listed at the end of the manuscript includes all those who contributed to the consensus process and the preparation of this consensus paper.
The various topics related to the use of TFA for CA are divided into 3 different subjects: experimental procedure, TFA methodology and documentation of TFA results.
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