Assessing Blood Flow Control Through a Bootstrap Method

Simpson, David M.; Panerai, Ronney B.; Ramos, Emilio; Lopes, José M. A.; Marinatto, M.N.V.; Nadal, Jurandir; Evans, D.H.

doi:10.1109/tbme.2004.827947

Cited by 21 publications

(36 citation statements)

References 11 publications

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“…3. Despite concerns about its variability and reliability [104,110,133], the ARI has been widely used in many clinical studies as discussed later in this review.…”

Section: Dynamic Cerebral Autoregulation Modellingmentioning

confidence: 99%

“…The squared coherence will approach 1.0 when the output is entirely and linearly dependent on the input. On the other hand, the coherence will tend to zero if (4) PAF (4) OH-DbHD (2) HUT 45°Static CBFV passive with MAP but preserved with 133 (8) HUT 50°Static TCD can reflect hypovolemia Levine et al [75] 1994 Healthy (13) LBNP to -55 mmHg Static CA not cause of syncope…”

Section: Dynamic Cerebral Autoregulation Modellingmentioning

confidence: 99%

“…Before the introduction of transcranial Doppler ultrasound (TCD), measurement of CBF in humans depended on indicator-dilution techniques, such as 133 Xenon or nitrous oxide which required several minutes to yield a single CBF value. As a consequence, the classical CA curve, showing a plateau in the case of normal autoregulation, represents a 'static' depiction of CA [144], with each data point consisting of mean values of CBF and MAP averaged over several minutes.…”

Section: Introductionmentioning

confidence: 99%

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Transcranial Doppler for evaluation of cerebral autoregulation

2009

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Transcranial Doppler ultrasound (TCD) can measure cerebral blood flow velocity in the main intracranial vessels non-invasively and with high accuracy. Combined with the availability of non-invasive devices for continuous measurement of arterial blood pressure, the relatively low cost, ease-of-use, and excellent temporal resolution of TCD have stimulated the development of new techniques to assess cerebral autoregulation in the laboratory or bedside using a dynamic approach, instead of the more classical 'static' method. Clinical applications have shown consistent results in certain conditions such as severe head injury and carotid artery disease. Studies in syncopal patients revealed a more complex pattern due to aetiological non-homogeneity and methodological limitations mainly due to inadequate sample-size. Different analytical models to quantify autoregulatory performance have also contributed to the diversity of results in the literature. The review concludes with specific recommendations for areas where further validation and research are needed to improve the reliability and usefulness of TCD in clinical practice.

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“…3. Despite concerns about its variability and reliability [104,110,133], the ARI has been widely used in many clinical studies as discussed later in this review.…”

Section: Dynamic Cerebral Autoregulation Modellingmentioning

confidence: 99%

Section: Dynamic Cerebral Autoregulation Modellingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Transcranial Doppler for evaluation of cerebral autoregulation

2009

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“…Our results, however, are in line with previous studies of cerebral autoregulation based on transfer function analysis between Doppler measurements of cerebral blood flow velocity and spontaneous variations in ABP. 14,15 It has been proposed to set a minimum variability in ABP for a more precise estimation of cerebral autoregulation from spontaneous fluctuations in ABP. 5,14 However, this approach will result in data loss, because in practice, the variability in ABP can be quite low over a considerable length of time.…”

Section: Adjustment For Variability In Blood Pressurementioning

confidence: 99%

Precision of coherence analysis to detect cerebral autoregulation by near-infrared spectroscopy in preterm infants

2010

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Abstract. Coherence between spontaneous fluctuations in arterial blood pressure ͑ABP͒ and the cerebral near-infrared spectroscopy signal can detect cerebral autoregulation. Because reliable measurement depends on signals with high signal-to-noise ratio, we hypothesized that coherence is more precisely determined when fluctuations in ABP are large rather than small. Therefore, we investigated whether adjusting for variability in ABP ͑variability ABP ͒ improves precision. We examined the impact of variability ABP within the power spectrum in each measurement and between repeated measurements in preterm infants. We also examined total monitoring time required to discriminate among infants with a simulation study. We studied 22 preterm infants ͑GAϽ 30͒ yielding 215 10-min measurements. Surprisingly, adjusting for variability ABP within the power spectrum did not improve the precision. However, adjusting for the variability ABP among repeated measurements ͑i.e., weighting measurements with high variability ABP in favor of those with low͒ improved the precision. The evidence of drift in individual infants was weak. Minimum monitoring time needed to discriminate among infants was 1.3-3.7 h. Coherence analysis in low frequencies ͑0.04-0.1 Hz͒ had higher precision and statistically more power than in very low frequencies ͑0.003-0.04 Hz͒. In conclusion, a reliable detection of cerebral autoregulation takes hours and the precision is improved by adjusting for variability ABP between repeated measurements.

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“…An alternative method is provided by the autoregulatory index ARI [15]: this varies from 0 (absent CA) to 9 (excellent CA), with each corresponding to a specific linear filter. The filter that best fits the data determines the ARI for the recording, and this can be applied to spontaneous as well as induced blood pressure variations [16,17] . The correlation of the ABP and CBFV time series [18] known as the Mx index, has also been very extensively used (e.g.…”

Section: Introductionmentioning

confidence: 99%

Optimising the assessment of cerebral autoregulation from black box models

Angarita-Jaimes

Kouchakpour

Liu

et al. 2014

Medical Engineering & Physics

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Cerebral autoregulation (CA) mechanisms maintain blood flow approximately stable despite changes in arterial blood pressure. Mathematical models that characterise this system have been used extensively in the quantitative assessment of function/impairment of CA. Using spontaneous fluctuations in arterial blood pressure (ABP) as input and cerebral blood flow velocity (CBFV) as output, the autoregulatory mechanism can be modelled using linear and nonlinear approaches, from which indexes can be extracted to provide an overall assessment of CA. Previous studies have considered a single -or at most a couple of measures, making it difficult to compare the performance of different CA parameters. We compare the performance of established autoregulatory parameters and propose novel measures. The key objective is to identify which model and index can best distinguish between normal and impaired CA. To this end twenty-six recordings of ABP and CBFV from normocapnia and hypercapnia (which temporarily impairs CA) in 13 healthy adults were analysed. In the absence of a 'gold' standard for the study of dynamic CA, lower inter-and intra-subject variability of the parameters in relation to the difference between normo-and hypercapnia were considered as criteria for identifying improved measures of CA. Significantly improved performance compared to some conventional approaches was achieved, with the simplest method emerging as probably the most promising for future studies.

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Assessing Blood Flow Control Through a Bootstrap Method

Cited by 21 publications

References 11 publications

Transcranial Doppler for evaluation of cerebral autoregulation

Transcranial Doppler for evaluation of cerebral autoregulation

Precision of coherence analysis to detect cerebral autoregulation by near-infrared spectroscopy in preterm infants

Optimising the assessment of cerebral autoregulation from black box models

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