Low-frequency sampling for PRx calculation does not reduce prognostication and produces similar CPPopt in intracerebral haemorrhage patients

Santos, Edgar; Diedler, Jennifer; Sýkora, Marek; Orakcioglu, Berk; Kentar, Modar; Czosnyka, Marek; Unterberg, Andreas; Sakowitz, Oliver W.

doi:10.1007/s00701-011-1148-5

Cited by 41 publications

(45 citation statements)

References 10 publications

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“…In their first comparative study Santos et al report a PRx-L-PRx correlation of R = 0.84, 28 which is higher than in our study (R = 0.7). It must be noted, however, that their series consists of 18 patients with spontaneous intracerebral hemorrhage, which may represent a less variable clinical entity than our series of 307 patients with TBI.…”

Section: Discussioncontrasting

confidence: 86%

“…28 This larger series, which we present herein, now shows an approximately 5 mm Hg difference between the two indices for CPPopt calculation, which is not statistically significant, and on top of that may represent a number that we consider difficult to target and/or maintain in a clinical ICU setting.…”

Section: Discussionmentioning

confidence: 52%

“…Recently a new PRx, called long (L)-PRx, that considers slower changes in ABP and ICP (within a frequency range of 0.0008-0.008 Hz) has been introduced. 27,28 Its utility and prognostic relevance have been confirmed in a small series of 18 patients with spontaneous intracranial hemorrhage, 28 and in a second series of 29 patients with TBI. 27 The aim of our study was to investigate, by reference to PRx performance, the utility and performance of the L-PRx for long-term outcome assessment and CPPopt calculation in a large TBI series.…”

Section: 29mentioning

confidence: 91%

“…2, 3,14,17,[27][28][29] It is clinically most attractive because it does not require an external stimulus or patient manipulation, and routinely monitored data can be used for clinical research. The PRx has been reported to correlate with outcome after traumatic brain injury (TBI), 14,17 and it is used for online calculation of the optimal cerebral perfusion pressure (CPPopt).…”

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confidence: 99%

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Short pressure reactivity index versus long pressure reactivity index in the management of traumatic brain injury

Lang

Kasprowicz

Smielewski

et al. 2015

JNS

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A rteriAl blood pressure (ABP), intracranial pressure (ICP), cerebral blood flow velocity measured with transcranial Doppler ultrasonography, and cerebral microcirculation assessed by laser Doppler flow are periodical biological signals that can be continuously monitored and recorded in both routine and ICU settings by using invasive and noninvasive techniques and devices. 5,12,16,30,32 These biological signals will show frequency peaks within certain frequency bands.6,11 They can be categorized into two groups: 1) the B waves that occur at approximately 0.5-3 cycles per minute, which corresponds to a frequency of 0.008-0.05 Hz;5,16,23 and 2) M waves, which occur at approximately 4-9 cycles per minute, which corresponds to a frequency of 0.07-0.15 Hz. 7,12,22 The 6 ventiabbreviatioNs ABP = arterial blood pressure; CPPopt = optimal cerebral perfusion pressure; GCS = Glasgow Coma Scale; GOS = Glasgow Outcome Scale; ICP = intracranial pressure; IQR = interquartile range; L-PRx = long pressure reactivity index; PVS = persistent vegetative state; TBI = traumatic brain injury. obJect The pressure reactivity index (PRx) correlates with outcome after traumatic brain injury (TBI) and is used to calculate optimal cerebral perfusion pressure (CPPopt). The PRx is a correlation coefficient between slow, spontaneous changes (0.003-0.05 Hz) in intracranial pressure (ICP) and arterial blood pressure (ABP). A novel index-the so-called long PRx (L-PRx)-that considers ABP and ICP changes (0.0008-0.008 Hz) was proposed. methods The authors compared PRx and L-PRx for 6-month outcome prediction and CPPopt calculation in 307 patients with TBI. The PRx- and L-PRx-based CPPopt were determined and the predictive power and discriminant abilities were compared. results The PRx and L-PRx correlation was good (R = 0.7, p < 0.00001; Spearman test). The PRx, age, CPP, and Glasgow Coma Scale score but not L-PRx were significant fatal outcome predictors (death and persistent vegetative state). There was a significant difference between the areas under the receiver operating characteristic curves calculated for PRx and L-PRx (0.61 ± 0.04 vs 0.51 ± 0.04; z-statistic = -3.26, p = 0.011), which indicates a better ability by PRx than L-PRx to predict fatal outcome. The CPPopt was higher for L-PRx than for PRx, without a statistical difference (median CPPopt for L-PRx: 76.9 mm Hg, interquartile range [IQR] ± 10.1 mm Hg; median CPPopt for PRx: 74.7 mm Hg, IQR ± 8.2 mm Hg). Death was associated with CPP below CPPopt for PRx (c 2 = 30.6, p < 0.00001), and severe disability was associated with CPP above CPPopt for PRx (c 2 = 7.8, p = 0.005). These relationships were not statistically significant for CPPopt for L-PRx. coNclusioNs The PRx is superior to the L-PRx for TBI outcome prediction. Individual CPPopt for L-PRx and PRx are not statistically different. Deviations between CPP and CPPopt for PRx are relevant for outcome prediction; those between CPP and CPPopt for L-PRx are not. The PRx uses the entire B-wave spectrum for index calculation, whereas the ...

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Section: Discussioncontrasting

confidence: 86%

Section: Discussionmentioning

confidence: 52%

Section: 29mentioning

confidence: 91%

mentioning

confidence: 99%

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Short pressure reactivity index versus long pressure reactivity index in the management of traumatic brain injury

Lang

Kasprowicz

Smielewski

et al. 2015

JNS

Self Cite

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“…The window length is chosen to capture the characteristic periodicity of the physiological slow waves present in the blood pressure signal [4]. In practice, a time window of 300 s is usually chosen [4–6], although other window lengths have been suggested [7–10]. Linear regression of the data is then performed and the Pearson correlation coefficient calculated for each window position:where the vectors X and Y are the two signals under investigation, cov(X, Y) is the covariance between X and Y, and σ X , σ y denote standard deviations: replacing X and Y by MAP and rSO 2 defines the COx measure (similarly, using CPP and CBF results in the Mx measure.…”

Section: Introductionmentioning

confidence: 99%

Gradient adjustment method for better discriminating correlating and non-correlating regions of physiological signals: application to the partitioning of impaired and intact zones of cerebral autoregulation

Addison

Antunes

Montgomery

et al. 2016

J Clin Monit Comput

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Cerebral blood flow (CBF) is regulated over a range of systemic blood pressures by the cerebral autoregulation (CA) control mechanism. This range lies within the lower and upper limits of autoregulation (LLA, ULA), beyond which blood pressure drives CBF, and CA function is considered impaired. A standard method to determine autoregulation limits noninvasively using NIRS technology is via the COx measure: a moving correlation index between mean arterial pressure and regional oxygen saturation. In the intact region, there should be no correlation between these variables whereas in the impaired region, the correlation index should approximate unity. In practice, however, the data may be noisy and/or the intact region may often exhibit a slightly positive relationship. This positive relationship may render traditional autoregulation limit calculations difficult to perform, resulting in the need for manual interpretation of the data using arbitrary thresholds. Further, the underlying mathematics of the technique are asymmetric in terms of the results produced for impaired and intact regions and are, in fact, not computable for the ideal case within the intact region. In this work, we propose a novel gradient adjustment method (GACOx) to enhance the differences in COx values observed in the intact and impaired regions. Results from a porcine model (N = 8) are used to demonstrate that GACOx is successful in determining LLA values where traditional methods fail. It is shown that the derived GACOx indices exhibit a mean difference between the intact/impaired regions of 1.54 ± 0.26 (mean ± SD), compared to 0.14 ± 0.10 for the traditional COx method. The GACOx effectively polarizes the COx data in order to better differentiate the intact and impaired zones and, in doing so, makes the determination of the LLA and ULA points a simpler and more consistent task. The method lends itself to the automation of the robust determination of autoregulation zone limits.

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Continuous Optimal CPP Based on Minute-by-Minute Monitoring Data: A Study of a Pediatric Population

Güiza

Meyfroidt

et al. 2016

Acta Neurochirurgica Supplement

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This paper describes the use of minute-by-minute monitoring data to determine continuous optimal cerebral perfusion pressure (CPP) recommendations based on the autoregulatory status of pediatric patients with traumatic brain injury. Data from 79 children were retrospectively studied. Optimal CPP recommendations were obtained for the majority of the first 72 h of monitoring time. Actual CPP close to recommended CPP values was significantly associated with better outcome and was a significant independent predictor of better outcome when considering IMPACT model covariates in multivariate logistic regression.

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Low-frequency sampling for PRx calculation does not reduce prognostication and produces similar CPPopt in intracerebral haemorrhage patients

Abstract: L-PRx correlated with the outcome as good as PRx did. CPPopt could be identified in more patients using L-PRx. Slower MAP and ICP changes (in the range of 1-20 min) can be used for autoregulation assessment and contain important prognostic information.

Cited by 41 publications

References 10 publications

Short pressure reactivity index versus long pressure reactivity index in the management of traumatic brain injury

Short pressure reactivity index versus long pressure reactivity index in the management of traumatic brain injury

Gradient adjustment method for better discriminating correlating and non-correlating regions of physiological signals: application to the partitioning of impaired and intact zones of cerebral autoregulation

Continuous Optimal CPP Based on Minute-by-Minute Monitoring Data: A Study of a Pediatric Population

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