Determination of Critical ICP Levels in Neurosurgical Patients: A Statistical Approach

Szewczykowski, J; Dytko, P; Kunicki, A; Korsak-Sliwka, J; Sliwka, S; Dziduszko, J; Augustyniak, B

doi:10.1007/978-3-642-66086-3_90

Cited by 15 publications

(7 citation statements)

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“…Several studies in the literature have verified this hypothesis and offer various insights into which form the predictive features of the time series might take. Amplitude of ICP [23], variance of changes [25], and rounding of pulse waveform [16] have been shown to correlate with changes of the mean ICP. Decreases in ABP were observed at the beginning of plateau waves [6], and A waves [18].…”

Section: Introductionmentioning

confidence: 99%

Intracranial hypertension prediction using extremely randomized decision trees

Scalzo

Hamilton

Asgari

et al. 2012

Medical Engineering & Physics

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Intracranial pressure (ICP) elevation (intracranial hypertension, IH) in neurocritical care is typically treated in a reactive fashion; it is only delivered after bedside clinicians notice prolonged ICP elevation. A proactive solution is desirable to improve the treatment of intracranial hypertension. Several studies have shown that the waveform morphology of the intracranial pressure pulse holds predictors about future intracranial hypertension and could therefore be used to alert the bedside clinician of a likely occurrence of the elevation in the immediate future. In this paper, a computational framework is proposed to predict prolonged intracranial hypertension based on morphological waveform features computed from the ICP. A key contribution of this work is to exploit an ensemble classifier method based on Extremely Randomized Decision Trees (Extra-Trees). Experiments on a representative set of 30 patients admitted for various intracranial pressure related conditions demonstrate the effectiveness of the predicting framework on ICP pulses acquired under clinical conditions and the superior results of the proposed approach in comparison to linear and AdaBoost classifiers.

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Section: Introductionmentioning

confidence: 99%

Intracranial hypertension prediction using extremely randomized decision trees

Scalzo

Hamilton

Asgari

et al. 2012

Medical Engineering & Physics

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“…Due to the high sampling rate of the ICP signals, we were able to determine the onset of the elevation with high precision. In previous studies, it is likely that the observed increase in cardiac peak amplitude, C pa , and mean-amplitude correlation, ρ ma , were observed after the beginning of the elevation, but before the mean was actually elevated [2,6,8]. This is illustrated by the example in Figs.…”

Section: Discussionmentioning

confidence: 84%

“…This study included eleven patients with head injuries who had a mean Glasgow Coma Scale (GCS) score of 6 (range [3][4][5][6][7][8][9][10][11][12][13][14] and were admitted to the pediatric intensive care unit at Doernbecher Children's Hospital. The study was reviewed and approved by the Institutional Review Board of Oregon Health and Science University, and the requirement for informed consent was waived.…”

Section: A Patient Population and Managementmentioning

confidence: 99%

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Precursors to Rapid Elevations in Intracranial Pressure

McNames¹,

Crespo²,

Ellenby³

et al. 2001

PsycEXTRA Dataset

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Intracranial pressure (ICP) monitoring and management have substantially improved the outcome of patients with traumatic brain injury (TBI). However, rapid elevations in ICP remain a significant problem as they may lead to secondary brain injury and worse outcome due to cerebral ischemia. Current therapy is targeted towards treating rapid ICP elevations after they occur. Ideally, anticipatory treatment to obviate any elevation in ICP could occur if reliable precursors to ICP elevation were determined. In this paper, we report evidence for a physiologic transition zone prior to rapid elevations in ICP. We found that in thirty-three episodes of ICP elevation recorded from eleven patients, there was a statistically consistent decrease in the cardiac component of the ICP signal and the coefficient of correlation between the ICP trend and the pulse amplitude. We conclude that specific ICP signal metrics may serve as precursors that characterize the transition zone prior to a rapid elevation and may enable prediction of these elevations up to thirty seconds ahead.

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“…Several studies have verified this hypothesis and offer various insights into which form the predictive features might take. Amplitude of ICP [31,32], variance of changes [33][34][35], and rounding of pulse waveform [36] have been shown to correlate with changes of the mean ICP. Decreases in ABP were observed at the beginning of plateau waves [37], and A waves [38].…”

Section: Predicting Intracranial Hypertensionmentioning

confidence: 99%