4.5 Article

Intracranial pressure pulse morphological features improved detection of decreased cerebral blood flow

Journal

PHYSIOLOGICAL MEASUREMENT
Volume 31, Issue 5, Pages 679-695

Publisher

IOP PUBLISHING LTD
DOI: 10.1088/0967-3334/31/5/006

Keywords

cerebral blood flow; intracranial pressure; machine learning; cerebral ischemia; morphology; brain injury

Funding

  1. UCLA Brain Injury Research Center (BIRC), NINDS [NS059797, NS054881, NS058489, NS066008]

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We investigated whether intracranial pressure (ICP) pulse morphological metrics could be used to realize continuous detection of low cerebral blood flow. Sixty-three acutely brain injured patients with ICP monitoring, daily (133)Xenon cerebral blood flow (CBF) and daily transcranial Doppler (TCD) assessments were studied. Their ICP recordings were time-aligned with the CBF and TCD measurements so that a 1 h ICP segment near the CBF and TCD measurements was obtained. Each of these recordings was processed by the Morphological Cluster and Analysis of Intracranial Pressure (MOCAIP) algorithm to extract pulse morphological metrics. Then the differential evolution algorithm was used to find the optimal combination of the metrics that provided, using the regularized linear discriminant analysis, the largest combined positive predictivity and sensitivity. At a CBF threshold of 20 ml/min/100 g, a sensitivity of 81.8 +/- 0.9% and a specificity of 50.1 +/- 0.2% were obtained using the optimal combination of conventional TCD and blood analysis metrics as input to a regularized linear classifier. However, using the optimal combination of the MOCAIP metrics alone we were able to achieve a sensitivity of 92.5 +/- 0.7% and a specificity of 84.8 +/- 0.8%. Searching the optimal combination of all available metrics, we achieved the best result that was marginally better than those from using MOCAIP alone. This study demonstrated that the potential role of ICP monitoring may be extended to provide an indicator of low global cerebral blood perfusion.

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