• Risa B Myers, Christos Lazaridis, Christopher M Jermaine, Claudia S Robertson, and Craig G Rusin.
• 1Department of Computer Science, Rice University, Houston, TX.2Division of Neurocritical Care, Department of Neurology, Baylor College of Medicine, Houston, TX.3Department of Neurosurgery, Baylor College of Medicine, Houston, TX.4Department of Pediatrics, Section of Cardiology, Baylor College of Medicine, Houston, TX.
• Crit. Care Med. 2016 Sep 1; 44 (9): 1754-61.

ObjectivesTo develop computer algorithms that can recognize physiologic patterns in traumatic brain injury patients that occur in advance of intracranial pressure and partial brain tissue oxygenation crises. The automated early detection of crisis precursors can provide clinicians with time to intervene in order to prevent or mitigate secondary brain injury.DesignA retrospective study was conducted from prospectively collected physiologic data. intracranial pressure, and partial brain tissue oxygenation crisis events were defined as intracranial pressure of greater than or equal to 20 mm Hg lasting at least 15 minutes and partial brain tissue oxygenation value of less than 10 mm Hg for at least 10 minutes, respectively. The physiologic data preceding each crisis event were used to identify precursors associated with crisis onset. Multivariate classification models were applied to recorded data in 30-minute epochs of time to predict crises between 15 and 360 minutes in the future.SettingThe neurosurgical unit of Ben Taub Hospital (Houston, TX).SubjectsOur cohort consisted of 817 subjects with severe traumatic brain injury.Measurements And Main ResultsOur algorithm can predict the onset of intracranial pressure crises with 30-minute advance warning with an area under the receiver operating characteristic curve of 0.86 using only intracranial pressure measurements and time since last crisis. An analogous algorithm can predict the start of partial brain tissue oxygenation crises with 30-minute advanced warning with an area under the receiver operating characteristic curve of 0.91.ConclusionsOur algorithms provide accurate and timely predictions of intracranial hypertension and tissue hypoxia crises in patients with severe traumatic brain injury. Almost all of the information needed to predict the onset of these events is contained within the signal of interest and the time since last crisis.

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