Magazine article Clinical Psychiatry News

Algorithm May Predict Intracranial Pressure Swings

Magazine article Clinical Psychiatry News

Algorithm May Predict Intracranial Pressure Swings

Article excerpt


LAKE BUENA VISTA, FLA. -- An algorithm might predict whether patients with severe traumatic brain injury are recovering well or need interventions to preempt evolving intracranial hypertension, according to Dr. Brandon Bonds.

"Valid predictive algorithms have the potential to revolutionize the care of patients with traumatic brain injury [TBI] and transform physiologic data from just a pure numeric value buried in a never-ending nursing flow sheet into a useful triage and decision-assist tool," study author Dr. Bonds said at the annual scientific assembly of the Eastern Association for the Surgery of Trauma.

A minimum of 10 hours of continuous data on vital signs (intracranial pressure, heart rate, systolic blood pressure, shock index, and mean arterial pressure) were used to predict intracranial pressure (ICP) values for a retrospective cohort of 132 adults with severe TBI, 97% of which was the result of blunt trauma.

Even relatively brief episodes of elevated ICP have been shown to be associated with poor outcomes in TBI patients, while marked elevation of ICP may lead to herniation and death, said Dr. Bonds of the R. Adams Cowley Shock Trauma Center, University of Maryland, Baltimore.

At the trauma center, vital signs are automatically collected every 6 seconds, 24 hours a day, on all TBI patients. This granularity of data was used to map patterns in the patients' physiology. The approach used a nearest neighbor regression (NNR) method: A model was constructed that predicts future numerical values for an individual based on comparisons to data from historical subjects.

The same mathematical principal is used by a variety of industries to predict likely responses. NetFlix, for example, uses a system similar to the NNR method to predict future television and movie picks based on prior selections, Dr. Bonds explained.

About 20 minutes of continuously collected, automated vital sign data were then used to test the algorithm on a per-patient basis. …

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