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ARMONK, N.Y. and LOS ANGELES -- IBM and Excel Medical Electronics (EME) are collaborating with the UCLA Department of Neurosurgery in a study to test the effectiveness of a real-time alarm intended to predict rising brain pressure in patients with traumatic brain injuries. The experimental system uses big data analytics software developed by IBM Research and EME that analyzes in real-time streams of vital signs continuously collected from the bedside monitor to spot subtle changes in the patient's pulse, blood and intracranial pressure, heart activity, and respiration, signaling that dangerous high-risk increases in brain pressure are on the way.
Today, patients with traumatic brain injuries are under constant surveillance by bedside monitors measuring the patient's vital signs, but nurses are only alerted by the bedside monitor alarm when brain pressure crosses a critical threshold. At that point, an instant decision must be made by the nurse or physician to determine if the alarm is false, if the condition is life-threatening, or if immediate action is needed to prevent brain damage or death.
IBM, Excel Medical and UCLA tackle big data in efforts to uncover preventative treatments for patients with traumatic brain injuries (TBIs). Dr. Xiao Hu, associate professor at the UCLA Department of Neurosurgery, analyzes brain wave data to predict the rise of deadly brain pressure as part of a National Institute of Neurological Disorders and Stroke study.
UCLA's study aims to address these questions. UCLA neurointensives will use real-time analysis from thousands of vitals collected and flowing from patients' bedside monitor inside the intensive care unit at Ronald Reagan UCLA Medical Center. The technology's goal is to provide advance warning to physicians and nurses of pending changes in the patient's condition, allowing them to take preventive action to keep patients safe from rising brain pressure.
The Centers for Disease Control estimates 1.7 million people in the United States sustain a traumatic brain injury every year. Of those individuals, about 52,000 die, 275,000 are hospitalized, and 1.365 million are treated and released from an emergency department. Research has been underway for several years to monitor and predict critical changes in these patients, but until now it has been difficult to analyze all of the patient data that flows in real-time. UCLA study's will be testing whether recent advances in streaming analytics software can now make this possible in the critical care setting.