Moussa Yazbeck1, Parveen Sra, Josef Parvizi. 1. Moussa Yazbeck, MD, is NeuroICU Director, John Muir Health, Walnut Creek, CA. Parveen Sra, MPH, is Research Coordinator, John Muir Health, Walnut Creek, CA.
Abstract
INTRODUCTION: Limited access to specialized technicians and trained neurologists results in delayed access to electroencephalography (EEG) and an accurate diagnosis of patients with critical neurological problems. This study evaluated the performance of Ceribell Rapid Response EEG System (RR-EEG), which promises fast EEG acquisition and interpretation without traditional technicians or EEG-trained specialists. METHODS: The new technology was tested in a community hospital intensive care unit in Northern California. Three physicians (without previous training in EEG) were trained by the manufacturer of the RR-EEG and acquired EEG without the help of any EEG technicians. Time needed from order to EEG acquisition was noted. Quality of EEG and diagnostic information obtained with the new EEG technology were evaluated and compared with the same information from conventional clinical EEG system. RESULTS: Ten patients were tested with this new EEG technology, and 6 of these patients went on to have conventional EEGs when the EEG technicians arrived at the site. In these cases, the conventional EEG was significantly delayed (11.2 ± 3.6 hours) compared with RR-EEG (5.0 ± 2.4 minutes; P < .005). Use of RR-EEG helped clinicians rule out status epilepticus and prevent overtreatment in 4 of 10 cases. RR-EEG and conventional EEG systems yielded similar diagnostic information. CONCLUSION: RR-EEG can be set up by nurses, and diagnostic information about the presence or absence of seizures can be appreciated by nurses. The RR-EEG system, compared with the conventional EEG, did not require EEG technologists and enabled significantly faster access to diagnostic EEG information. This report confirms the ease of use and speed of acquisition and interpretation of EEG information at a community hospital setting using an RR-EEG device. This new technology has the potential to improve emergent clinical decision making and prevent overtreatment of patients in the intensive care unit setting while empowering nursing staff with useful diagnostic information in real time and at the bedside.
INTRODUCTION: Limited access to specialized technicians and trained neurologists results in delayed access to electroencephalography (EEG) and an accurate diagnosis of patients with critical neurological problems. This study evaluated the performance of Ceribell Rapid Response EEG System (RR-EEG), which promises fast EEG acquisition and interpretation without traditional technicians or EEG-trained specialists. METHODS: The new technology was tested in a community hospital intensive care unit in Northern California. Three physicians (without previous training in EEG) were trained by the manufacturer of the RR-EEG and acquired EEG without the help of any EEG technicians. Time needed from order to EEG acquisition was noted. Quality of EEG and diagnostic information obtained with the new EEG technology were evaluated and compared with the same information from conventional clinical EEG system. RESULTS: Ten patients were tested with this new EEG technology, and 6 of these patients went on to have conventional EEGs when the EEG technicians arrived at the site. In these cases, the conventional EEG was significantly delayed (11.2 ± 3.6 hours) compared with RR-EEG (5.0 ± 2.4 minutes; P < .005). Use of RR-EEG helped clinicians rule out status epilepticus and prevent overtreatment in 4 of 10 cases. RR-EEG and conventional EEG systems yielded similar diagnostic information. CONCLUSION: RR-EEG can be set up by nurses, and diagnostic information about the presence or absence of seizures can be appreciated by nurses. The RR-EEG system, compared with the conventional EEG, did not require EEG technologists and enabled significantly faster access to diagnostic EEG information. This report confirms the ease of use and speed of acquisition and interpretation of EEG information at a community hospital setting using an RR-EEG device. This new technology has the potential to improve emergent clinical decision making and prevent overtreatment of patients in the intensive care unit setting while empowering nursing staff with useful diagnostic information in real time and at the bedside.
Authors: Paul M Vespa; DaiWai M Olson; Sayona John; Kyle S Hobbs; Kapil Gururangan; Kun Nie; Masoom J Desai; Matthew Markert; Josef Parvizi; Thomas P Bleck; Lawrence J Hirsch; M Brandon Westover Journal: Crit Care Med Date: 2020-09 Impact factor: 9.296
Authors: Aristea S Galanopoulou; Victor Ferastraoaru; Daniel J Correa; Koshi Cherian; Susan Duberstein; Jonathan Gursky; Rajani Hanumanthu; Christine Hung; Isaac Molinero; Olga Khodakivska; Alan D Legatt; Puja Patel; Jillian Rosengard; Elayna Rubens; William Sugrue; Elissa Yozawitz; Mark F Mehler; Karen Ballaban-Gil; Sheryl R Haut; Solomon L Moshé; Alexis Boro Journal: Epilepsia Open Date: 2020-05-17
Authors: Afshin A Divani; Sasan Andalib; José Biller; Mario Di Napoli; Narges Moghimi; Clio A Rubinos; Christa O'Hana Nobleza; P N Sylaja; Michel Toledano; Simona Lattanzi; Louise D McCullough; Salvador Cruz-Flores; Michel Torbey; M Reza Azarpazhooh Journal: Curr Neurol Neurosci Rep Date: 2020-10-30 Impact factor: 5.081
Authors: Baharan Kamousi; Suganya Karunakaran; Kapil Gururangan; Matthew Markert; Barbara Decker; Pouya Khankhanian; Laura Mainardi; James Quinn; Raymond Woo; Josef Parvizi Journal: Neurocrit Care Date: 2020-10-06 Impact factor: 3.210