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Eberhard E, Beckerman SR. Rapid-Response Electroencephalography in Seizure Diagnosis and Patient Care: Lessons From a Community Hospital. J Neurosci Nurs 2023; 55:157-163. [PMID: 37556461 DOI: 10.1097/jnn.0000000000000715] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/11/2023]
Abstract
ABSTRACT BACKGROUND: Nonconvulsive seizures are a major source of in-hospital morbidity and a cause of unexplained encephalopathy in critically ill patients. Electroencephalography (EEG) is essential to confirm nonconvulsive seizures and can guide patient-specific workup, treatment, and prognostication. In a 208-bed community hospital, EEG services were limited to 1 part-time EEG technician and 1 EEG machine shared between inpatient and outpatient settings. Its use was restricted to typical business hours. A nursing-led quality improvement (QI) project endeavored to enhance access to EEG by introducing a point-of-care rapid-response EEG program. METHODS: For this project, a multidisciplinary protocol was developed to deploy a Food and Drug Administration-cleared, point-of-care rapid-response EEG platform (Ceribell Inc) in a community hospital's emergency department and inpatient units to streamline neurodiagnostic workups. This QI project compared EEG volume, study location, time-to-EEG, number of cases with seizures captured on EEG, and hospital-level financial metrics of diagnosis-related group reimbursements and length of stay for the 6 months before (pre-QI, using conventional EEG) and 6 months after implementing the rapid-response protocol (post-QI). RESULTS: Electroencephalography volume increased from 35 studies pre-QI to 115 post-QI (3.29-fold increase), whereas the median time from EEG order to EEG start decreased 7.6-fold (74 [34-187] minutes post-QI vs 562 [321-1034] minutes pre-QI). Point-of-care EEG was also associated with more confirmed seizure diagnoses compared with conventional EEG (27/115 post-QI vs 0/35 pre-QI). This resulted in additional diagnosis-related group reimbursements and hospital revenue. Availability of point-of-care EEG was also associated with a shorter median length of stay. CONCLUSION: A nurse-led, rapid-response EEG protocol at a community hospital resulted in significant improvements in EEG accessibility and seizure diagnosis with hospital-level financial benefits. By expanding access to EEG, confirming nonconvulsive seizures, and increasing care efficiency, rapid-response EEG protocols can enhance patient care.
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Affiliation(s)
- Eleanor Eberhard
- Eleanor Eberhard, DNP MBA RN, is VP, CNO, and COO, Dignity Health Sequoia Hospital
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2
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Wired for sound: The effect of sound on the epileptic brain. Seizure 2022; 102:22-31. [PMID: 36179456 DOI: 10.1016/j.seizure.2022.09.016] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2022] [Revised: 09/08/2022] [Accepted: 09/23/2022] [Indexed: 11/22/2022] Open
Abstract
Sound waves are all around us resonating at audible and inaudible frequencies. Our ability to hear is crucial in providing information and enabling interaction with our environment. The human brain generates neural oscillations or brainwaves through synchronised electrical impulses. In epilepsy these brainwaves can change and form rhythmic bursts of abnormal activity outwardly appearing as seizures. When two waveforms meet, they can superimpose onto one another forming constructive, destructive or mixed interference. The effects of audible soundwaves on epileptic brainwaves has been largely explored with music. The Mozart Sonata for Two Pianos in D major, K. 448 has been examined in a number of studies where significant clinical and methodological heterogeneity exists. These studies report variable reductions in seizures and interictal epileptiform discharges. Treatment effects of Mozart Piano Sonata in C Major, K.545 and other composer interventions have been examined with some musical exposures, for example Hayden's Symphony No. 94 appearing pro-epileptic. The underlying anti-epileptic mechanism of Mozart music is currently unknown, but interesting research is moving away from dopamine reward system theories to computational analysis of specific auditory parameters. In the last decade several studies have examined inaudible low intensity focused ultrasound as a neuro-modulatory intervention in focal epilepsy. Whilst acute and chronic epilepsy rodent model studies have consistently demonstrated an anti-epileptic treatment effect this is yet to be reported within large scale human trials. Inaudible infrasound is of concern since at present there are no reported studies on the effects of exposure to infrasound on epilepsy. Understanding the impact of infrasound on epilepsy is critical in an era where sustainable energies are likely to increase exposure.
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Gomez-Quintana S, O'Shea A, Factor A, Popovici E, Temko A. A method for AI assisted human interpretation of neonatal EEG. Sci Rep 2022; 12:10932. [PMID: 35768501 PMCID: PMC9243143 DOI: 10.1038/s41598-022-14894-4] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2022] [Accepted: 06/14/2022] [Indexed: 12/03/2022] Open
Abstract
The study proposes a novel method to empower healthcare professionals to interact and leverage AI decision support in an intuitive manner using auditory senses. The method’s suitability is assessed through acoustic detection of the presence of neonatal seizures in electroencephalography (EEG). Neurophysiologists use EEG recordings to identify seizures visually. However, neurophysiological expertise is expensive and not available 24/7, even in tertiary hospitals. Other neonatal and pediatric medical professionals (nurses, doctors, etc.) can make erroneous interpretations of highly complex EEG signals. While artificial intelligence (AI) has been widely used to provide objective decision support for EEG analysis, AI decisions are not always explainable. This work developed a solution to combine AI algorithms with a human-centric intuitive EEG interpretation method. Specifically, EEG is converted to sound using an AI-driven attention mechanism. The perceptual characteristics of seizure events can be heard using this method, and an hour of EEG can be analysed in five seconds. A survey that has been conducted among targeted end-users on a publicly available dataset has demonstrated that not only does it drastically reduce the burden of reviewing the EEG data, but also the obtained accuracy is on par with experienced neurophysiologists trained to interpret neonatal EEG. It is also shown that the proposed communion of a medical professional and AI outperforms AI alone by empowering the human with little or no experience to leverage AI attention mechanisms to enhance the perceptual characteristics of seizure events.
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Affiliation(s)
| | - Alison O'Shea
- Department of Computer Science, Munster Technological University, Cork, Ireland
| | - Andreea Factor
- Department of Anatomy and Neuroscience, University College Cork, Cork, Ireland
| | - Emanuel Popovici
- Electrical and Electronic Engineering, University College Cork, Cork, Ireland
| | - Andriy Temko
- Electrical and Electronic Engineering, University College Cork, Cork, Ireland
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Madill ES, Gururangan K, Krishnamohan P. Improved access to rapid electroencephalography at a community hospital reduces inter-hospital transfers for suspected non-convulsive seizures. Epileptic Disord 2022; 24:507-516. [PMID: 35770749 DOI: 10.1684/epd.2021.1410] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2021] [Accepted: 12/03/2021] [Indexed: 11/17/2022]
Abstract
OBJECTIVE Patients with suspected non-convulsive seizures are optimally evaluated with EEG. However, limited EEG infrastructure at community hospitals often necessitates transfer for long-term EEG monitoring (LTM). Novel point-of-care EEG systems could expedite management of nonconvulsive seizures and reduce unnecessary transfers. We aimed to describe the impact of rapid access to EEG using a novel EEG device with remote expert interpretation (tele-EEG) on rates of transfer for LTM. METHODS We retrospectively identified a cohort of patients who underwent Rapid-EEG (Ceribell Inc., Mountain View, CA) monitoring as part of a new standard-of-care at a community hospital. Rapid-EEGs were initially reviewed on-site by a community hospital neurologist before transitioning to tele-EEG review by epileptologists at an affiliated academic hospital. We compared the rate of transfer for LTM after Rapid-EEG/tele-EEG implementation to the expected rate if rapid access to EEG was unavailable. RESULTS Seventy-four patients underwent a total of 118 Rapid-EEG studies (10 with seizure, 18 with highly epileptiform patterns, 90 with slow/normal activity). Eighty-one studies (69%), including 9 of 10 studies that detected seizures, occurred after-hours when EEG was previously unavailable. Based on historical practice patterns, we estimated that Rapid-EEG potentially obviated transfer for LTM in 31 of 33 patients (94%); both completed transfers occurred before the transition to tele-EEG review. SIGNIFICANCE Rapid access to EEG led to the detection of seizures that would otherwise have been missed and reduced inter-hospital transfers for LTM. We estimate that the reduction in inter-hospital transportation costs alone would be in excess of $39,000 ($1,274 per patient). Point-of-care EEG systems may support a hub-and-spoke model for managing non-convulsive seizures (similar to that utilized in this study and analogous to existing acute stroke infrastructures), with increased EEG capacity at community hospitals and tele-EEG interpretation by specialists at academic hospitals that can accept transfers for LTM.
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Alkhachroum A, Kromm J, De Georgia MA. Big data and predictive analytics in neurocritical care. Curr Neurol Neurosci Rep 2022; 22:19-32. [PMID: 35080751 DOI: 10.1007/s11910-022-01167-w] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 12/15/2021] [Indexed: 11/26/2022]
Abstract
PURPOSE OF REVIEW To describe predictive data and workflow in the intensive care unit when managing neurologically ill patients. RECENT FINDINGS In the era of Big Data in medicine, intensive critical care units are data-rich environments. Neurocritical care adds another layer of data with advanced multimodal monitoring to prevent secondary brain injury from ischemia, tissue hypoxia, and a cascade of ongoing metabolic events. A step closer toward personalized medicine is the application of multimodal monitoring of cerebral hemodynamics, bran oxygenation, brain metabolism, and electrophysiologic indices, all of which have complex and dynamic interactions. These data are acquired and visualized using different tools and monitors facing multiple challenges toward the goal of the optimal decision support system. In this review, we highlight some of the predictive data used to diagnose, treat, and prognosticate the neurologically ill patients. We describe information management in neurocritical care units including data acquisition, wrangling, analysis, and visualization.
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Affiliation(s)
- Ayham Alkhachroum
- Miller School of Medicine, Neurocritical Care Division, Department of Neurology, University of Miami, Miami, FL, 33146, USA
| | - Julie Kromm
- Cumming School of Medicine, Department of Critical Care Medicine, University of Calgary, Calgary, AB, Canada
- Cumming School of Medicine, Department of Clinical Neurosciences, University of Calgary, Calgary, AB, Canada
- Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada
| | - Michael A De Georgia
- Center for Neurocritical Care, Neurological Institute, University Hospital Cleveland Medical Center, 11100 Euclid Avenue, Cleveland, OH, 44106-5040, USA.
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Kaleem S, Kang JH, Sahgal A, Hernandez CE, Sinha SR, Swisher CB. Electrographic Seizure Detection by Neuroscience Intensive Care Unit Nurses via Bedside Real-Time Quantitative EEG. Neurol Clin Pract 2021; 11:420-428. [PMID: 34840869 DOI: 10.1212/cpj.0000000000001107] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2020] [Accepted: 03/11/2021] [Indexed: 12/20/2022]
Abstract
Objective Our primary objective was to determine the performance of real-time neuroscience intensive care unit (neuro-ICU) nurse interpretation of quantitative EEG (qEEG) at the bedside for seizure detection. Secondary objectives included determining nurse time to seizure detection and assessing factors that influenced nurse accuracy. Methods Nurses caring for neuro-ICU patients undergoing continuous EEG (cEEG) were trained using a 1-hour qEEG panel (rhythmicity spectrogram and amplitude-integrated EEG) bedside display. Nurses' hourly interpretations were compared with post hoc cEEG review by 2 neurophysiologists as the gold standard. Diagnostic performance, time to seizure detection compared with standard of care (SOC), and effects of other factors on nurse accuracy were calculated. Results A total of 109 patients and 65 nurses were studied. Eight patients had seizures during the study period (7%). Nurse sensitivity and specificity for the detection of seizures were 74% and 92%, respectively. Mean nurse time to seizure detection was significantly shorter than SOC by 132 minutes (Cox proportional hazard ratio 6.96). Inaccurate nurse interpretation was associated with increased hours monitored and presence of brief rhythmic discharges. Conclusions This prospective study of real-time nurse interpretation of qEEG for seizure detection in neuro-ICU patients showed clinically adequate sensitivity and specificity. Time to seizure detection was less than that of SOC. Trial Registration Information Clinical trial registration number NCT02082873. Classification of Evidence This study provides Class I evidence that neuro-ICU nurse interpretation of qEEG detects seizures in adults with a sensitivity of 74% and a specificity of 92% compared with traditional cEEG review.
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Affiliation(s)
- Safa Kaleem
- Duke University School of Medicine (SK), Department of Neurology (JHK, AS, CEH, SRS), Duke University, Durham; and Department of Pulmonary Critical Care (CBS), Carolinas Medical Center, Atrium Health, Charlotte
| | - Jennifer H Kang
- Duke University School of Medicine (SK), Department of Neurology (JHK, AS, CEH, SRS), Duke University, Durham; and Department of Pulmonary Critical Care (CBS), Carolinas Medical Center, Atrium Health, Charlotte
| | - Alok Sahgal
- Duke University School of Medicine (SK), Department of Neurology (JHK, AS, CEH, SRS), Duke University, Durham; and Department of Pulmonary Critical Care (CBS), Carolinas Medical Center, Atrium Health, Charlotte
| | - Christian E Hernandez
- Duke University School of Medicine (SK), Department of Neurology (JHK, AS, CEH, SRS), Duke University, Durham; and Department of Pulmonary Critical Care (CBS), Carolinas Medical Center, Atrium Health, Charlotte
| | - Saurabh R Sinha
- Duke University School of Medicine (SK), Department of Neurology (JHK, AS, CEH, SRS), Duke University, Durham; and Department of Pulmonary Critical Care (CBS), Carolinas Medical Center, Atrium Health, Charlotte
| | - Christa B Swisher
- Duke University School of Medicine (SK), Department of Neurology (JHK, AS, CEH, SRS), Duke University, Durham; and Department of Pulmonary Critical Care (CBS), Carolinas Medical Center, Atrium Health, Charlotte
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Tatum WO, Desai N, Feyissa A. Ambulatory EEG: Crossing the divide during a pandemic. Epilepsy Behav Rep 2021; 16:100500. [PMID: 34778740 PMCID: PMC8578031 DOI: 10.1016/j.ebr.2021.100500] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2021] [Revised: 09/02/2021] [Accepted: 09/06/2021] [Indexed: 01/07/2023] Open
Abstract
The COVID-19 pandemic forced temporary closure of epilepsy monitoring units across the globe due to potential hospital-based contagion. As COVID-19 exposures and deaths continues to surge in the United States and around the world, other types of long-term EEG monitoring have risen to fill the gap and minimize hospital exposure. AEEG has high yield compared to standard EEG. Prolonged audio-visual video-EEG capability can record events and epileptiform activity with quality like inpatient video-EEG monitoring. Technological advances in AEEG using miniaturized hardware and wireless secure transmission have evolved to small portable devices that are perfect for people forced to stay at home during the pandemic. Application of seizure detection algorithms and Cloud-based storage with real-time access provides connectivity to AEEG interpreters during prolonged "shut-down". In this article we highlight the benefits of AEEG as an alternative to diagnostic inpatient VEM during the paradigm shift to mobile heath forced by the Coronavirus.
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Affiliation(s)
| | - Nimit Desai
- Department of Neurology, Medical University of South Carolina, Charleston, SC, USA
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8
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Piantino JA, Lin A, Luther M, Centeno LD, Williams CN, Newgard CD. Simultaneous Heart Rate Variability and Electroencephalographic Monitoring in Children in the Emergency Department. JOURNAL OF CHILD & ADOLESCENT TRAUMA 2021; 14:165-175. [PMID: 33986903 PMCID: PMC8099962 DOI: 10.1007/s40653-020-00313-1] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
Changes in heart rate variability (HRV) and electroencephalographic (EEG) background are promising tools for risk stratification and outcome prediction in children seen in the Emergency Department (ED). Novel monitoring technologies offer an opportunity for determining the clinical value of these physiologic variables, however, studies evaluating these measurements obtained in the Pediatric ED are sparse. The current study used a single center, prospective, observational cohort study of HRV and EEG as early predictors of outcome in children with acute trauma. ECG and HRV data were successfully collected in 167 subjects and simultaneous collection of ECG and EEG data using a wireless monitoring device was piloted in 17 patients with 15 patients having EEG data rated as appropriate for clinical interpretation. The mean time from ED arrival to ECG and EEG recording start was 7.5 (SD 11.6) and 34.5 (SD 15.5) minutes, respectively. The mean time required for EEG electrode placement was 9.3 min (SD 5.8 min). Results showed recording early HRV and EEG is feasible in children with acute injury seen in the ED. This study suggests that high consent rates are possible with the adequate research infrastructure and physiologic variables may offer an early, non-invasive marker for injury stratification and prognosis in children.
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Affiliation(s)
- Juan A. Piantino
- Department of Pediatrics, Division of Child Neurology, Doernbecher Children’s Hospital, Oregon Health and Science University, Portland, OR USA
| | - Amber Lin
- Department of Emergency Medicine biostatistician at the Center for Policy and Research in Emergency Medicine, Oregon Health & Science University, Portland, OR USA
| | - Madison Luther
- Department of Pediatrics, Research Assistant at the, Oregon Health & Science University, Portland, OR USA
| | - Luis D. Centeno
- Division of Trauma Surgery, Department of Surgery, Oregon Health & Science University, Portland, OR USA
| | - Cydni N. Williams
- Department of Pediatrics, Division of Pediatric Critical Care, Oregon Health & Science University, Portland, OR USA
| | - Craig D. Newgard
- Department of Emergency Medicine Professor at the Center for Policy and Research in Emergency Medicine, Oregon Health & Science University, Portland, OR USA
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Wright NMK, Madill ES, Isenberg D, Gururangan K, McClellen H, Snell S, Jacobson MP, Gentile NT, Govindarajan P. Evaluating the utility of Rapid Response EEG in emergency care. Emerg Med J 2021; 38:923-926. [PMID: 34039642 DOI: 10.1136/emermed-2020-210903] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2020] [Accepted: 05/11/2021] [Indexed: 11/04/2022]
Abstract
BACKGROUND Timely management of non-convulsive status epilepticus (NCSE) is critical to improving patient outcomes. However, NCSE can only be confirmed using electroencephalography (EEG), which is either significantly delayed or entirely unavailable in emergency departments (EDs). We piloted the use of a new bedside EEG device, Rapid Response EEG (Rapid-EEG, Ceribell), in the ED and evaluated its impact on seizure management when used by emergency physicians. METHODS Patients who underwent Rapid-EEG to rule out NCSE were prospectively enrolled in a pilot project conducted at two ED sites (an academic hospital and a community hospital). Physicians were surveyed on the perceived impact of the device on seizure treatment and patient disposition, and we calculated physicians' sensitivity and specificity (with 95% CI) for diagnosing NCSE using Rapid-EEG's Brain Stethoscope function. RESULTS Of the 38 patients enrolled, the one patient with NCSE was successfully diagnosed and treated within minutes of evaluation. Physicians reported that Rapid-EEG changed clinical management for 20 patients (53%, 95% CI 37% to 68%), primarily by ruling out seizures and avoiding antiseizure treatment escalation, and expedited disposition for 8 patients (21%, 95% CI 11% to 36%). At the community site, physicians diagnosed seizures by their sound using Brain Stethoscope with 100% sensitivity (95% CI 5% to 100%) and 92% specificity (95% CI 62% to 100%). CONCLUSION Rapid-EEG was successfully deployed by emergency physicians at academic and community hospitals, and the device changed management in a majority of cases. Widespread adoption of Rapid-EEG may lead to earlier diagnosis of NCSE, reduced unnecessary treatment and expedited disposition of seizure mimics.
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Affiliation(s)
- Norah M K Wright
- Emergency Medicine, Lewis Katz School of Medicine at Temple University, Philadelphia, Pennsylvania, USA
| | - Evan S Madill
- Neurology, Stanford University School of Medicine, Stanford, California, USA
| | - Derek Isenberg
- Emergency Medicine, Lewis Katz School of Medicine at Temple University, Philadelphia, Pennsylvania, USA
| | - Kapil Gururangan
- Neurology, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Hannah McClellen
- Emergency Services, Stanford Health Care, Stanford, California, USA
| | - Samuel Snell
- Emergency Services, Stanford Health Care, Stanford, California, USA
| | - Mercedes P Jacobson
- Neurology, Lewis Katz School of Medicine at Temple University, Philadelphia, Pennsylvania, USA
| | - Nina T Gentile
- Emergency Medicine, Lewis Katz School of Medicine at Temple University, Philadelphia, Pennsylvania, USA
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Evaluating the Clinical Impact of Rapid Response Electroencephalography: The DECIDE Multicenter Prospective Observational Clinical Study. Crit Care Med 2021; 48:1249-1257. [PMID: 32618687 DOI: 10.1097/ccm.0000000000004428] [Citation(s) in RCA: 39] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
OBJECTIVES To measure the diagnostic accuracy, timeliness, and ease of use of Ceribell rapid response electroencephalography. We assessed physicians' diagnostic assessments and treatment plans before and after rapid response electroencephalography assessment. Primary outcomes were changes in physicians' diagnostic and therapeutic decision making and their confidence in these decisions based on the use of the rapid response electroencephalography system. Secondary outcomes were time to electroencephalography, setup time, ease of use, and quality of electroencephalography data. DESIGN Prospective multicenter nonrandomized observational study. SETTING ICUs in five academic hospitals in the United States. SUBJECTS Patients with encephalopathy suspected of having nonconvulsive seizures and physicians evaluating these patients. INTERVENTIONS Physician bedside assessment of sonified electroencephalography (30 s from each hemisphere) and visual electroencephalography (60 s) using rapid response electroencephalography. MEASUREMENTS AND MAIN RESULTS Physicians (29 fellows or residents, eight attending neurologists) evaluated 181 ICU patients; complete clinical and electroencephalography data were available in 164 patients (average 58.6 ± 18.7 yr old, 45% females). Relying on rapid response electroencephalography information at the bedside improved the sensitivity (95% CI) of physicians' seizure diagnosis from 77.8% (40.0%, 97.2%) to 100% (66.4%, 100%) and the specificity (95% CI) of their diagnosis from 63.9% (55.8%, 71.4%) to 89% (83.0%, 93.5%). Physicians' confidence in their own diagnosis and treatment plan were also improved. Time to electroencephalography (median [interquartile range]) was 5 minutes (4-10 min) with rapid response electroencephalography while the conventional electroencephalography was delayed by several hours (median [interquartile range] delay = 239 minutes [134-471 min] [p < 0.0001 using Wilcoxon signed rank test]). The device was rated as easy to use (mean ± SD: 4.7 ± 0.6 [1 = difficult, 5 = easy]) and was without serious adverse effects. CONCLUSIONS Rapid response electroencephalography enabled timely and more accurate assessment of patients in the critical care setting. The use of rapid response electroencephalography may be clinically beneficial in the assessment of patients with high suspicion for nonconvulsive seizures and status epilepticus.
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McCredie VA. Sonification of Seizures: Music to Our Ears. Crit Care Med 2021; 48:1383-1385. [PMID: 32826490 DOI: 10.1097/ccm.0000000000004483] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
Affiliation(s)
- Victoria A McCredie
- Interdepartmental Division of Critical Care Medicine, University of Toronto; Department of Critical Care Medicine Toronto Western Hospital University Health Network; and Krembil Research Institute, Toronto Western Hospital, Toronto, ON, Canada
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12
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Continuous Electroencephalographic Training for Neuroscience Intensive Care Unit Nurses: A Feasibility Study. J Neurosci Nurs 2021; 52:245-250. [PMID: 32740316 DOI: 10.1097/jnn.0000000000000535] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
Abstract
BACKGROUND Use of continuous electroencephalographic (cEEG) monitoring has more than doubled at our institution for the last 4 years. Although intensive care unit cEEG is reviewed remotely by board-certified epileptologists every 4 to 6 hours, there are inherent delays between occurrence, recognition, and treatment of epileptiform activity. Neuroscience intensive care unit (NSICU) nurses are uniquely positioned to monitor cEEG in real time yet do not receive formal training. The purpose of this study was to evaluate the effectiveness of an education program to teach nurses to monitor cEEG, identify a burst suppression pattern, and measure the duration of suppression. METHODS We performed a retrospective analysis of pretest and posttest data. All NSICU nurses (40) were invited to complete the pretest (PT-0), with 25 participating. Learning style/preference, demographics, comfort with cEEG, and knowledge of EEG fundamentals were assessed. A convenience cohort of NSICU nurses (13) were selected to undergo EEG training. Posttests evaluating EEG fundamental knowledge were completed immediately after training (PT-1), at 3 months (PT-3), and at 6 months (PT-6). The cohort also completed a burst suppression module after the training, which assessed ability to quantify the duration of suppression. RESULTS Mean cohort test scores significantly improved after the training (P < .001). All nurses showed improvement in test scores, and 76.9% passed PT-1 (a score of 80% or higher). Reported mean comfort level with EEG also significantly improved after the training (P = .001). There was no significant difference between mean cohort scores between PT-1, PT-3, and PT-6 (all 88.6%; P = 1.000). Mean cohort score from the bust suppression module was 73%, with test scores ranging from 31% to 93%. CONCLUSIONS NSICU nurses can be taught fundamentals of cEEG, to identify a burst suppression pattern, and to quantify the duration of suppression. Further research is needed to determine whether this knowledge can be translated into clinical competency and affect patient care.
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LaMonte MP. Ceribell EEG shortens seizure diagnosis and workforce time and is useful for COVID isolation. Epilepsia Open 2021; 6:331-338. [PMID: 34033243 PMCID: PMC8013275 DOI: 10.1002/epi4.12474] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2020] [Revised: 01/22/2021] [Accepted: 01/31/2021] [Indexed: 12/14/2022] Open
Abstract
OBJECTIVE To determine whether the portable Ceribell® electroencephalograph (EEG) (Mountain View, CA) used for suspected status epilepticus (SE) can reduce time to diagnosis and on-call workforce demands and whether it can be applied to patients in respiratory isolation. METHODS A multidisciplinary team developed a protocol for the use of the Ceribell EEG. The staff deploying the device, the attending physician, and the interpreting neurologist completed evaluation tools for each patient. Data maintained for quality and resource planning of 18-channel electroencephalography ordered for suspected SE were used as controls. Times to diagnosis were compared by application of Welch-Satterthwaite tests and workforce call-in demands by Fisher's exact t test. We evaluated qualitative data related to the use of the EEG in COVID-19 isolation rooms and on its technical aspects and acceptance by staff members. RESULTS The Ceribell EEG reduced diagnosis time (P = .0000006) and on-call workforce demand (P = .02). The device can be used at any time of day in any hospital care area and has advantages in respiratory isolation rooms. SIGNIFICANCE Compared with a standard 18-channel EEG, the Ceribell device allowed earlier diagnosis of SE and non-SE conditions and reduced workforce demands. Due to the ease of its use and its simple components, which can be readily disinfected, it is advantageous for COVID-19 patients in isolation.
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Affiliation(s)
- Marian P LaMonte
- Ascension St. Agnes Hospital, University of Maryland School of Medicine, Baltimore, MD, USA
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Kromm J, Fiest KM, Alkhachroum A, Josephson C, Kramer A, Jette N. Structure and Outcomes of Educational Programs for Training Non-electroencephalographers in Performing and Screening Adult EEG: A Systematic Review. Neurocrit Care 2021; 35:894-912. [PMID: 33591537 DOI: 10.1007/s12028-020-01172-2] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2020] [Accepted: 12/01/2020] [Indexed: 12/21/2022]
Abstract
OBJECTIVE To qualitatively and quantitatively summarize curricula, teaching methods, and effectiveness of educational programs for training bedside care providers (non-experts) in the performance and screening of adult electroencephalography (EEG) for nonconvulsive seizures and other patterns. METHODS PRISMA methodological standards were followed. MEDLINE, EMBASE, Cochrane, CINAHL, WOS, Scopus, and MedEdPORTAL databases were searched from inception until February 26, 2020 with no restrictions. Abstract and full-text review was completed in duplicate. Studies were included if they were original research; involved non-experts performing, troubleshooting, or screening adult EEG; and provided qualitative descriptions of curricula and teaching methods and/or quantitative assessment of non-experts (vs gold standard EEG performance by neurodiagnostic technologists or interpretation by neurophysiologists). Data were extracted in duplicate. A content analysis and a meta-narrative review were performed. RESULTS Of 2430 abstracts, 35 studies were included. Sensitivity and specificity of seizure identification varied from 38 to 100% and 65 to 100% for raw EEG; 40 to 93% and 38 to 95% for quantitative EEG, and 95 to 100% and 65 to 85% for sonified EEG, respectively. Non-expert performance of EEG resulted in statistically significant reduced delay (86 min, p < 0.0001; 196 min, p < 0.0001; 667 min, p < 0.005) in EEG completion and changes in management in approximately 40% of patients. Non-experts who were trained included physicians, nurses, neurodiagnostic technicians, and medical students. Numerous teaching methods were utilized and often combined, with instructional and hands-on training being most common. CONCLUSIONS Several different bedside providers can be educated to perform and screen adult EEG, particularly for the purpose of diagnosing nonconvulsive seizures. While further rigorous research is warranted, this review demonstrates several potential bridges by which EEG may be integrated into the care of critically ill patients.
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Affiliation(s)
- Julie Kromm
- Department of Critical Care Medicine, Cumming School of Medicine, University of Calgary, Room 04112, Foothills Medical Centre, McCaig Tower, 3134 Hospital Drive NW, Calgary, Alberta, T2N 5A1, Canada. .,Department of Clinical Neurosciences, Cumming School of Medicine, University of Calgary, Calgary, Canada. .,Hotchkiss Brain Institute, Cumming School of Medicine, University of Calgary, Calgary, Canada.
| | - Kirsten M Fiest
- Department of Critical Care Medicine, Cumming School of Medicine, University of Calgary, Room 04112, Foothills Medical Centre, McCaig Tower, 3134 Hospital Drive NW, Calgary, Alberta, T2N 5A1, Canada.,Department of Community Health Sciences, Cumming School of Medicine, University of Calgary, Calgary, Canada.,Department of Psychiatry, Cumming School of Medicine, University of Calgary, Calgary, Canada
| | - Ayham Alkhachroum
- Neurocritical Care Division, Miller School of Medicine, University of Miami, Miami, USA
| | - Colin Josephson
- Department of Clinical Neurosciences, Cumming School of Medicine, University of Calgary, Calgary, Canada.,Department of Community Health Sciences, Cumming School of Medicine, University of Calgary, Calgary, Canada.,Hotchkiss Brain Institute, Cumming School of Medicine, University of Calgary, Calgary, Canada
| | - Andreas Kramer
- Department of Critical Care Medicine, Cumming School of Medicine, University of Calgary, Room 04112, Foothills Medical Centre, McCaig Tower, 3134 Hospital Drive NW, Calgary, Alberta, T2N 5A1, Canada.,Department of Clinical Neurosciences, Cumming School of Medicine, University of Calgary, Calgary, Canada.,Hotchkiss Brain Institute, Cumming School of Medicine, University of Calgary, Calgary, Canada
| | - Nathalie Jette
- Department of Neurology, Icahn School of Medicine, Mount Sinai, New York, USA
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Kamousi B, Karunakaran S, Gururangan K, Markert M, Decker B, Khankhanian P, Mainardi L, Quinn J, Woo R, Parvizi J. Monitoring the Burden of Seizures and Highly Epileptiform Patterns in Critical Care with a Novel Machine Learning Method. Neurocrit Care 2020; 34:908-917. [PMID: 33025543 PMCID: PMC8021593 DOI: 10.1007/s12028-020-01120-0] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2020] [Accepted: 09/17/2020] [Indexed: 12/18/2022]
Abstract
INTRODUCTION Current electroencephalography (EEG) practice relies on interpretation by expert neurologists, which introduces diagnostic and therapeutic delays that can impact patients' clinical outcomes. As EEG practice expands, these experts are becoming increasingly limited resources. A highly sensitive and specific automated seizure detection system would streamline practice and expedite appropriate management for patients with possible nonconvulsive seizures. We aimed to test the performance of a recently FDA-cleared machine learning method (Claritγ, Ceribell Inc.) that measures the burden of seizure activity in real time and generates bedside alerts for possible status epilepticus (SE). METHODS We retrospectively identified adult patients (n = 353) who underwent evaluation of possible seizures with Rapid Response EEG system (Rapid-EEG, Ceribell Inc.). Automated detection of seizure activity and seizure burden throughout a recording (calculated as the percentage of ten-second epochs with seizure activity in any 5-min EEG segment) was performed with Claritγ, and various thresholds of seizure burden were tested (≥ 10% indicating ≥ 30 s of seizure activity in the last 5 min, ≥ 50% indicating ≥ 2.5 min of seizure activity, and ≥ 90% indicating ≥ 4.5 min of seizure activity and triggering a SE alert). The sensitivity and specificity of Claritγ's real-time seizure burden measurements and SE alerts were compared to the majority consensus of at least two expert neurologists. RESULTS Majority consensus of neurologists labeled the 353 EEGs as normal or slow activity (n = 249), highly epileptiform patterns (HEP, n = 87), or seizures [n = 17, nine longer than 5 min (e.g., SE), and eight shorter than 5 min]. The algorithm generated a SE alert (≥ 90% seizure burden) with 100% sensitivity and 93% specificity. The sensitivity and specificity of various thresholds for seizure burden during EEG recordings for detecting patients with seizures were 100% and 82% for ≥ 50% seizure burden and 88% and 60% for ≥ 10% seizure burden. Of the 179 EEG recordings in which the algorithm detected no seizures, seizures were identified by the expert reviewers in only two cases, indicating a negative predictive value of 99%. DISCUSSION Claritγ detected SE events with high sensitivity and specificity, and it demonstrated a high negative predictive value for distinguishing nonepileptiform activity from seizure and highly epileptiform activity. CONCLUSIONS Ruling out seizures accurately in a large proportion of cases can help prevent unnecessary or aggressive over-treatment in critical care settings, where empiric treatment with antiseizure medications is currently prevalent. Claritγ's high sensitivity for SE and high negative predictive value for cases without epileptiform activity make it a useful tool for triaging treatment and the need for urgent neurological consultation.
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Affiliation(s)
- Baharan Kamousi
- Ceribell Inc., 2483 Old Middlefield Way, Suite 120, Mountain View, CA, USA
| | | | - Kapil Gururangan
- Department of Neurology, The Mount Sinai Hospital, New York, NY, USA
| | - Matthew Markert
- Department of Neurology and Neurological Sciences, Stanford University Medical Center, 300 Pasteur Drive, Stanford, CA, 94305, USA
| | - Barbara Decker
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Pouya Khankhanian
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Laura Mainardi
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - James Quinn
- Department of Emergency Medicine, Stanford University Medical Center, Stanford, CA, USA
| | - Raymond Woo
- Ceribell Inc., 2483 Old Middlefield Way, Suite 120, Mountain View, CA, USA
| | - Josef Parvizi
- Department of Neurology and Neurological Sciences, Stanford University Medical Center, 300 Pasteur Drive, Stanford, CA, 94305, USA.
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16
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Rapid Response Electroencephalography for Urgent Evaluation of Patients in Community Hospital Intensive Care Practice. J Neurosci Nurs 2020; 51:308-312. [PMID: 31688282 DOI: 10.1097/jnn.0000000000000476] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
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.
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Hobbs K, Krishnamohan P, Legault C, Goodman S, Parvizi J, Gururangan K, Mlynash M. Rapid Bedside Evaluation of Seizures in the ICU by Listening to the Sound of Brainwaves: A Prospective Observational Clinical Trial of Ceribell's Brain Stethoscope Function. Neurocrit Care 2019; 29:302-312. [PMID: 29923167 DOI: 10.1007/s12028-018-0543-7] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/28/2023]
Abstract
BACKGROUND Patients suffering from non-convulsive seizures experience delays in diagnosis and treatment due to limitations in acquiring and interpreting electroencephalography (EEG) data. The Ceribell EEG System offers rapid EEG acquisition and conversion of EEG signals to sound (sonification) using a proprietary algorithm. This study was designed to test the performance of this EEG system in an intensive care unit (ICU) setting and measure its impact on clinician treatment decision. METHODS Encephalopathic ICU patients at Stanford University Hospital were enrolled if clinical suspicion for seizures warranted EEG monitoring. Treating physicians rated suspicion for seizure and decided if the patient needed antiepileptic drug (AED) treatment at the time of bedside evaluation. After listening to 30 s of EEG from each hemisphere in each patient, they reevaluated their suspicion for seizure and decision for additional treatment. The EEG waveforms recorded with Ceribell EEG were subsequently analyzed by three blinded epileptologists to assess the presence or absence of seizures within and outside the sonification window. Study outcomes were EEG set up time, ease of use of the device, change in clinician seizure suspicion, and change in decision to treat with AED before and after sonification. RESULTS Thirty-five cases of EEG sonification were performed. Mean EEG setup time was 6 ± 3 min, and time to obtain sonified EEG was significantly faster than conventional EEG (p < 0.001). One patient had non-convulsive seizure during sonification and another had rhythmic activity that was followed by seizure shortly after sonification. Change in treatment decision after sonification occurred in approximately 40% of patients and resulted in a significant net reduction in unnecessary additional treatments (p = 0.01). Ceribell EEG System was consistently rated easy to use. CONCLUSION The Ceribell EEG System enabled rapid acquisition of EEG in patients at risk for non-convulsive seizures and aided clinicians in their evaluation of encephalopathic ICU patients. The ease of use and speed of EEG acquisition and interpretation by EEG-untrained individuals has the potential to improve emergent clinical decision making by quickly detecting non-convulsive seizures in the ICU.
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Affiliation(s)
- Kyle Hobbs
- Department of Neurology and Neurological Sciences, Stanford University School of Medicine, Stanford, CA, USA. .,Wake Forest University School of Medicine, 1 Medical Center Boulevard, Winston-Salem, NC, 27157, USA.
| | - Prashanth Krishnamohan
- Department of Neurology and Neurological Sciences, Stanford University School of Medicine, Stanford, CA, USA
| | - Catherine Legault
- Department of Neurology and Neurological Sciences, Stanford University School of Medicine, Stanford, CA, USA
| | - Steve Goodman
- Department of Health Research and Policy, Stanford University School of Medicine, Stanford, CA, USA
| | - Josef Parvizi
- Department of Neurology and Neurological Sciences, Stanford University School of Medicine, Stanford, CA, USA
| | - Kapil Gururangan
- Department of Neurology and Neurological Sciences, Stanford University School of Medicine, Stanford, CA, USA
| | - Michael Mlynash
- Department of Neurology and Neurological Sciences, Stanford University School of Medicine, Stanford, CA, USA
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Amengual-Gual M, Ulate-Campos A, Loddenkemper T. Status epilepticus prevention, ambulatory monitoring, early seizure detection and prediction in at-risk patients. Seizure 2019; 68:31-37. [DOI: 10.1016/j.seizure.2018.09.013] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2018] [Revised: 08/16/2018] [Accepted: 09/15/2018] [Indexed: 02/08/2023] Open
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