Alexis A Topjian1, Bingqing Zhang2, Rui Xiao3, France W Fung4, Robert A Berg5, Kathryn Graham5, Nicholas S Abend4. 1. Department of Anesthesia and Critical Care Medicine, Children's Hospital of Philadelphia and University of Pennsylvania Perelman School of Medicine, United States. Electronic address: topjian@email.chop.edu. 2. Department of Anesthesia and Critical Care Medicine, Children's Hospital of Philadelphia and University of Pennsylvania Perelman School of Medicine, United States; Data Science and Biostatistics Unit, Department of Biomedical and Health Informatics, Children's Hospital of Philadelphia, United States. 3. Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine at the University of Pennsylvania, United States. 4. Division of Neurology, Children's Hospital of Philadelphia, United States; Departments of Neurology and Pediatrics, University of Pennsylvania Perelman School of Medicine, United States. 5. Department of Anesthesia and Critical Care Medicine, Children's Hospital of Philadelphia and University of Pennsylvania Perelman School of Medicine, United States.
Abstract
AIMS: Assessment of brain injury severity early after cardiac arrest (CA) may guide therapeutic interventions and help clinicians counsel families regarding neurologic prognosis. We aimed to determine whether adding EEG features to predictive models including clinical variables and examination signs increased the accuracy of short-term neurobehavioral outcome prediction. METHODS: This was a prospective, observational, single-center study of consecutive infants and children resuscitated from CA. Standardized EEG scoring was performed by an electroencephalographer for the initial EEG timepoint after return of spontaneous circulation (ROSC) and each 12-h segment from the time of ROSC up to 48 h. EEG Background Category was scored as: (1) normal; (2) slow-disorganized; (3) discontinuous or burst-suppression; or (4) attenuated-featureless. The primary outcome was neurobehavioral outcome at discharge from the Pediatric Intensive Care Unit. To develop the final predictive model, we compared areas under the receiver operating characteristic curves (AUROC) from models with varying combinations of Demographic/Arrest Variables, Examination Signs, and EEG Features. RESULTS: We evaluated 89 infants and children. Initial EEG Background Category was normal in 9 subjects (10%), slow-disorganized in 44 (49%), discontinuous or burst suppression in 22 (25%), and attenuated-featureless in 14 (16%). The final model included Demographic/Arrest Variables (witnessed status, doses of epinephrine, initial lactate after ROSC) and EEG Background Category which achieved AUROC of 0.9 for unfavorable neurobehavioral outcome and 0.83 for mortality. CONCLUSIONS: The addition of standardized EEG Background Categories to readily available CA variables significantly improved early stratification of brain injury severity after pediatric CA.
AIMS: Assessment of brain injury severity early after cardiac arrest (CA) may guide therapeutic interventions and help clinicians counsel families regarding neurologic prognosis. We aimed to determine whether adding EEG features to predictive models including clinical variables and examination signs increased the accuracy of short-term neurobehavioral outcome prediction. METHODS: This was a prospective, observational, single-center study of consecutive infants and children resuscitated from CA. Standardized EEG scoring was performed by an electroencephalographer for the initial EEG timepoint after return of spontaneous circulation (ROSC) and each 12-h segment from the time of ROSC up to 48 h. EEG Background Category was scored as: (1) normal; (2) slow-disorganized; (3) discontinuous or burst-suppression; or (4) attenuated-featureless. The primary outcome was neurobehavioral outcome at discharge from the Pediatric Intensive Care Unit. To develop the final predictive model, we compared areas under the receiver operating characteristic curves (AUROC) from models with varying combinations of Demographic/Arrest Variables, Examination Signs, and EEG Features. RESULTS: We evaluated 89 infants and children. Initial EEG Background Category was normal in 9 subjects (10%), slow-disorganized in 44 (49%), discontinuous or burst suppression in 22 (25%), and attenuated-featureless in 14 (16%). The final model included Demographic/Arrest Variables (witnessed status, doses of epinephrine, initial lactate after ROSC) and EEG Background Category which achieved AUROC of 0.9 for unfavorable neurobehavioral outcome and 0.83 for mortality. CONCLUSIONS: The addition of standardized EEG Background Categories to readily available CA variables significantly improved early stratification of brain injury severity after pediatric CA.
Authors: Sudha Kilaru Kessler; Alexis A Topjian; Ana M Gutierrez-Colina; Rebecca N Ichord; Maureen Donnelly; Vinay M Nadkarni; Robert A Berg; Dennis J Dlugos; Robert R Clancy; Nicholas S Abend Journal: Neurocrit Care Date: 2011-02 Impact factor: 3.210
Authors: Alexis A Topjian; Benjamin French; Robert M Sutton; Thomas Conlon; Vinay M Nadkarni; Frank W Moler; J Michael Dean; Robert A Berg Journal: Crit Care Med Date: 2014-06 Impact factor: 7.598
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Authors: Alexis A Topjian; Allan de Caen; Mark S Wainwright; Benjamin S Abella; Nicholas S Abend; Dianne L Atkins; Melania M Bembea; Ericka L Fink; Anne-Marie Guerguerian; Sarah E Haskell; J Hope Kilgannon; Javier J Lasa; Mary Fran Hazinski Journal: Circulation Date: 2019-06-27 Impact factor: 29.690
Authors: France W Fung; Jiaxin Fan; Lisa Vala; Marin Jacobwitz; Darshana S Parikh; Maureen Donnelly; Alexis A Topjian; Rui Xiao; Nicholas S Abend Journal: Neurology Date: 2020-07-20 Impact factor: 9.910
Authors: Mathias J. Holmberg; Catherine E. Ross; Garrett M. Fitzmaurice; Paul S. Chan; Jordan Duval-Arnould; Anne V. Grossestreuer; Tuyen Yankama; Michael W. Donnino; Lars W. Andersen Journal: Circ Cardiovasc Qual Outcomes Date: 2019-07-09
Authors: Matthew P Kirschen; Alexis A Topjian; Rachel Hammond; Judy Illes; Nicholas S Abend Journal: Pediatr Neurol Date: 2014-07-24 Impact factor: 3.372
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