France W Fung1,2, Marin Jacobwitz1, Darshana S Parikh1,3, Lisa Vala4, Maureen Donnelly4, Jiaxin Fan5, Rui Xiao5, Alexis A Topjian3,6, Nicholas S Abend1,2,4,5,6. 1. Department of Pediatrics (Division of Neurology), Children's Hospital of Philadelphia, Philadelphia, PA, USA. 2. Departments of Neurology and Pediatrics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA. 3. Department of Anesthesia and Critical Care Medicine, Children's Hospital of Philadelphia, Philadelphia, PA, USA. 4. Department of Neurodiagnostics, Children's Hospital of Philadelphia, Philadelphia, PA, USA. 5. Center for Clinical Epidemiology and Biostatistics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA. 6. Department of Anesthesia & Critical Care, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA.
Abstract
OBJECTIVE: Electroencephalographic seizures (ESs) are common in encephalopathic critically ill children, but ES identification with continuous electroencephalography (EEG) monitoring (CEEG) is resource-intense. We aimed to develop an ES prediction model that would enable clinicians to stratify patients by ES risk and optimally target limited CEEG resources. We aimed to determine whether incorporating data from a screening EEG yielded better performance characteristics than models using clinical variables alone. METHODS: We performed a prospective observational study of 719 consecutive critically ill children with acute encephalopathy undergoing CEEG in the pediatric intensive care unit of a quaternary care institution between April 2017 and February 2019. We identified clinical and EEG risk factors for ES. We evaluated model performance with area under the receiver-operating characteristic (ROC) curve (AUC), validated the optimal model with the highest AUC using a fivefold cross-validation, and calculated test characteristics emphasizing high sensitivity. We applied the optimal operating slope strategy to identify the optimal cutoff to define whether a patient should undergo CEEG. RESULTS: The incidence of ES was 26%. Variables associated with increased ES risk included age, acute encephalopathy category, clinical seizures prior to CEEG initiation, EEG background, and epileptiform discharges. Combining clinical and EEG variables yielded better model performance (AUC 0.80) than clinical variables alone (AUC 0.69; P < .01). At a 0.10 cutoff selected to emphasize sensitivity, the optimal model had a sensitivity of 92%, specificity of 37%, positive predictive value of 34%, and negative predictive value of 93%. If applied, the model would limit 29% of patients from undergoing CEEG while failing to identify 8% of patients with ES. SIGNIFICANCE: A model employing readily available clinical and EEG variables could target limited CEEG resources to critically ill children at highest risk for ES, making CEEG-guided management a more viable neuroprotective strategy. Wiley Periodicals, Inc.
OBJECTIVE: Electroencephalographic seizures (ESs) are common in encephalopathic critically ill children, but ES identification with continuous electroencephalography (EEG) monitoring (CEEG) is resource-intense. We aimed to develop an ES prediction model that would enable clinicians to stratify patients by ES risk and optimally target limited CEEG resources. We aimed to determine whether incorporating data from a screening EEG yielded better performance characteristics than models using clinical variables alone. METHODS: We performed a prospective observational study of 719 consecutive critically ill children with acute encephalopathy undergoing CEEG in the pediatric intensive care unit of a quaternary care institution between April 2017 and February 2019. We identified clinical and EEG risk factors for ES. We evaluated model performance with area under the receiver-operating characteristic (ROC) curve (AUC), validated the optimal model with the highest AUC using a fivefold cross-validation, and calculated test characteristics emphasizing high sensitivity. We applied the optimal operating slope strategy to identify the optimal cutoff to define whether a patient should undergo CEEG. RESULTS: The incidence of ES was 26%. Variables associated with increased ES risk included age, acute encephalopathy category, clinical seizures prior to CEEG initiation, EEG background, and epileptiform discharges. Combining clinical and EEG variables yielded better model performance (AUC 0.80) than clinical variables alone (AUC 0.69; P < .01). At a 0.10 cutoff selected to emphasize sensitivity, the optimal model had a sensitivity of 92%, specificity of 37%, positive predictive value of 34%, and negative predictive value of 93%. If applied, the model would limit 29% of patients from undergoing CEEG while failing to identify 8% of patients with ES. SIGNIFICANCE: A model employing readily available clinical and EEG variables could target limited CEEG resources to critically ill children at highest risk for ES, making CEEG-guided management a more viable neuroprotective strategy. Wiley Periodicals, Inc.
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: France W Fung; Jiaxin Fan; Darshana S Parikh; Lisa Vala; Maureen Donnelly; Marin Jacobwitz; Alexis A Topjian; Rui Xiao; Nicholas S Abend Journal: J Clin Neurophysiol Date: 2022-04-20 Impact factor: 2.590
Authors: Jian Hu; France W Fung; Marin Jacobwitz; Darshana S Parikh; Lisa Vala; Maureen Donnelly; Alexis A Topjian; Nicholas S Abend; Rui Xiao Journal: Seizure Date: 2021-03-04 Impact factor: 3.184
Authors: France W Fung; Zi Wang; Darshana S Parikh; Marin Jacobwitz; Lisa Vala; Maureen Donnelly; Alexis A Topjian; Rui Xiao; Nicholas S Abend Journal: Neurology Date: 2021-04-23 Impact factor: 11.800
Authors: Alexis A Topjian; Bingqing Zhang; Rui Xiao; France W Fung; Robert A Berg; Kathryn Graham; Nicholas S Abend Journal: Resuscitation Date: 2021-07-05 Impact factor: 6.251