Kyoung Min You1, Gil Joon Suh2, Woon Yong Kwon3, Kyung Su Kim4, Sang-Bae Ko5, Min Ji Park4, Taegyun Kim4, Jung-In Ko4. 1. Department of Emergency Medicine, Boramae Medical Center, 20 Boramae-ro 5-gil, Dongjak-gu, Seoul 07061, Republic of Korea. 2. Department of Emergency Medicine, Seoul National University College of Medicine, 103 Daehak-ro, Jongno-gu, Seoul 03080, Republic of Korea; Department of Emergency Medicine, Seoul National University Hospital, 101 Daehak-ro, Jongno-gu, Seoul 03080, Republic of Korea. Electronic address: suhgil@snu.ac.kr. 3. Department of Emergency Medicine, Seoul National University College of Medicine, 103 Daehak-ro, Jongno-gu, Seoul 03080, Republic of Korea; Department of Emergency Medicine, Seoul National University Hospital, 101 Daehak-ro, Jongno-gu, Seoul 03080, Republic of Korea. 4. Department of Emergency Medicine, Seoul National University Hospital, 101 Daehak-ro, Jongno-gu, Seoul 03080, Republic of Korea. 5. Department of Neurology, Seoul National University Hospital, 101 Daehak-ro, Jongno-gu, Seoul 03080, Republic of Korea.
Abstract
INTRODUCTION: We performed this study to investigate whether the SEDline system, a 4-channel-processed electroencephalography (EEG) monitoring device in the frontal area, can detect epileptiform discharges accurately during post-resuscitation care in comatose cardiac arrest survivors. METHODS: Adult comatose cardiac arrest survivors, who were admitted to the intensive care unit (ICU) for post-resuscitation care including TTM, were enrolled. Within 72h post-return of spontaneous circulation (ROSC), conventional EEG was conducted for 30min. The SEDline system data were recorded with a video camera simultaneously with conventional EEG. Data retrieved from conventional EEG were interpreted by a neurologist and data from the SEDline system were interpreted by three emergency physicians blinded to the conventional EEG data. Then, the sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and accuracy of the SEDline system to detect epileptiform discharges were calculated. RESULTS: Thirty-nine patients were enrolled in this study. Epileptiform discharges were confirmed in 6 patients (15.4%) who had the same patterns of generalized periodic epileptiform discharges in both conventional EEG and the concurrent SEDline system. The SEDline system showed 100.0% (95% confidence interval (CI), 54.1-100.0%) of sensitivity, 100.0% (95% CI, 89.4-100.0%) of specificity, 100.0% (95% CI, 54.1-100.0%) of PPV, and 100.0% (95% CI, 89.4-100.0%) of NPV. The overall classification accuracy of the SEDline system to detect epileptiform discharges was 100.0%. CONCLUSION: The SEDline system detected epileptiform discharges accurately in comatose cardiac arrest survivors during post-resuscitation care.
INTRODUCTION: We performed this study to investigate whether the SEDline system, a 4-channel-processed electroencephalography (EEG) monitoring device in the frontal area, can detect epileptiform discharges accurately during post-resuscitation care in comatose cardiac arrest survivors. METHODS: Adult comatose cardiac arrest survivors, who were admitted to the intensive care unit (ICU) for post-resuscitation care including TTM, were enrolled. Within 72h post-return of spontaneous circulation (ROSC), conventional EEG was conducted for 30min. The SEDline system data were recorded with a video camera simultaneously with conventional EEG. Data retrieved from conventional EEG were interpreted by a neurologist and data from the SEDline system were interpreted by three emergency physicians blinded to the conventional EEG data. Then, the sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and accuracy of the SEDline system to detect epileptiform discharges were calculated. RESULTS: Thirty-nine patients were enrolled in this study. Epileptiform discharges were confirmed in 6 patients (15.4%) who had the same patterns of generalized periodic epileptiform discharges in both conventional EEG and the concurrent SEDline system. The SEDline system showed 100.0% (95% confidence interval (CI), 54.1-100.0%) of sensitivity, 100.0% (95% CI, 89.4-100.0%) of specificity, 100.0% (95% CI, 54.1-100.0%) of PPV, and 100.0% (95% CI, 89.4-100.0%) of NPV. The overall classification accuracy of the SEDline system to detect epileptiform discharges was 100.0%. CONCLUSION: The SEDline system detected epileptiform discharges accurately in comatose cardiac arrest survivors during post-resuscitation care.
Authors: Edilberto Amorim; Shirley S Mo; Sebastian Palacios; Mohammad M Ghassemi; Wei-Hung Weng; Sydney S Cash; Matthew T Bianchi; M Brandon Westover Journal: Neurology Date: 2020-07-13 Impact factor: 9.910
Authors: Seungha Lee; Xuelong Zhao; Kathryn A Davis; Alexis A Topjian; Brian Litt; Nicholas S Abend Journal: Neurology Date: 2019-04-10 Impact factor: 9.910