Maayke Hunfeld

255 General discussion References 1. Topjian AA, Sanchez SM, Shults J, Berg RA, Dlugos DJ, AbendNS. Early Electroencephalographic Background Features Predict Outcomes in Children Resuscitated From Cardiac Arrest. Pediatr Crit Care Med. 2016;17(6):547-57. 2. Ostendorf AP, Hartman ME, Friess SH. Early Electroencephalographic Findings Correlate With Neurologic Outcome in Children Following Cardiac Arrest. Pediatr Crit Care Med. 2016;17(7):667-76. 3. Kessler SK, Topjian AA, Gutierrez-Colina AM, Ichord RN, Donnelly M, Nadkarni VM, et al. Short-term outcome prediction by electroencephalographic features in children treated with therapeutic hypothermia after cardiac arrest. Neurocrit Care. 2011;14(1):37-43. 4. Nishisaki A, Sullivan J, 3rd, Steger B, Bayer CR, Dlugos D, Lin R, et al. Retrospective analysis of the prognostic value of electroencephalography patterns obtained in pediatric in-hospital cardiac arrest survivors during three years. Pediatr Crit Care Med. 2007;8(1):10-7. 5. Brooks GA, Park JT. Clinical and Electroencephalographic Correlates in Pediatric Cardiac Arrest: Experience at a Tertiary Care Center. Neuropediatrics. 2018;49(5):324-9. 6. Ducharme-Crevier L, Press CA, Kurz JE, Mills MG, Goldstein JL, Wainwright MS. Early Presence of Sleep Spindles on Electroencephalography Is Associated With Good Outcome After Pediatric Cardiac Arrest. Pediatr Crit Care Med. 2017;18(5):452-60. 7. BP Rourke JFJ, et al. Child Neuropsychology. An Introduction to Theory, Research, and Clinical Practice. New York, NY: The Guilford Press; 1983. 8. Topjian AA, de Caen A, Wainwright MS, Abella BS, Abend NS, Atkins DL, et al. Pediatric Post-Cardiac Arrest Care: A Scientific Statement From the American Heart Association. Circulation. 2019;140(6):e194-e233. 9. Wilkinson MD, Dumontier M, Aalbersberg IJ, Appleton G, Axton M, Baak A, et al. The FAIR Guiding Principles for scientific data management and stewardship (vol 15, 160018, 2016). Sci Data. 2019;6. 10. Eisermann M, Kaminska A, Moutard ML, Soufflet C, Plouin P. Normal EEG in childhood: from neonates to adolescents. Neurophysiol Clin. 2013;43(1):35-65. 11. Abend NS, Gutierrez-Colina A, Zhao H, Guo R, Marsh E, Clancy RR, et al. Interobserver reproducibility of electroencephalogram interpretation in critically ill children. J Clin Neurophysiol. 2011;28(1):15-9. 12. Young GB, McLachlan RS, Kreeft JH, Demelo JD. An electroencephalographic classification for coma. Can J Neurol Sci. 1997;24(4):320-5. 13. Grant AC, Abdel-Baki SG, Weedon J, Arnedo V, Chari G, Koziorynska E, et al. EEG interpretation reliability and interpreter confidence: a large single-center study. Epilepsy Behav. 2014;32:102-7. 14. Tjepkema-Cloostermans MC, van Meulen FB, Meinsma G, van Putten MJ. A Cerebral Recovery Index (CRI) for early prognosis in patients after cardiac arrest. Crit Care. 2013;17(5):R252. 15. Tjepkema-Cloostermans MC, da Silva Lourenco C, Ruijter BJ, Tromp SC, Drost G, Kornips FHM, et al. Outcome Prediction in Postanoxic Coma With Deep Learning. Crit Care Med. 2019;47(10):1424-32. 16. Amorim E, van der Stoel M, Nagaraj SB, Ghassemi MM, Jing J, O’Reilly UM, et al. Quantitative EEG reactivity and machine learning for prognostication in hypoxic-ischemic brain injury. Clin Neurophysiol. 2019;130(10):1908-16. 17. Wiley SL, Razavi B, Krishnamohan P, Mlynash M, Eyngorn I, Meador KJ, et al. Quantitative EEG Metrics Differ Between Outcome Groups and Change Over the First 72 h in Comatose Cardiac Arrest Patients. Neurocrit Care. 2018;28(1):51-9. 8