PURPOSE: We aimed to determine whether conventional standardized EEG features could be consolidated into a more limited number of factors and whether the derived factor scores changed during the acute period after pediatric cardiac arrest. METHODS: Children resuscitated after cardiac arrest underwent conventional continuous EEG monitoring. The EEG was scored in 12-hour epochs for up to 72-hours after return of circulation by an electroencephalographer using standardized critical care EEG terminology. We performed a polychoric factor analysis to determine whether numerous observed EEG features could be represented by a smaller number of derived factors. Linear mixed-effects regression models and heat maps evaluated whether the factor scores remained stable across epochs. RESULTS: We performed EEG monitoring in 89 consecutive children, which yielded 453 EEG segments. We identified two factors, which were not correlated. The background features were factor loaded with the features continuity, voltage, and frequency. The intermittent features were factor loaded with the features of seizures, periodic patterns, and interictal discharges. Factor scores were calculated for each EEG segment. Linear, mixed-effect, regression results indicated that the factor scores did not change over time for the background features factor (coefficient, 0.18; 95% confidence interval, 0.04-0.07; P = 0.52) or the intermittent features factor (coefficient, -0.003; 95% confidence interval, -0.02 to 0.01; P = 0.70). However, heat maps showed that some individual subjects did experience factor score changes over time, particularly if they had medium initial factor scores. CONCLUSIONS: Subsequent studies assessing whether EEG is informative for neurobehavioral outcomes after pediatric cardiac arrest could combine numerous EEG features into two factors, each reflecting multiple background and intermittent features. Furthermore, the factor scores would be expected to remain stable during the acute period for most subjects.
PURPOSE: We aimed to determine whether conventional standardized EEG features could be consolidated into a more limited number of factors and whether the derived factor scores changed during the acute period after pediatric cardiac arrest. METHODS:Children resuscitated after cardiac arrest underwent conventional continuous EEG monitoring. The EEG was scored in 12-hour epochs for up to 72-hours after return of circulation by an electroencephalographer using standardized critical care EEG terminology. We performed a polychoric factor analysis to determine whether numerous observed EEG features could be represented by a smaller number of derived factors. Linear mixed-effects regression models and heat maps evaluated whether the factor scores remained stable across epochs. RESULTS: We performed EEG monitoring in 89 consecutive children, which yielded 453 EEG segments. We identified two factors, which were not correlated. The background features were factor loaded with the features continuity, voltage, and frequency. The intermittent features were factor loaded with the features of seizures, periodic patterns, and interictal discharges. Factor scores were calculated for each EEG segment. Linear, mixed-effect, regression results indicated that the factor scores did not change over time for the background features factor (coefficient, 0.18; 95% confidence interval, 0.04-0.07; P = 0.52) or the intermittent features factor (coefficient, -0.003; 95% confidence interval, -0.02 to 0.01; P = 0.70). However, heat maps showed that some individual subjects did experience factor score changes over time, particularly if they had medium initial factor scores. CONCLUSIONS: Subsequent studies assessing whether EEG is informative for neurobehavioral outcomes after pediatric cardiac arrest could combine numerous EEG features into two factors, each reflecting multiple background and intermittent features. Furthermore, the factor scores would be expected to remain stable during the acute period for most subjects.
Authors: Paul A Harris; Robert Taylor; Robert Thielke; Jonathon Payne; Nathaniel Gonzalez; Jose G Conde Journal: J Biomed Inform Date: 2008-09-30 Impact factor: 6.317
Authors: Rebecca M Starling; Karuna Shekdar; Dan Licht; Vinay M Nadkarni; Robert A Berg; Alexis A Topjian Journal: Pediatr Crit Care Med Date: 2015-07 Impact factor: 3.624
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
Authors: Thomas W Conlon; Christine B Falkensammer; Rachel S Hammond; Vinay M Nadkarni; Robert A Berg; Alexis A Topjian Journal: Pediatr Crit Care Med Date: 2015-02 Impact factor: 3.624
Authors: Katherine L Wagenman; Taylor P Blake; Sarah M Sanchez; Maria T Schultheis; Jerilynn Radcliffe; Robert A Berg; Dennis J Dlugos; Alexis A Topjian; Nicholas S Abend Journal: Neurology Date: 2014-01-02 Impact factor: 9.910
Authors: Alexis A Topjian; Sarah M Sánchez; Justine Shults; Robert A Berg; Dennis J Dlugos; Nicholas S Abend Journal: Pediatr Crit Care Med Date: 2016-06 Impact factor: 3.624
Authors: N S Abend; A Topjian; R Ichord; S T Herman; M Helfaer; M Donnelly; V Nadkarni; D J Dlugos; R R Clancy Journal: Neurology Date: 2009-06-02 Impact factor: 9.910
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: Jennifer C Laws; Lori C Jordan; Lindsay M Pagano; John C Wellons; Michael S Wolf Journal: Pediatr Neurol Date: 2022-02-02 Impact factor: 3.372
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: 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