Browsing by Author "Massey, Shavonne"

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    Association of EEG Background and Neurodevelopmental Outcome in Neonates With Hypoxic-Ischemic Encephalopathy Receiving Hypothermia
    (Wolters Kluwer, 2023-11-27) Glass, Hannah C.; Numis, Adam L.; Comstock, Bryan A.; Gonzalez, Fernando F.; Mietzsch, Ulrike; Bonifacio, Sonia Lomeli; Massey, Shavonne; Thomas, Cameron; Natarajan, Niranjana; Mayock, Dennis E.; Sokol, Gregory M.; Van Meurs, Krisa P.; Ahmad, Kaashif A.; Maitre, Nathalie; Heagerty, Patrick J.; Juul, Sandra E.; Wu, Yvonne W.; Wusthoff, Courtney J.; Pediatrics, School of Medicine
    Background and objectives: Predicting neurodevelopmental outcome for neonates with hypoxic-ischemic encephalopathy (HIE) is important for clinical decision-making, care planning, and parent communication. We examined the relationship between EEG background and neurodevelopmental outcome among children enrolled in a trial of erythropoietin or placebo for neonates with HIE treated with therapeutic hypothermia. Methods: Participants had EEG recorded throughout hypothermia. EEG background was classified as normal, discontinuous, or severely abnormal (defined as burst suppression, low voltage suppressed, or status epilepticus) at 5 1-hour epochs: onset of recording, 24, 36, 48, and 72 hours after birth. The predominant background pattern during the entire continuous video EEG monitoring recording was calculated using the arithmetic mean of the 5 EEG background ratings (normal = 0; discontinuous = 1; severely abnormal = 2) as follows: "predominantly normal" (mean = 0), "normal/discontinuous" (0 < mean<1), "predominantly discontinuous" (mean = 1), "discontinuous/severely abnormal" (1 < mean<2), or "predominantly severely abnormal" (mean = 2). Primary outcome was death or neurodevelopmental impairment (NDI) defined as cerebral palsy, Gross Motor Function Classification Score ≥1, or cognitive score <90 on Bayley Scales of Infant Toddler Development, third edition at age 2 years. Neurodevelopment was also categorized into a 5-level ordinal measure: no, mild, moderate, severe NDI, or death for secondary analysis. We used generalized linear regression models with robust standard errors to assess the relative risk of death or NDI by EEG background in both unadjusted and adjusted analyses controlling for the effects of treatment group, sex, HIE severity, and study recruitment site. Results: Among 142 neonates, the predominant background EEG pattern was predominantly normal in 35 (25%), normal/discontinuous in 68 (48%), predominantly discontinuous in 11 (7.7%), discontinuous/severely abnormal in 16 (11%), and predominantly severely abnormal in 12 (8.5%). Increasing severity of background across monitoring epochs was associated with increasingly worse clinical outcomes. Children with severe EEG background abnormality at any time point (n = 36, 25%) were significantly more likely to die or have severe NDI at 2 years (adjusted relative risk: 7.95, 95% CI 3.49-18.12). Discussion: EEG background is strongly associated with NDI at age 2 years. These results can be used to assist health care providers to plan follow-up care and counsel families for decision-making related to goals of care.
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    Development and Validation of a Seizure Prediction Model in Neonates Following Cardiac Surgery
    (Elsevier, 2020) Naim, Maryam Y.; Putt, Mary; Abend, Nicholas S.; Mastropietro, Christopher W.; Frank, Deborah U.; Chen, Jonathan M.; Fuller, Stephanie; Gangemi, James J.; Gaynor, J. William; Heinan, Kristin; Licht, Daniel J.; Mascio, Christopher E.; Massey, Shavonne; Roeser, Mark E.; Smith, Clyde J.; Kimmel, Stephen E.; Pediatrics, School of Medicine
    BACKGROUND Electroencephalographic seizures (ES) following neonatal cardiac surgery are often subclinical and have been associated with poor outcomes. An accurate ES prediction model could allow targeted continuous electroencephalographic monitoring (CEEG) for high-risk neonates. METHODS Development and validation of ES prediction models in a multi-center prospective cohort where all postoperative neonates with cardiopulmonary bypass (CPB) underwent CEEG. RESULTS ES occurred in 7.4% of neonates (78 of 1053). Model predictors included gestational age, head circumference, single ventricle defect, DHCA duration, cardiac arrest, nitric oxide, ECMO, and delayed sternal closure. The model performed well in the derivation cohort (c-statistic 0.77, Hosmer-Lemeshow p=0.56), with a net benefit (NB) over monitoring all and none over a threshold probability of 2% in decision curve analysis (DCA). The model had good calibration in the validation cohort (Hosmer-Lemeshow, p=0.60); however, discrimination was poor (c-statistic 0.61) and in DCA there was no NB of the prediction model between the threshold probabilities of 8% and 18%. Using a cut-point that emphasized negative predictive value (NPV) in the derivation cohort, 32% (236 of 737) of neonates would not undergo CEEG, including 3.5% (2 of 58) with ES (NPV 99%, sensitivity 97%). CONCLUSIONS In this large prospective cohort, a prediction model of ES in neonates following CPB had good performance in the derivation cohort with a NB in DCA. However, performance in the validation cohort was weak with poor discrimination, calibration, and no NB in DCA. These findings support CEEG monitoring of all neonates following CPB.