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Data on multimodal approach for early poor outcome (Cerebral Performance Categories 3-5) prediction after cardiac arrest.

ABSTRACT: The data presented in this article are related to our research article entitled 'Neurophysiological and neuroradiological multimodal approach for early poor outcome prediction after cardiac arrest' (Scarpino et al., 2018) [1]. We reported two additional analyses, including results gathered from somatosensory evoked potentials(SEPs), brain computed tomography(CT) and electroencephalography(EEG) performed on 183 subjects within the first 24?h after cardiac arrest(CA). In the first analysis, we considered the Cerebral Performance Categories(CPC) 3, 4 and 5a,b (severe disability, unresponsive wakefulness state, neurological death and non-neurological death, respectively) as poor outcomes. In the second analysis, patients that died from non-neurological causes (CPC 5b) were excluded from the analysis. Concerning the first analysis, bilateral absent/absent-pathologic(AA/AP) cortical SEPs predicted poor outcome with a sensitivity of 49.3%. A Grey Matter/White Matter(GM/WM) ratio <1.21 predicted poor outcome with a sensitivity of 41.7%. Isoelectric/burst-suppression EEG patterns predicted poor outcome with a sensitivity of 33.5%. If at least one of these poor prognostic patterns was present, the sensitivity for an ominous outcome increased to 60.9%. Concerning the second analysis, AA/AP cortical SEPs predicted poor outcome with a sensitivity of 52.5%. GM/WM ratio <1.21 predicted poor outcome with a sensitivity of 50.4%. Isoelectric/burst-suppression EEG patterns predicted poor outcome with a sensitivity of 39.8%.

SUBMITTER: Scarpino M

PROVIDER: S-EPMC5998182 | biostudies-literature | 2018 Aug

REPOSITORIES: biostudies-literature

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Data on multimodal approach for early poor outcome (Cerebral Performance Categories 3-5) prediction after cardiac arrest.

Scarpino Maenia M Lanzo Giovanni G Lolli Francesco F Carrai Riccardo R Moretti Marco M Spalletti Maddalena M Cozzolino Morena M Peris Adriano A Amantini Aldo A Grippo Antonello A

Data in brief 20180525

The data presented in this article are related to our research article entitled 'Neurophysiological and neuroradiological multimodal approach for early poor outcome prediction after cardiac arrest' (Scarpino et al., 2018) [1]. We reported two additional analyses, including results gathered from somatosensory evoked potentials(SEPs), brain computed tomography(CT) and electroencephalography(EEG) performed on 183 subjects within the first 24 h after cardiac arrest(CA). In the first analysis, we con ...[more]

PMID: 29904713

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Project description:PurposeWe aimed to determine which early EEG features and feature combinations most accurately predicted short-term neurobehavioral outcomes and survival in children resuscitated after cardiac arrest.MethodsThis was a prospective, single-center observational study of infants and children resuscitated from cardiac arrest who underwent conventional EEG monitoring with standardized EEG scoring. Logistic regression evaluated the marginal effect of each EEG variable or EEG variable combinations on the outcome. The primary outcome was neurobehavioral outcome (Pediatric Cerebral Performance Category score), and the secondary outcome was mortality. The authors identified the models with the highest areas under the receiver operating characteristic curve (AUC), evaluated the optimal models using a 5-fold cross-validation approach, and calculated test characteristics maximizing specificity.ResultsEighty-nine infants and children were evaluated. Unfavorable neurologic outcome (Pediatric Cerebral Performance Category score 4-6) occurred in 44 subjects (49%), including mortality in 30 subjects (34%). A model incorporating a four-level EEG Background Category (normal, slow-disorganized, discontinuous or burst-suppression, or attenuated-flat), stage 2 Sleep Transients (present or absent), and Reactivity-Variability (present or absent) had the highest AUC. Five-fold cross-validation for the optimal model predicting neurologic outcome indicated a mean AUC of 0.75 (range, 0.70-0.81) and for the optimal model predicting mortality indicated a mean AUC of 0.84 (range, 0.76-0.97). The specificity for unfavorable neurologic outcome and mortality were 95% and 97%, respectively. The positive predictive value for unfavorable neurologic outcome and mortality were both 86%.ConclusionsThe specificity of the optimal model using a combination of early EEG features was high for unfavorable neurologic outcome and mortality in critically ill children after cardiac arrest. However, the positive predictive value was only 86% for both outcomes. Therefore, EEG data must be considered in overall clinical context when used for neuroprognostication early after cardiac arrest.

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Data on multimodal approach for early poor outcome (Cerebral Performance Categories 3-5) prediction after cardiac arrest.

Publications

Data on multimodal approach for early poor outcome (Cerebral Performance Categories 3-5) prediction after cardiac arrest.

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