FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W4309366979> ?p ?o ?g. }

• W4309366979 endingPage "468" @default.
• W4309366979 startingPage "456" @default.
• W4309366979 abstract "Abstract Objective To assess if early clinical and electroencephalography (EEG) features predict later seizure development in infants with hypoxic‐ischemic encephalopathy (HIE). Methods Clinical and EEG parameters <12 h of birth from infants with HIE across eight European Neonatal Units were used to develop seizure‐prediction models. Clinical parameters included intrapartum complications, fetal distress, gestational age, delivery mode, gender, birth weight, Apgar scores, assisted ventilation, cord pH, and blood gases. The earliest EEG hour provided a qualitative analysis (discontinuity, amplitude, asymmetry/asynchrony, sleep–wake cycle [SWC]) and a quantitative analysis (power, discontinuity, spectral distribution, inter‐hemispheric connectivity) from full montage and two‐channel amplitude‐integrated EEG (aEEG). Subgroup analysis, only including infants without anti‐seizure medication (ASM) prior to EEG was also performed. Machine‐learning (ML) models (random forest and gradient boosting algorithms) were developed to predict infants who would later develop seizures and assessed using Matthews correlation coefficient (MCC) and area under the receiver‐operating characteristic curve (AUC). Results The study included 162 infants with HIE (53 had seizures). Low Apgar, need for ventilation, high lactate, low base excess, absent SWC, low EEG power, and increased EEG discontinuity were associated with seizures. The following predictive models were developed: clinical (MCC 0.368, AUC 0.681), qualitative EEG (MCC 0.467, AUC 0.729), quantitative EEG (MCC 0.473, AUC 0.730), clinical and qualitative EEG (MCC 0.470, AUC 0.721), and clinical and quantitative EEG (MCC 0.513, AUC 0.746). The clinical and qualitative‐EEG model significantly outperformed the clinical model alone (MCC 0.470 vs 0.368, p ‐value .037). The clinical and quantitative‐EEG model significantly outperformed the clinical model (MCC 0.513 vs 0.368, p ‐value .012). The clinical and quantitative‐EEG model for infants without ASM ( n = 131) had MCC 0.588, AUC 0.832. Performance for quantitative aEEG ( n = 159) was MCC 0.381, AUC 0.696 and clinical and quantitative aEEG was MCC 0.384, AUC 0.720. Significance Early EEG background analysis combined with readily available clinical data helped predict infants who were at highest risk of seizures, hours before they occur. Automated quantitative‐EEG analysis was as good as expert analysis for predicting seizures, supporting the use of automated assessment tools for early evaluation of HIE." @default.
• W4309366979 created "2022-11-26" @default.
• W4309366979 creator A5009619268 @default.
• W4309366979 creator A5015129043 @default.
• W4309366979 creator A5016154619 @default.
• W4309366979 creator A5018146969 @default.
• W4309366979 creator A5019262839 @default.
• W4309366979 creator A5020472295 @default.
• W4309366979 creator A5021698345 @default.
• W4309366979 creator A5025399359 @default.
• W4309366979 creator A5026906208 @default.
• W4309366979 creator A5028878593 @default.
• W4309366979 creator A5032337420 @default.
• W4309366979 creator A5036706635 @default.
• W4309366979 creator A5038391182 @default.
• W4309366979 creator A5040351944 @default.
• W4309366979 creator A5042600112 @default.
• W4309366979 creator A5048180085 @default.
• W4309366979 creator A5048447416 @default.
• W4309366979 creator A5049636753 @default.
• W4309366979 creator A5050938248 @default.
• W4309366979 creator A5053377817 @default.
• W4309366979 creator A5054143601 @default.
• W4309366979 creator A5056655255 @default.
• W4309366979 creator A5061931444 @default.
• W4309366979 creator A5067995773 @default.
• W4309366979 creator A5070186324 @default.
• W4309366979 creator A5071681020 @default.
• W4309366979 creator A5072503871 @default.
• W4309366979 creator A5075843152 @default.
• W4309366979 creator A5077336767 @default.
• W4309366979 creator A5079129373 @default.
• W4309366979 creator A5079944668 @default.
• W4309366979 creator A5082431457 @default.
• W4309366979 creator A5082486070 @default.
• W4309366979 creator A5084014715 @default.
• W4309366979 creator A5084153901 @default.
• W4309366979 creator A5090580356 @default.
• W4309366979 date "2022-12-20" @default.
• W4309366979 modified "2023-10-01" @default.
• W4309366979 title "Machine learning for the early prediction of infants with electrographic seizures in neonatal hypoxic‐ischemic encephalopathy" @default.
• W4309366979 cites W1498702089 @default.
• W4309366979 cites W1569193735 @default.
• W4309366979 cites W1967002727 @default.
• W4309366979 cites W1973603186 @default.
• W4309366979 cites W1981994271 @default.
• W4309366979 cites W1986535199 @default.
• W4309366979 cites W2004468992 @default.
• W4309366979 cites W2020458933 @default.
• W4309366979 cites W2024313504 @default.
• W4309366979 cites W2032027948 @default.
• W4309366979 cites W2036648234 @default.
• W4309366979 cites W2048327337 @default.
• W4309366979 cites W2052684622 @default.
• W4309366979 cites W2063091823 @default.
• W4309366979 cites W2064786050 @default.
• W4309366979 cites W2066997495 @default.
• W4309366979 cites W2077648208 @default.
• W4309366979 cites W2078348791 @default.
• W4309366979 cites W2082995148 @default.
• W4309366979 cites W2083733099 @default.
• W4309366979 cites W2084175167 @default.
• W4309366979 cites W2091855602 @default.
• W4309366979 cites W2098951649 @default.
• W4309366979 cites W2103006189 @default.
• W4309366979 cites W2109593524 @default.
• W4309366979 cites W2111321542 @default.
• W4309366979 cites W2132575173 @default.
• W4309366979 cites W2140820990 @default.
• W4309366979 cites W2145818012 @default.
• W4309366979 cites W2162332184 @default.
• W4309366979 cites W2162755743 @default.
• W4309366979 cites W2164127547 @default.
• W4309366979 cites W2319109216 @default.
• W4309366979 cites W2520685154 @default.
• W4309366979 cites W2528375557 @default.
• W4309366979 cites W2549496864 @default.
• W4309366979 cites W2606517034 @default.
• W4309366979 cites W2765430261 @default.
• W4309366979 cites W2802042868 @default.
• W4309366979 cites W2902222279 @default.
• W4309366979 cites W2924240557 @default.
• W4309366979 cites W2971893337 @default.
• W4309366979 cites W2980121027 @default.
• W4309366979 cites W2998876539 @default.
• W4309366979 cites W2999309192 @default.
• W4309366979 cites W3023997891 @default.
• W4309366979 cites W3080952300 @default.
• W4309366979 cites W3086993258 @default.
• W4309366979 cites W3097770020 @default.
• W4309366979 cites W3129128079 @default.
• W4309366979 cites W4309366979 @default.
• W4309366979 doi "https://doi.org/10.1111/epi.17468" @default.
• W4309366979 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/36398397" @default.
• W4309366979 hasPublicationYear "2022" @default.
• W4309366979 type Work @default.
• W4309366979 citedByCount "6" @default.
• W4309366979 countsByYear W43093669792022 @default.

• next