The future is in the background: background EEG patterns, not acute seizures, predict epilepsy and neurodevelopmental outcomes in neonatal HIE
Notice bibliographique
Résumé
Background Hypoxic ischemic encephalopathy (HIE) is the most common neurologic emergency in the neonatal population, with a broad spectrum of potential neurodevelopmental outcomes. Additionally, HIE is the most common cause of seizures during the acute neonatal period. Unfortunately, predicting neurodevelopmental outcomes and epilepsy risk is difficult in this population, and seizure burden during the acute period has not consistently been correlated with outcomes in prior studies. We aimed to examine EEG background data to determine whether there is a relationship between background abnormalities, neurodevelopmental outcomes, and epilepsy risk, and whether this information is more informative for predicting outcomes compared to other clinical data points. Methods Patients were retrospectively recruited from level 3 Neonatal Intensive Care Units (NICU's) in Calgary, Alberta, from 2014 to 2020. All patients who met the criteria for therapeutic hypothermia after being classified as at risk for HIE were included in the study. Clinical information captured included measures from clinical examination, blood work, MRI (day 3–5, scored using Barkovich scoring system) and medications. Continuous video EEG (cvEEG) recordings were separated into day 1, 2, and 3, and separate classifications systems were used for background and ictal findings. Neurodevelopmental follow-up was completed at two years of age, and patients were also categorized as having no epilepsy, or either well-controlled or refractory epilepsy. Poisson regression models and relative risk were used to compare background and ictal scores to long term neurodevelopmental outcomes and future epilepsy risk. Three supervised learning algorithms were trained to predict neurodevelopmental outcomes based on clinical factors. Results Two-hundred and six patients were eligible for the study. Among neonates with seizures, only 18% developed epilepsy, while 52% of those with severely abnormal EEG background patterns did. Total ictal burden was not significantly associated with epilepsy at follow up, and no antiseizures medications were significant predictors. In contrast, EEG background score was strongly associated with epilepsy risk (adjusted ß = 2.75, p = 0.002), with severely abnormal backgrounds conferring significantly increased risk (37.5% vs. 5.2%, RR = 7.22, 95% CI: 3.09–16.88). Similarly, ictal burden did not predict poor neurodevelopmental outcome or death, whereas background score was a strong predictor (adjusted ß = 1.74, p < 0.001; RR = 2.44, 95% CI: 1.70–3.50). Machine learning models identified background features as more predictive than ictal scores, with XGBoost achieving the best classification performance (accuracy 0.724) and random forest yielding the highest AUC (0.751). Conclusions In our cohort, EEG background patterns outperformed ictal burden in predicting both neurodevelopmental outcomes and future epilepsy risk. Although background patterns are not directly modifiable, they provide powerful, early markers of brain injury severity, offering clinicians a valuable tool for prognostication and family counseling at a critical juncture in care.
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».