109 Examining the impact of a genetic etiology on clinical outcomes in children living with paediatric epilepsy
Notice bibliographique
Résumé
Abstract Background Paediatric epilepsy poses a substantial clinical challenge, particularly when standard treatments fall short of achieving optimal control. Traditional approaches to antiseizure medication (ASM) selection often involve trial and error, which can lead to extended periods of inadequate management and increased risk of adverse effects. Genetic testing has emerged as a promising tool for guiding ASM selection by identifying epilepsy-related genetic variations, allowing for a more personalized approach. Preliminary studies suggest that genetic results can help optimize ASM choice; however, research is still limited in directly assessing the impact on clinical outcomes, such as long-term seizure control and quality of life improvements. Further investigation is needed to evaluate how genetic insights translate into tangible benefits for paediatric epilepsy patients. Objectives This study aims to compare seizure outcomes between paediatric epilepsy patients with a genetic etiology and those with a non-genetic etiology, using a sample of New Brunswick children who have undergone genetic testing. Positive genetic test results can lead to tailored treatment plans, while negative results typically receive standard treatment. By evaluating outcomes in children receiving personalized management based on genetic findings versus those receiving conventional treatment, the study seeks to determine if genetic etiology is associated with improved seizure outcomes in paediatric epilepsy. Design/Methods This longitudinal study utilized quantitative methods to assess seizure outcomes in paediatric epilepsy patients at three time points: baseline (6 months pre-genetic test), 6 months post-genetic test, and 12 months post-genetic test. The study included New Brunswick patients aged 0-18 who underwent genetic testing. A sample of 35 patients was selected using proportionate random sampling, comprising 21 with positive genetic test results and 14 with negative results. Seizure outcomes were measured for frequency, severity, duration, and freedom at each time point. Data was extracted from deidentified patient charts, and genetic test results were interpreted independently to minimize bias. Statistical analysis included descriptive statistics for demographic variables and outcomes, followed by Friedman Tests to assess within-group changes over time. Pairwise Wilcoxon Signed-Ranks Tests evaluated specific time point differences, while Cochran’s Q tests assessed seizure freedom outcomes, all at a significance level of α = 0.05. Results For patients with positive genetic test results, significant reductions in seizure frequency, severity, and duration were observed from baseline to 12 months, indicating enhanced outcomes with targeted ASM treatment. Specifically, seizure frequency decreased significantly between baseline and 12 months (p = 0.0005), and 90.5% of the positive test group achieved seizure freedom within 12 months. In contrast, the negative test group showed a slower improvement trajectory, with only 64.3% achieving seizure freedom by 12 months. Moreover, improvements in seizure outcomes for the positive test group occurred earlier, with reductions in severity evident by 6 months. The study supports that a positive genetic test can potentially enable faster, more effective treatment in paediatric epilepsy by guiding ASM selection and mitigating adverse effects associated with standard treatments. Conclusion This study suggests that in paediatric epilepsy positive genetic test results may facilitate more effective ASM selection, earlier reductions in seizure severity, and potentially higher rates of seizure freedom, underscoring the potential for improved patient outcomes through personalized management. However, as an initial study, these findings highlight the need for further research to fully understand the long-term impacts of tailored management and the broader applicability of genetic testing in diverse paediatric epilepsy populations.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,003 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 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 source (Gemma direct ou Codex distillé), 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 ».