Anatomical Determinants of Epilepsy Surgery Outcomes: A Systematic Review and Individual Patient Data Meta-Analysis
Notice bibliographique
Résumé
Abstract Importance To date, epilepsy surgery outcomes remain highly variable, with seizure freedom rates hovering around 50-70%, highlighting the need for a deeper understanding of the factors influencing surgical success. Objective To conduct an individual patient data meta-analysis of epilepsy surgery outcomes in drug-resistant epilepsy, leveraging granular, patient-level data to identify key clinical, demographic, and anatomical factors that influence surgical success. Data Sources MEDLINE (via Ovid), Embase, and Scopus were searched from inception to August 9, 2024. Study Selection Primary studies reporting patient-level surgical outcomes and clinical information in patients with drug-resistant epilepsy. Data Extraction and Synthesis Data were abstracted following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. Unique patient data from 385 studies were pooled, yielding 5,588 patients with outcomes, localization, demographics, pathology, and other findings. Surgical success rates were reported with 95% Wald confidence intervals. Main Outcome(s) and Measure(s) Measured outcomes were surgical success rates (% Engel 1/ ILAE 1-2) based on key patient and disease-specific factors. Statistical associations were tested with chi-squared tests (p<0.05), effect sizes measured with Cramer’s V, and post-hoc comparisons adjusted using the false discovery rate. Results Surgical success rates (Engel I/ILAE 1-2) have remained stable over the past decades (r=0.25, p=0.13), while seizure freedom rates (Engel Ia/ILAE 1) have significantly improved (r=0.59, p<0.01). This occurred alongside a rise in surgical interventions, including more complex cases, as indicated by increased stereo-EEG use, and the adoption of minimally invasive techniques. Surgical success varied significantly by lobar anatomy (χ 2 =52, p<0.01), with the highest success rates in temporal (68.6% [67.0–70.1%]) and insular lobes (66.2% [55.4–77.0%]), although only temporal outcomes were statistically significant. Multilobar resections had lower success rates, with outcomes varying significantly by lobar combination (χ 2 =25, p=0.02). Variability in outcomes were also influenced by histopathology and MRI findings (χ 2 =121, p<0.001), and the type of surgical intervention (χ 2 =30.5, p<0.001). Conclusions and Relevance This meta-analysis combined patient-level data from multiple studies to understand how individual patient characteristics influence surgical outcomes. Identifying these prognostic factors can guide more personalized patient selection and surgical planning, and ultimately improve rates of favorable outcomes in epilepsy surgery. Key Points Question What are the main factors influencing surgical success in drug-resistant epilepsy patients? Findings A systematic review of 5,588 individual patient data from 385 primary research studies showed that the anatomical region, surgical technique, and histopathological diagnosis impact epilepsy surgery outcomes, with varying success rates based on these factors’ interaction. Meaning Presurgical evaluations and research into potential biomarkers and treatment options should consider these patient-specific factors instead of relying on generalized, population-level outcome statistics.
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 | 0,030 | 0,062 |
| Méta-épidémiologie (sens strict) | 0,003 | 0,001 |
| Méta-épidémiologie (sens large) | 0,019 | 0,052 |
| Bibliométrie | 0,008 | 0,009 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,004 | 0,002 |
| Science ouverte | 0,002 | 0,002 |
| Intégrité de la recherche | 0,002 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 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 ».