Anatomical Determinants of Epilepsy Surgery Outcomes: A Systematic Review and Individual Patient Data Meta-Analysis
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.030 | 0.062 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.019 | 0.052 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".