Dataset Information

Predicting seizure freedom after epilepsy surgery, a challenge in clinical practice.

ABSTRACT:

Objective

The objective of this study was to compare the accuracy of clinical judgment in predicting seizure outcome after resective epilepsy surgery relative to two recently published statistical tools [the Epilepsy Surgery Nomogram (ESN) and the modified Seizure-Freedom score (m-SFS)].

Methods

Details of presurgical evaluations of 20 patients who underwent epilepsy surgery were presented to 20 epilepsy experts. The final surgical treatment was also disclosed. The clinicians were asked to predict the likelihood of a good outcome (Engel 1) at 2 and 5?years in each case. The ESN and the m-SFS predictions were calculated with the data provided to the clinicians. The discriminative ability of clinical judgment, ESN, and m-SFS was assessed by calculating a concordance index (C-index). Expert opinion, the m-SFS and the ESN performances were compared using a Receiver Operating Characteristic (ROC) curve analysis.

Results

The mean age at surgery was 29?years (standard deviation [SD]?=?14); 40% were male; 70% were right-handed, and thirteen (65%) had an Engel outcome 1 at 2 and 5?years. The mean C-index for the mean physician's prediction was 0.478 with a variance of 0.012. The ESN had an area under the curve (AUC) of 0.528 and 0.533 for the 2-year and 5-year predictions in comparison with the clinicians' predictions that was 0.476, and 0.466, respectively. For the m-SFS, the AUC at 2?years and 5?years was 0.539 and 0.539, respectively. No statistical difference was noted between the ESN and the clinicians or between m-SFS and the ESN, but there is a moderate statistical difference favoring the m-SFS to the clinicians (p 0.0960 and 0.0514, for 2 and 5?years).

Significance

Clinical judgment was not superior to the ESN nor to the m-SFS. Together with the interphysician's prediction variability, our findings reinforce the need for better tools to predict postoperative outcomes.

SUBMITTER: Gracia CG

PROVIDER: S-EPMC6546523 | biostudies-literature | 2019 Jun

REPOSITORIES: biostudies-literature

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Publications

Predicting seizure freedom after epilepsy surgery, a challenge in clinical practice.

Gracia Camilo Garcia CG Chagin Kevin K Kattan Michael W MW Ji Xinge X Kattan Madeleine G MG Crotty Lizzie L Najm Imad I Gonzalez-Martinez Jorge J Bingaman William W Jehi Lara L

Epilepsy & behavior : E&B 20190428

<h4>Objective</h4>The objective of this study was to compare the accuracy of clinical judgment in predicting seizure outcome after resective epilepsy surgery relative to two recently published statistical tools [the Epilepsy Surgery Nomogram (ESN) and the modified Seizure-Freedom score (m-SFS)].<h4>Methods</h4>Details of presurgical evaluations of 20 patients who underwent epilepsy surgery were presented to 20 epilepsy experts. The final surgical treatment was also disclosed. The clinicians were ...[more]

PMID: 31035104

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Project description:ObjectivesGliomas are often diagnosed due to epileptic seizures as well as neurocognitive deficits. First treatment choice for patients with gliomas in speech-related areas is awake surgery, which aims at maximizing tumor resection while preserving or improving patient's neurological status. The present study aimed at evaluating neurocognitive functioning and occurrence of epileptic seizures in patients suffering from gliomas located in language-related areas before and after awake surgery as well as during their follow up course of disease.Materials and methodsIn this prospective study we included patients who underwent awake surgery for glioma in the inferior frontal gyrus, superior temporal gyrus, or anterior temporal lobe. Preoperatively, as well as in the short-term (median 4.1 months, IQR 2.1-6.0) and long-term (median 18.3 months, IQR 12.3-36.6) postoperative course, neurocognitive functioning, neurologic status, the occurrence of epileptic seizures and number of antiepileptic drugs were recorded.ResultsBetween 09/2012 and 09/2019, a total of 27 glioma patients, aged 36.1 ± 11.8 years, were included. Tumor resection was complete in 15, subtotal in 6 and partial in 6 patients, respectively. While preoperatively impairment in at least one neurocognitive domain was found in 37.0% of patients, postoperatively, in the short-term, 36.4% of patients presented a significant deterioration in word fluency (p=0.009) and 34.8% of patients in executive functions (p=0.049). Over the long-term, scores improved to preoperative baseline levels. The number of patients with mood disturbances significantly declined from 66.7% to 34.8% after surgery (p=0.03). Regarding seizures, these were present in 18 (66.7%) patients prior to surgery. Postoperatively, 22 (81.5%) patients were treated with antiepileptic drugs with all patients presenting seizure-freedom.ConclusionsIn patients suffering from gliomas in eloquent areas, the combination of awake surgery, regular neurocognitive assessment - considering individual patients´ functional outcome and rehabilitation needs - and the individual adjustment of antiepileptic therapy results in excellent patient outcome in the long-term course.

Project description:Objective: Central nervous system infections (CNSIs), especially viral encephalitis and meningitis, are well-recognized causes of medically refractory epilepsy. Although surgery is an effective and durable intervention against these infections, the seizure control outcomes described in previous surgical series have been variable. Accordingly, it is not clear which variables are most valuable in predicting seizure control following surgery for CNSI. The aim of this meta-analysis was to identify the predictors of favorable surgical outcomes in CNSI-related epilepsy. Methods: The PubMed, EMBASE, Cochrane Library, WANGFANG, VIP, CBM, and CNKI databases were searched for studies according to the inclusion criteria. Prognostic factors, surgical outcomes, and patient characteristics were extracted. Heterogeneity was detected by the I2 and Q statistics. Results: Seventeen studies were included in our meta-analysis. Eight predictors of favorable outcomes (Engel Class I/II) were determined, including abnormal MRI findings, meningitis, temporal location only, regional ictal pattern, unilateral ictal pattern, older age at epilepsy, longer silent period, and longer time from infection, as follows: OR = 3.34 (95% CI 1.44-7.74), OR = 0.31 (95% CI 0.13-0.70), OR = 0.34 (95% CI 0.16-0.74), OR = 5.65 (95% CI 1.75-18.30), and OR = 9.53 (95% CI 2.36-38.48), respectively, and MD = 2.15 (95% CI 0.20-4.11), MD = 2.40 (95% CI 0.09-4.70), and MD = 8.49 (95% CI 1.50-15.48), respectively. A subgroup analysis found the following associations: regional and unilateral ictal patterns in viral encephalitis, a younger age at infection in parasitic encephalopathy, an older age at surgery, a longer time from onset, and a longer time from infection in unexplained meningitis. A sensitivity analysis restricted to studies that included each variable yielded robust results. Little evidence of publication bias was observed. Conclusions: This meta-analysis suggests that abnormal MRI findings, meningitis, temporal location only, regional and unilateral ictal patterns, older age at epilepsy, longer silent period, and longer time from infection are predictive factors in patients with favorable surgical outcomes in CNSI-related epilepsy. In addition, different infective agents influenced the results in regional and unilateral ictal patterns in ictal electroencephalography, as well as the relationship between age at infection and surgery and the time from epilepsy onset and infection.

Project description:ObjectiveThe accurate prediction of seizure freedom after epilepsy surgery remains challenging. We investigated if (1) training more complex models, (2) recruiting larger sample sizes, or (3) using data-driven selection of clinical predictors would improve our ability to predict postoperative seizure outcome using clinical features. We also conducted the first substantial external validation of a machine learning model trained to predict postoperative seizure outcome.MethodsWe performed a retrospective cohort study of 797 children who had undergone resective or disconnective epilepsy surgery at a tertiary center. We extracted patient information from medical records and trained three models-a logistic regression, a multilayer perceptron, and an XGBoost model-to predict 1-year postoperative seizure outcome on our data set. We evaluated the performance of a recently published XGBoost model on the same patients. We further investigated the impact of sample size on model performance, using learning curve analysis to estimate performance at samples up to N = 2000. Finally, we examined the impact of predictor selection on model performance.ResultsOur logistic regression achieved an accuracy of 72% (95% confidence interval [CI] = 68%-75%, area under the curve [AUC] = .72), whereas our multilayer perceptron and XGBoost both achieved accuracies of 71% (95% CIMLP = 67%-74%, AUCMLP = .70; 95% CIXGBoost own = 68%-75%, AUCXGBoost own = .70). There was no significant difference in performance between our three models (all p > .4) and they all performed better than the external XGBoost, which achieved an accuracy of 63% (95% CI = 59%-67%, AUC = .62; pLR = .005, pMLP = .01, pXGBoost own = .01) on our data. All models showed improved performance with increasing sample size, but limited improvements beyond our current sample. The best model performance was achieved with data-driven feature selection.SignificanceWe show that neither the deployment of complex machine learning models nor the assembly of thousands of patients alone is likely to generate significant improvements in our ability to predict postoperative seizure freedom. We instead propose that improved feature selection alongside collaboration, data standardization, and model sharing is required to advance the field.

Project description:ObjectiveSeizure outcomes after brain surgery for drug-resistant epilepsy (DRE) are very heterogeneous and difficult to predict with models utilizing the current clinical, imaging, and electrophysiological variables. In this pilot study, we investigated whether genetic and molecular biomarkers (e.g., genomic, transcriptomic) can provide additional insight into differential response to surgery.MethodsPost-operative seizure-outcomes were collected at last follow-up (>6 months) for 201 adult patients with DRE who underwent surgery between 2004 and 2020. Resected tissue was sent for miRNA sequencing (n = 132) and mRNA sequencing (n = 135). Following the selection of 10 genes (SCN1A, NBEA, PTEN, GABRA1, LGL1, DEPDC5, IL1A, ABCB1, C3, CALHM1), we investigated SNPs in those 10 genes from previously acquired exome sequencing data (n = 106). Logistic regression was performed to test for associations between individual features (mRNAs, miRNAs, and SNPs) and post-operative seizure-outcome with an exploratory FDR P < 0.25 as the threshold for significance. Post-operative time-to-seizure analyses were performed for each SNP using a Cox proportional hazards model.ResultsThe majority of patients (83%) had temporal lobe epilepsy. Mean age at surgery was 38.3 years, and 56% were female. Three SNPs (rs10276036, rs11975994, rs1128503) in multi-drug resistance gene, ABCB1, were associated with post-operative seizure outcomes. Patients with alternate alleles in ABCB1 were more likely to be seizure-free at last follow-up (52-56% reduction in seizure recurrence; FDR P = 0.24). All three SNPs were in linkage disequilibrium and highly correlated with each other. Median post-operative time-to-seizure was 63 months for patients with 2 alternate alleles, 24-33 months with 1 alternate allele, and 10-11 months with 0 alternate alleles. These SNPs improved outcome prediction beyond MRI and sex alone. No independent miRNAs or mRNAs were significantly associated with seizure-outcome (P > 0.05). However, pathway analysis identified "cancer drug resistance by drug efflux" (mir-154 and mir-379) as enriched (P = 0.02), supporting the role of drug response genes in post-operative seizure recurrence.SignificanceABCB1 may have a role in epileptogenesis and surgery outcomes independent of its drug efflux activity necessitating further investigation. SNPs in ABCB1 may serve as independent predictors of post-operative outcome.

Dataset Information

Predicting seizure freedom after epilepsy surgery, a challenge in clinical practice.

Objective

Methods

Results

Significance

Publications

Predicting seizure freedom after epilepsy surgery, a challenge in clinical practice.

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