Seizure freedom score: A new simple method to predict success of epilepsy surgery

A spatial perturbation framework to validate implantation of the epileptogenic zone

Stereo-electroencephalography (SEEG) is the gold standard to delineate surgical targets in focal drug-resistant epilepsy. SEEG uses electrodes placed directly into the brain to identify the seizure-onset zone (SOZ). However, its major constraint is limited brain coverage, potentially leading to misidentification of the 'true' SOZ. Here, we propose a framework to assess adequate SEEG sampling by coupling epileptic biomarkers with their spatial distribution and measuring the system's response to a perturbation of this coupling. We demonstrate that the system's response is strongest in well-sampled patients when virtually removing the measured SOZ. We then introduce the spatial perturbation map, a tool that enables qualitative assessment of the implantation coverage. Probability modelling reveals a higher likelihood of well-implanted SOZs in seizure-free patients or non-seizure free patients with incomplete SOZ resections, compared to non-seizure-free patients with complete resections. This highlights the framework's value in sparing patients from unsuccessful surgeries resulting from poor SEEG coverage.

The value of linear and non-linear quantitative EEG analysis in paediatric epilepsy surgery: a machine learning approach

Epilepsy surgery is effective for patients with medication-resistant seizures, however 20-40% of them are not seizure free after surgery. Aim of this study is to evaluate the role of linear and non-linear EEG features to predict post-surgical outcome. We included 123 paediatric patients who underwent epilepsy surgery at Bambino Gesù Children Hospital (January 2009-April 2020). All patients had long term video-EEG monitoring. We analysed 1-min scalp interictal EEG (wakefulness and sleep) and extracted 13 linear and non-linear EEG features (power spectral density (PSD), Hjorth, approximate entropy, permutation entropy, Lyapunov and Hurst value). We used a logistic regression (LR) as feature selection process. To quantify the correlation between EEG features and surgical outcome we used an artificial neural network (ANN) model with 18 architectures. LR revealed a significant correlation between PSD of alpha band (sleep), Mobility index (sleep) and the Hurst value (sleep and awake) with outcome. The fifty-four ANN models gave a range of accuracy (46-65%) in predicting outcome. Within the fifty-four ANN models, we found a higher accuracy (64.8% ± 7.6%) in seizure outcome prediction, using features selected by LR. The combination of PSD of alpha band, mobility and the Hurst value positively correlate with good surgical outcome.

Specific consistency score for rational selection of epilepsy resection surgery candidates

OBJECTIVE

Degree of indication for epilepsy surgery is determined by taking multiple factors into account. This study aimed to investigate the usefulness of the Specific Consistency Score (SCS), a proposed score for focal epilepsy to rate the indication for epilepsy focal resection.

METHODS

This retrospective cohort study included patients considered for resective epilepsy surgery in Kyoto University Hospital from 2011 to 2022. Plausible epileptic focus was tentatively defined. Cardinal findings were scored based on specificity and consistency with the estimated laterality and lobe. The total points represented SCS. The association between SCS and the following clinical parameters was assessed by univariate and multivariate analysis: (1) probability of undergoing resective epilepsy surgery, (2) good postoperative seizure outcome (Engel I and II or Engel I only), and (3) lobar concordance between the noninvasively estimated focus and intracranial electroencephalographic (EEG) recordings.

RESULTS

A total of 131 patients were evaluated. Univariate analysis revealed higher SCS in the (1) epilepsy surgery group (8.4 [95% confidence interval (CI) = 7.8-8.9] vs. 4.9 [95% CI = 4.3-5.5] points; p < .001), (2) good postoperative seizure outcome group (Engel I and II; 8.7 [95% CI = 8.2-9.3] vs. 6.4 [95% CI = 4.5-8.3] points; p = .008), and (3) patients whose focus defined by intracranial EEG matched the noninvasively estimated focus (8.3 [95% CI = 7.3-9.2] vs. 5.4 [95% CI = 3.5-7.3] points; p = .004). Multivariate analysis revealed areas under the curve of .843, .825, and .881 for Parameters 1, 2, and 3, respectively.

SIGNIFICANCE

SCS provides a reliable index of good indication for resective epilepsy surgery and can be easily available in many institutions not necessarily specializing in epilepsy.

Prediction tools and risk stratification in epilepsy surgery

OBJECTIVE

This study was undertaken to conduct external validation of previously published epilepsy surgery prediction tools using a large independent multicenter dataset and to assess whether these tools can stratify patients for being operated on and for becoming free of disabling seizures (International League Against Epilepsy stage 1 and 2).

METHODS

We analyzed a dataset of 1562 patients, not used for tool development. We applied two scales: Epilepsy Surgery Grading Scale (ESGS) and Seizure Freedom Score (SFS); and two versions of Epilepsy Surgery Nomogram (ESN): the original version and the modified version, which included electroencephalographic data. For the ESNs, we used calibration curves and concordance indexes. We stratified the patients into three tiers for assessing the chances of attaining freedom from disabling seizures after surgery: high (ESGS = 1, SFS = 3-4, ESNs > 70%), moderate (ESGS = 2, SFS = 2, ESNs = 40%-70%), and low (ESGS = 2, SFS = 0-1, ESNs < 40%). We compared the three tiers as stratified by these tools, concerning the proportion of patients who were operated on, and for the proportion of patients who became free of disabling seizures.

RESULTS

The concordance indexes for the various versions of the nomograms were between .56 and .69. Both scales (ESGS, SFS) and nomograms accurately stratified the patients for becoming free of disabling seizures, with significant differences among the three tiers (p < .05). In addition, ESGS and the modified ESN accurately stratified the patients for having been offered surgery, with significant difference among the three tiers (p < .05).

SIGNIFICANCE

ESGS and the modified ESN (at thresholds of 40% and 70%) stratify patients undergoing presurgical evaluation into three tiers, with high, moderate, and low chance for favorable outcome, with significant differences between the groups concerning having surgery and becoming free of disabling seizures. Stratifying patients for epilepsy surgery has the potential to help select the optimal candidates in underprivileged areas and better allocate resources in developed countries.

Implantation and reimplantation of intracranial EEG electrodes in patients considering epilepsy surgery

In patients with drug-resistant epilepsy who are considering surgery, intracranial EEG (iEEG) helps delineate the putative epileptogenic zone. In a minority of patients, iEEG fails to identify seizure onsets. In such cases, it might be worthwhile to reimplant more iEEG electrodes. The consequences of such a strategy for the patient are unknown. We matched 12 patients in whom the initially implanted iEEG electrodes did not delineate the seizure onset zone precisely enough to offer resective surgery, and in whom additional iEEG electrodes were implanted during the same inpatient stay, to controls who did not undergo reimplantation. Seven cases and eight controls proceeded to resective surgery. No intracranial infection occurred. One control suffered an intracranial hemorrhage. Three cases and two controls suffered from a post-operative neurological or neuropsychological deficit. We found no difference in post-operative seizure control between cases and controls. Compared to an ILAE score of 5 (ie, stable seizure frequency in the absence of resective surgery), cases showed significant improvement. Reimplantation of iEEG electrodes can offer the possibility of resective epilepsy surgery to patients in whom the initial iEEG investigation was inconclusive, without compromising on the risk of complications or seizure control.

Predicting seizure outcome after epilepsy surgery: Do we need more complex models, larger samples, or better data?

OBJECTIVE

The 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.

METHODS

We 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.

RESULTS

Our 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.

SIGNIFICANCE

We 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.

Short and long term prediction of seizure freedom in drug-resistant focal epilepsy surgery

BACKGROUND

The selection of candidates for drug-resistant focal epilepsy surgery is essential to achieve the best post-surgical outcomes.

OBJECTIVE

To develop two prediction models for seizure freedom in the short and long-term follow-up and from them to create a risk calculator in order to individualize the selection of candidates for surgery and future therapies in each patients.

METHODS

A sample of 64 consecutive patients who underwent epilepsy surgery at two Cuban tertiary health institutions between 2012 and 2020 constituted the basis for the prediction models. Two models were obtained through the novel methodology, based on biomarker selection reached by resampling methods, cross-validation and high-accuracy index measured through the area under the receiving operating curve (ROC) procedure.

RESULTS

The first, to pre-operative model included five predictors: epilepsy type, seizures per month, ictal pattern, interictal EEG topography and normal or abnormal magnetic resonance imaging,. it's precision was 0.77 at one year, and with four years and more 0.63. The second model including variables from the trans-surgical and post-surgical stages: the interictal discharges in the post-surgical EEG, incomplete or complete resection of the epileptogenic zone, the surgical techniques employed and disappearance of the discharge in post-resection electrocorticography; the precision of this model was 0.82 at one year, and with four years and more 0.97.

CONCLUSIONS

The introduction of trans-surgical and post-surgical variables increase the prediction of the pre-surgical model. A risk calculator was developed using these prediction models, which could be useful as an accurate tool to improve the prediction in epilepsy surgery.

Identifying important factors for successful surgery in patients with lateral temporal lobe epilepsy

OBJECTIVE

Lateral temporal lobe epilepsy (LTLE) has been diagnosed in only a small number of patients; therefore, its surgical outcome is not as well-known as that of mesial temporal lobe epilepsy. We aimed to evaluate the long-term (5 years) and short-term (2 years) surgical outcomes and identify possible prognostic factors in patients with LTLE.

METHODS

This retrospective cohort study was conducted between January 1995 and December 2018 among patients who underwent resective surgery in a university-affiliated hospital. Patients were classified as LTLE if ictal onset zone was in lateral temporal area. Surgical outcomes were evaluated at 2 and 5 years. We subdivided based on outcomes and compared clinical and neuroimaging data including cortical thickness between two groups.

RESULTS

Sixty-four patients were included in the study. The mean follow-up duration after the surgery was 8.4 years. Five years after surgery, 45 of the 63 (71.4%) patients achieved seizure freedom. Clinically and statistically significant prognostic factors for postsurgical outcomes were the duration of epilepsy before surgery and focal cortical dysplasia on postoperative histopathology at the 5-year follow-up. Optimal cut-off point for epilepsy duration was eight years after the seizure onset (odds ratio 4.375, p-value = 0.0214). Furthermore, we propose a model for predicting seizure outcomes 5 years after surgery using the receiver operating characteristic curve and nomogram (area under the curve = 0.733; 95% confidence interval, 0.588-0.879). Cortical thinning was observed in ipsilateral cingulate gyrus and contralateral parietal lobe in poor surgical group compared to good surgical group (p-value < 0.01, uncorrected).

CONCLUSIONS

The identified predictors of unfavorable surgical outcomes may help in selecting optimal candidates and identifying the optimal timing for surgery among patients with LTLE. Additionally, cortical thinning was more extensive in the poor surgical group.

New insights for predicting surgery outcome in patients with temporal lobe epilepsy. A systematic review

Resective surgery is the treatment of choice for one-third of adult patients with focal, drug-resistant epilepsy. This procedure is associated with substantial clinical and cognitive risks. In clinical practice, there is no validated model for epilepsy surgery outcome prediction (ESOP). Meta-analyses on ESOP studies assessing prognostic factors report discrepancies in terms of study design. Our review aims to systematically investigate methodological and analytical aspects of studies predicting clinical and cognitive outcomes after temporal lobe epilepsy surgery. A systematic review of ESOP studies published between 2000 and 2022 from three databases (MEDLINE, Web of Science, and PsycINFO) was completed by following the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) guidelines. It yielded 4867 articles. Among them, 21 corresponded to our inclusion criteria and were therefore retained in the final review. The risk of bias was assessed using A Tool to Assess Risk of Bias and Applicability of Prediction Model Studies (PROBAST). Data extracted from the 21 studies were analyzed using narrative synthesis and descriptive statistics. Our findings show an increase in the use of multimodal datasets and machine learning analyses in recent ESOP studies, although regression remained the most frequently used approach. We also identified a more frequent use of network notions in recent ESOP studies. Nevertheless, several methodological issues were noted, such as small sample sizes, lack of information on the follow-up period, variability in seizure outcome, and the definition of neuropsychological postoperative change. Of 21 studies, only one provided a clinical tool to anticipate the cognitive outcome after epilepsy surgery. We conclude that methodological issues should be overcome before we move towards more complete models to better predict clinical and cognitive outcomes after epilepsy surgery. Recommendations for future studies to harness the possibilities of multimodal datasets and data fusion, are provided. A stronger bridge between fundamental and clinical research may result in developing accessible clinical tools.

Beyond seizure freedom: Dissecting long-term seizure control after surgical resection for drug-resistant epilepsy

OBJECTIVE

This study was undertaken to better understand the long-term palliative and disease-modifying effects of surgical resection beyond seizure freedom, including frequency reduction and both late recurrence and remission, in patients with drug-resistant epilepsy.

METHODS

This retrospective database-driven cohort study included all patients with >9 years of follow-up at a single high-volume epilepsy center. We included patients who underwent lobectomy, multilobar resection, or lesionectomies for drug-resistant epilepsy; we excluded patients who underwent hemispherectomies. Our main outcomes were (1) reduction in frequency of disabling seizures (at 6 months, each year up to 9 years postoperatively, and at last follow-up), (2) achievement of seizure remission (>6 months, >1 year, and longest duration), and (3) seizure freedom at last follow-up.

RESULTS

We included 251 patients; 234 (93.2%) achieved 6 months and 232 (92.4%) experienced 1 year of seizure freedom. Of these, the average period of seizure freedom was 10.3 years. A total of 182 (72.5%) patients were seizure-free at last follow-up (defined as >1 year without seizures), with a median 11.9 years since remission. For patients not completely seizure-free, the mean seizure frequency reduction at each time point was 76.2%, and ranged from 66.6% to 85.0%. Patients decreased their number of antiseizure medications on average by .58, and 53 (21.2%) patients were on no antiseizure medication at last follow-up. Nearly half (47.1%) of those seizure-free at last follow-up were not seizure-free immediately postoperatively.

SIGNIFICANCE

Patients who continue to have seizures after resection often have considerable reductions in seizure frequency, and many are able to achieve seizure freedom in a delayed manner.

Factors Predicting Outcome After Intracranial EEG Evaluation in Patients With Medically Refractory Epilepsy

BACKGROUND AND OBJECTIVES

The aim of this study was to identify predictors of a resective surgery and subsequent seizure freedom following intracranial EEG (ICEEG) for seizure-onset localization.

METHODS

This is a retrospective chart review of 178 consecutive patients with medically refractory epilepsy who underwent ICEEG monitoring from 2002 to 2015. Univariable and multivariable regression analysis identified independent predictors of resection vs other options. Stepwise Akaike information criteria with the aid of clinical consideration were used to select the best multivariable model for predicting resection and outcome. Discrete time survival analysis was used to analyze the factors predicting seizure-free outcome. Cumulative probability of seizure freedom was analyzed using Kaplan-Meier curves and compared between resection and nonresection groups. Additional univariate analysis was performed on 8 select clinical scenarios commonly encountered during epilepsy surgical evaluations.

RESULTS

Multivariable analysis identified the presence of a lesional MRI, presurgical hypothesis suggesting temporal lobe onset, and a nondominant hemisphere implant as independent predictors of resection (p < 0.0001, area under the receiver operating characteristic curve 0.80, 95% CI 0.73-0.87). Focal ICEEG onset and undergoing a resective surgery predicted absolute seizure freedom at the 5-year follow-up. Patients who underwent resective surgery were more likely to be seizure-free at 5 years compared with continued medical treatment or neuromodulation (60% vs 7%; p < 0.0001, hazard ratio 0.16, 95% CI 0.09-0.28). Even patients thought to have unfavorable predictors (nonlesional MRI or extratemporal lobe hypothesis or dominant hemisphere implant) had ≥50% chance of seizure freedom at 5 years if they underwent resection.

DISCUSSION

Unfavorable predictors, including having nonlesional extratemporal epilepsy, should not deter a thorough presurgical evaluation, including with invasive recordings in many cases. Resective surgery without functional impairment offers the best chance for sustained seizure freedom and should always be considered first.

CLASSIFICATION OF EVIDENCE

This study provides Class II evidence that the presence of a lesional MRI, presurgical hypothesis suggesting temporal lobe onset, and a nondominant hemisphere implant are independent predictors of resection. Focal ICEEG onset and undergoing resection are independent predictors of 5-year seizure freedom.

Development and validation of machine learning models for prediction of seizure outcome after pediatric epilepsy surgery

OBJECTIVE

There is substantial variability in reported seizure outcome following pediatric epilepsy surgery, and lack of individualized predictive tools that could evaluate the probability of seizure freedom postsurgery. The aim of this study was to develop and validate a supervised machine learning (ML) model for predicting seizure freedom after pediatric epilepsy surgery.

METHODS

This is a multicenter retrospective study of children who underwent epilepsy surgery at five pediatric epilepsy centers in North America. Clinical information, diagnostic investigations, and surgical characteristics were collected, and used as features to predict seizure-free outcome 1 year after surgery. The dataset was split randomly into 80% training and 20% testing data. Thirty-five combinations of five feature sets with seven ML classifiers were assessed on the training cohort using 10-fold cross-validation for model development. The performance of the optimal combination of ML classifier and feature set was evaluated in the testing cohort, and compared with logistic regression, a classical statistical approach.

RESULTS

Of the 801 patients included, 61.3% were seizure-free 1 year postsurgery. During model development, the best combination was XGBoost ML algorithm with five features from the univariate feature set, including number of antiseizure medications, magnetic resonance imaging lesion, age at seizure onset, video-electroencephalography concordance, and surgery type, with a mean area under the curve (AUC) of .73 (95% confidence interval [CI] = .69-.77). The combination of XGBoost and univariate feature set was then evaluated on the testing cohort and achieved an AUC of .74 (95% CI = .66-.82; sensitivity = .87, 95% CI = .81-.94; specificity = .58, 95% CI = .47-.71). The XGBoost model outperformed the logistic regression model (AUC = .72, 95% CI = .63-.80; sensitivity = .72, 95% CI = .63-.82; specificity = .66, 95% CI = .53-.77) in the testing cohort (p = .005).

SIGNIFICANCE

This study identified important features and validated an ML algorithm, XGBoost, for predicting the probability of seizure freedom after pediatric epilepsy surgery. Improved prognostication of epilepsy surgery is critical for presurgical counseling and will inform treatment decisions.

Presurgical temporal lobe epilepsy connectome fingerprint for seizure outcome prediction

Temporal lobe epilepsy presents a unique situation where confident clinical localization of the seizure focus does not always result in a seizure-free or favourable outcome after mesial temporal surgery. In this work, magnetic resonance imaging derived functional and structural whole-brain connectivity was used to compute a network fingerprint that captures the connectivity profile characteristics that are common across a group of nine of these patients with seizure-free outcome. The connectivity profile was then computed for 38 left-out patients with the hypothesis that similarity to the fingerprint indicates seizure-free surgical outcome. Patient profile distance to the fingerprint was compared with 1-year seizure outcome and standard clinical parameters. Distance to the fingerprint was higher for patients with Engel III-IV 1-year outcome compared with those with Engel Ia, Ib-d, and II outcome (Kruskal-Wallis, P < 0.01; Wilcoxon rank-sum p _corr <0.05 Bonferroni-corrected). Receiver operator characteristic analysis revealed 100% sensitivity and 90% specificity in identifying patients with Engel III-IV outcome based on distance to the fingerprint in the left-out patients. Furthermore, distance to the fingerprint was not related to any individual clinical parameter including age at scan, duration of disease, total seizure frequency, presence of mesial temporal sclerosis, lateralizing ictal, interictal scalp electroencephalography, invasive stereo-encephalography, or positron emission tomography. And two published algorithms utilizing multiple clinical measures for predicting seizure outcome were not related to distance to the fingerprint, nor predictive of seizure outcome in this cohort. The functional and structural connectome fingerprint provides quantitative, clinically interpretable and significant information not captured by standard clinical assessments alone or in combinations. This automated and simple method may improve patient-specific prediction of seizure outcome in patients with a clinically identified focus in the mesial temporal lobe.

Atlas of lesion locations and postsurgical seizure freedom in focal cortical dysplasia: A MELD study

OBJECTIVE

Drug-resistant focal epilepsy is often caused by focal cortical dysplasias (FCDs). The distribution of these lesions across the cerebral cortex and the impact of lesion location on clinical presentation and surgical outcome are largely unknown. We created a neuroimaging cohort of patients with individually mapped FCDs to determine factors associated with lesion location and predictors of postsurgical outcome.

METHODS

The MELD (Multi-centre Epilepsy Lesion Detection) project collated a retrospective cohort of 580 patients with epilepsy attributed to FCD from 20 epilepsy centers worldwide. Magnetic resonance imaging-based maps of individual FCDs with accompanying demographic, clinical, and surgical information were collected. We mapped the distribution of FCDs, examined for associations between clinical factors and lesion location, and developed a predictive model of postsurgical seizure freedom.

RESULTS

FCDs were nonuniformly distributed, concentrating in the superior frontal sulcus, frontal pole, and temporal pole. Epilepsy onset was typically before the age of 10 years. Earlier epilepsy onset was associated with lesions in primary sensory areas, whereas later epilepsy onset was associated with lesions in association cortices. Lesions in temporal and occipital lobes tended to be larger than frontal lobe lesions. Seizure freedom rates varied with FCD location, from around 30% in visual, motor, and premotor areas to 75% in superior temporal and frontal gyri. The predictive model of postsurgical seizure freedom had a positive predictive value of 70% and negative predictive value of 61%.

SIGNIFICANCE

FCD location is an important determinant of its size, the age at epilepsy onset, and the likelihood of seizure freedom postsurgery. Our atlas of lesion locations can be used to guide the radiological search for subtle lesions in individual patients. Our atlas of regional seizure freedom rates and associated predictive model can be used to estimate individual likelihoods of postsurgical seizure freedom. Data-driven atlases and predictive models are essential for evidence-based, precision medicine and risk counseling in epilepsy.

Surgical evaluation in children <3 years of age with drug-resistant epilepsy: Patient characteristics, diagnostic utilization, and potential for treatment delays

OBJECTIVE

Drug-resistant epilepsy (DRE) occurs at higher rates in children <3 years old. Epilepsy surgery is effective, but rarely utilized in young children despite developmental benefits of early seizure freedom. The present study aims to identify unique patient characteristics and evaluation strategies in children <3 years old who undergo epilepsy surgery evaluation as a means to assess contributors and potential solutions to health care disparities in this group.

METHODS

The Pediatric Epilepsy Research Consortium Epilepsy Surgery Database, a multicentered, cross-sectional collaboration of 21 US pediatric epilepsy centers, collects prospective data on children <18 years of age referred for epilepsy surgery evaluation. We compared patient characteristics, diagnostic utilization, and surgical treatment between children <3 years old and those older undergoing initial presurgical evaluation. We evaluated patient characteristics leading to delayed referral (>1 year) after DRE diagnosis in the very young.

RESULTS

The cohort included 437 children, of whom 71 (16%) were <3 years of age at referral. Children evaluated before the age of 3 years more commonly had abnormal neurological examinations (p = .002) and daily seizures (p = .001). At least one ancillary test was used in 44% of evaluations. Fifty-nine percent were seizure-free following surgery (n = 34), with 35% undergoing limited focal resections. Children with delayed referrals more often had focal aware (p < .001) seizures and recommendation for palliative surgeries (p < .001).

SIGNIFICANCE

There are relatively few studies of epilepsy surgery in the very young. Surgery is effective, but may be disproportionally offered to those with severe presentations. Relatively low utilization of ancillary testing may contribute to reduced surgical therapy for those without evident lesions on magnetic resonance imaging. Despite this, a sizeable portion of patients have favorable outcome after focal epilepsy surgery resections.

Automated Identification of Surgical Candidates and Estimation of Postoperative Seizure Freedom in Children - A Focused Review

Surgery is an effective but underused treatment for drug-resistant epilepsy in children. Algorithms to identify surgical candidates and estimate the likelihood of postoperative clinical improvement may be valuable to improve access to epilepsy surgery. We provide a focused review of these approaches. For adults with epilepsy, tools to identify surgical candidates and predict seizure and cognitive outcomes (Ie, Cases for Epilepsy (toolsforepilepsy.com) and Epilepsy Surgery Grading Scale) have been validated and are in use. Analogous tools for children need development. A promising approach is to apply statistical learning tools to clinical datasets, such as electroencephalogram tracings, imaging studies, and the text of clinician notes. Demonstration projects suggest these techniques have the potential to be highly accurate, and await further validation and clinical application.

Blinded study: prospectively defined high-frequency oscillations predict seizure outcome in individual patients

Interictal high-frequency oscillations are discussed as biomarkers for epileptogenic brain tissue that should be resected in epilepsy surgery to achieve seizure freedom. The prospective classification of tissue sampled by individual electrode contacts remains a challenge. We have developed an automated, prospective definition of clinically relevant high-frequency oscillations in intracranial EEG from Montreal and tested it in recordings from Zurich. We here validated the algorithm on intracranial EEG that was recorded in an independent epilepsy centre so that the analysis was blinded to seizure outcome. We selected consecutive patients who underwent resective epilepsy surgery in Geneva with post-surgical follow-up > 12 months. We analysed long-term recordings during sleep that we segmented into intervals of 5 min. High-frequency oscillations were defined in the ripple (80–250 Hz) and the fast ripple (250–500 Hz) frequency bands. Contacts with the highest rate of ripples co-occurring with fast ripples designated the relevant area. As a validity criterion, we calculated the test–retest reliability of the high-frequency oscillations area between the 5 min intervals (dwell time ≥50%). If the area was not fully resected and the patient suffered from recurrent seizures, this was classified as a true positive prediction. We included recordings from 16 patients (median age 32 years, range 18–53 years) with stereotactic depth electrodes and/or with subdural electrode grids (median follow-up 27 months, range 12–55 months). For each patient, we included several 5 min intervals (median 17 intervals). The relevant area had high test–retest reliability across intervals (median dwell time 95%). In two patients, the test–retest reliability was too low (dwell time < 50%) so that outcome prediction was not possible. The area was fully included in the resected volume in 2/4 patients who achieved post-operative seizure freedom (specificity 50%) and was not fully included in 9/10 patients with recurrent seizures (sensitivity 90%), leading to an accuracy of 79%. An additional exploratory analysis suggested that high-frequency oscillations were associated with interictal epileptic discharges only in channels within the relevant area and not associated in channels outside the area. We thereby validated the automated procedure to delineate the clinically relevant area in each individual patient of an independently recorded dataset and achieved the same good accuracy as in our previous studies. The reproducibility of our results across datasets is promising for a multicentre study to test the clinical application of high-frequency oscillations to guide epilepsy surgery.

Healthcare professionals' knowledge, attitude, and perception of epilepsy surgery: A systematic review

OBJECTIVE

The epilepsy surgery treatment gap is well defined and secondary to a broad range of issues, including healthcare professionals' (HCPs') knowledge, attitude, and perception (KAP) toward epilepsy surgery. However, no previous systematic reviews investigated this important topic.

METHODS

The systematic review was conducted according to Preferred Reporting Items for the Systematic Reviews and Meta-Analyses (PRISMA) guidelines. We identified a total of 652 articles from multiple databases using database-specific queries and included 65 articles for full-text review after screening the titles and abstracts of the articles. Finally, we selected 11 papers for qualitative analysis. We critically appraised the quality of the studies using the Joanna Briggs critical appraisal tool.

RESULTS

The qualitative analysis of the content identified several key reasons causing healthcare professional-related barriers to epilepsy surgery: inadequate knowledge and awareness about the role of epilepsy surgery in drug-resistant epilepsy (DRE), poor identification and referral of patients with DRE, insufficient selection of candidates for presurgical workup, negative or ambivalent attitudes and perceptions regarding epilepsy surgery, deficient communication practices with patients regarding risk-benefit analysis of epilepsy surgery, and challenging coordination issues with the surgical referral. Neurologists with formal instruction in epilepsy, surgical exposure during training, participation in high volume epilepsy practice, or prior experience in surgical referral may refer more patients for surgical evaluation.

CONCLUSIONS

While significant work has been conducted in a limited number of studies to explore HCPs' knowledge gap and educational need regarding epilepsy surgery, further research is needed in defining the learning goals, assessing and validating specific learning gaps among providers, defining the learning outcomes, optimizing the educational format, content, and outcome measures, and appraising the achieved results following the educational intervention.

Patient specific prediction of temporal lobe epilepsy surgical outcomes

OBJECTIVE

Drug-resistant temporal lobe epilepsy (TLE) is the most common type of epilepsy for which patients undergo surgery. Despite the best clinical judgment and currently available prediction algorithms, surgical outcomes remain variable. We aimed to build and to evaluate the performance of multidimensional Bayesian network classifiers (MBCs), a type of probabilistic graphical model, at predicting probability of seizure freedom after TLE surgery.

METHODS

Clinical, neurophysiological, and imaging variables were collected from 231 TLE patients who underwent surgery at the University of California, San Francisco (UCSF) or the Montreal Neurological Institute (MNI) over a 15-year period. Postsurgical Engel outcomes at year 1 (Y1), Y2, and Y5 were analyzed as primary end points. We trained an MBC model on combined data sets from both institutions. Bootstrap bias corrected cross-validation (BBC-CV) was used to evaluate the performance of the models.

RESULTS

The MBC was compared with logistic regression and Cox proportional hazards according to the area under the receiver-operating characteristic curve (AUC). The MBC achieved an AUC of 0.67 at Y1, 0.72 at Y2, and 0.67 at Y5, which indicates modest performance yet superior to what has been reported in the state-of-the-art studies to date.

SIGNIFICANCE

The MBC can more precisely encode probabilistic relationships between predictors and class variables (Engel outcomes), achieving promising experimental results compared to other well-known statistical methods. Multisite application of the MBC could further optimize its classification accuracy with prospective data sets. Online access to the MBC is provided, paving the way for its use as an adjunct clinical tool in aiding pre-operative TLE surgical counseling.

Predicting epilepsy surgery outcome in adult patients: May psychiatric diagnosis improve predictive models?

OBJECTIVE

With this study, we aimed to assess the importance of including psychiatric disorders in a comprehensive prediction model for epilepsy surgery.

METHODS

Ambispective observational study with a sample of adults who underwent resective surgery. Participants were evaluated, before and one year after surgery, to collect data regarding their neurological and psychiatric history. The one-year post-surgical outcome was classified according to the Engel Outcome Scale. Previously identified predictors of post-surgical Engel Class were included in a logistic regression model. Then, the accuracy of alternative predictive models, including or excluding, past and current psychiatric diagnoses, were tried.

RESULTS

One hundred and forty-six people participated in this study. The inclusion of psychiatric diagnosis resulted in a model with a higher AUC curve, however, the Delong method showed no significant statistical differences between the models.

SIGNIFICANCE

Despite the fact that presurgical psychiatric diagnoses have shown to contribute to the prediction of epilepsy surgery outcome they do not contribute to a significant improvement of predictive models.

MRI network progression in mesial temporal lobe epilepsy related to healthy brain architecture

We measured MRI network progression in mesial temporal lobe epilepsy (mTLE) patients as a function of healthy brain architecture. Resting-state functional MRI and diffusion-weighted MRI were acquired in 40 unilateral mTLE patients and 70 healthy controls. Data were used to construct region-to-region functional connectivity, structural connectivity, and streamline length connectomes per subject. Three models of distance from the presumed seizure focus in the anterior hippocampus in the healthy brain were computed using the average connectome across controls. A fourth model was defined using regions of transmodal (higher cognitive function) to unimodal (perceptual) networks across a published functional gradient in the healthy brain. These models were used to test whether network progression in patients increased when distance from the anterior hippocampus or along a functional gradient in the healthy brain decreases. Results showed that alterations of structural and functional networks in mTLE occur in greater magnitude in regions of the brain closer to the seizure focus based on healthy brain topology, and decrease as distance from the focus increases over duration of disease. Overall, this work provides evidence that changes across the brain in focal epilepsy occur along healthy brain architecture.

Underutilization of epilepsy surgery: Part II: Strategies to overcome barriers

Interventions focused on utilization of epilepsy surgery can be divided into groups: those that improve patients' access to surgical evaluation and those that facilitate completion of the surgical evaluation and treatment. Educational intervention, technological innovation, and effective coordination and communication can significantly improve patients' access to surgery. Patient and public facing, individualized (analog and/or digital) communication can raise awareness and acceptance of epilepsy surgery. Educational interventions aimed at providers may mitigate knowledge gaps using practical and concise consensus statements and guidelines, while specific training can improve awareness around implicit bias. Innovative technology, such as clinical decision-making toolkits within the electronic medical record (EMR), machine learning techniques, online decision-support tools, nomograms, and scoring algorithms can facilitate timely identification of appropriate candidates for epilepsy surgery with individualized guidance regarding referral appropriateness, postoperative seizure freedom rate, and risks of complication after surgery. There are specific strategies applicable for epilepsy centers' success: building a multidisciplinary setup, maintaining/tracking volume and complexity of cases, collaborating with other centers, improving surgical outcome with reduced complications, utilizing advanced diagnostics tools, and considering minimally invasive surgical techniques. Established centers may use other strategies, such as multi-stage procedures for multifocal epilepsy, advanced functional mapping with tailored surgery for epilepsy involving the eloquent cortex, and generation of fresh hypotheses in cases of surgical failure. Finally, improved access to epilepsy surgery can be accomplished with policy changes (e.g., anti-discrimination policy, exemption in transportation cost, telehealth reimbursement policy, patient-centered epilepsy care models, pay-per-performance models, affordability and access to insurance, and increased funding for research). Every intervention should receive regular evaluation and feedback-driven modification to ensure appropriate utilization of epilepsy surgery.

An exploratory study into the influence of laterality and location of hippocampal sclerosis on seizure prognosis and global cortical thinning

In mesial temporal lobe epilepsy (mTLE), the correlation between disease duration, seizure laterality, and rostro-caudal location of hippocampal sclerosis has not been examined in the context of seizure severity and global cortical thinning. In this retrospective study, we analyzed structural 3 T MRI from 35 mTLE subjects. Regions of FLAIR hyperintensity (as an indicator of sclerosis)—based on 2D coronal FLAIR sequences—in the hippocampus were manually segmented, independently and in duplicate; degree of segmentation agreement was confirmed using the DICE index. Segmented lesions were used for separate analyses. First, the correlation of cortical thickness with disease duration and seizure focus laterality was explored using linear model regression. Then, the relationship between the rostro-caudal location of the FLAIR hyperintense signal and seizure severity, based on the Cleveland Clinic seizure freedom score (ccSFS), was explored using probabilistic voxel-wise mapping and functional connectivity analysis from normative data. The mean DICE Index was 0.71 (range 0.60–0.81). A significant correlation between duration of epilepsy and decreased mean whole brain cortical thickness was identified, regardless of seizure laterality (p < 0.05). The slope of cortical volume loss over time, however, was greater in subjects with right seizure focus. Based on probabilistic voxel-wise mapping, FLAIR hyperintensity in the posterior hippocampus was significantly associated with lower ccSFS scores (greater seizure severity). Finally, the right hippocampus was found to have greater brain-wide connectivity, compared to the left side, based on normative connectomic data. We have demonstrated a significant correlation between duration of epilepsy and right-sided seizure focus with global cortical thinning, potentially due to greater brain-wide connectivity. Sclerosis along the posterior hippocampus was associated with greater seizure severity, potentially serving as an important biomarker of seizure outcome after surgery.

Relative contribution of individual versus combined functional imaging studies in predicting seizure freedom in pediatric epilepsy surgery: an area under the curve analysis

OBJECTIVE

The goal of this study was to evaluate the predictive value and relative contribution of noninvasive presurgical functional imaging modalities based on the authors' institutional experience in pursuing seizure-free surgical outcomes in children with medically refractory epilepsy.

METHODS

This was a retrospective, single-institution, observational cohort study of pediatric patients who underwent evaluation and surgical treatment for medically refractory partial epilepsy between December 2003 and June 2016. During this interval, 108 children with medically refractory partial epilepsy underwent evaluation for localization and resective epilepsy surgery. Different noninvasive functional imaging modalities, including ictal SPECT, FDG-PET, and magnetoencephalography-magnetic source imaging, were utilized to augment a standardized paradigm (electroencephalography/semiology, MRI, and neuropsychology findings) for localization. Outcomes were evaluated at a minimum of 2 years (mean 7.5 years) utilizing area under the receiver operating characteristic curve analysis. Localizing modalities and other clinical covariates were examined in relation to long-term surgical outcomes.

RESULTS

There was variation in the contribution of each test, and no single presurgical workup modality could singularly and reliably predict a seizure-free outcome. However, concordance of presurgical modalities yielded a high predictive value. No difference in long-term outcomes between inconclusive (normal or diffusely abnormal) and abnormal focal MRI results were found. Long-term survival analyses revealed a statistically significant association between seizure freedom and patients with focal ictal EEG, early surgical intervention, and no history of generalized convulsions.

CONCLUSIONS

Comprehensive preoperative evaluation utilizing multiple noninvasive functional imaging modalities is not redundant and can improve pediatric epilepsy surgical outcomes.

Seizure outcome and use of antiepileptic drugs after epilepsy surgery according to histopathological diagnosis: a retrospective multicentre cohort study

BACKGROUND

Surgery is a widely accepted treatment option for drug-resistant focal epilepsy. A detailed analysis of longitudinal postoperative seizure outcomes and use of antiepileptic drugs for different brain lesions causing epilepsy is not available. We aimed to analyse the association between histopathology and seizure outcome and drug freedom up to 5 years after epilepsy surgery, to improve presurgical decision making and counselling.

METHODS

In this retrospective, multicentre, longitudinal, cohort study, patients who had epilepsy surgery between Jan 1, 2000, and Dec 31, 2012, at 37 collaborating tertiary referral centres across 18 European countries of the European Epilepsy Brain Bank consortium were assessed. We included patients of all ages with histopathology available after epilepsy surgery. Histopathological diagnoses and a minimal dataset of clinical variables were collected from existing local databases and patient records. The primary outcomes were freedom from disabling seizures (Engel class 1) and drug freedom at 1, 2, and 5 years after surgery. Proportions of individuals who were Engel class 1 and drug-free were reported for the 11 main categories of histopathological diagnosis. We analysed the association between histopathology, duration of epilepsy, and age at surgery, and the primary outcomes using random effects multivariable logistic regression to control for confounding.

FINDINGS

9147 patients were included, of whom seizure outcomes were available for 8191 (89·5%) participants at 2 years, and for 5577 (61·0%) at 5 years. The diagnoses of low-grade epilepsy associated neuroepithelial tumour (LEAT), vascular malformation, and hippocampal sclerosis had the best seizure outcome at 2 years after surgery, with 77·5% (1027 of 1325) of patients free from disabling seizures for LEAT, 74·0% (328 of 443) for vascular malformation, and 71·5% (2108 of 2948) for hippocampal sclerosis. The worst seizure outcomes at 2 years were seen for patients with focal cortical dysplasia type I or mild malformation of cortical development (50·0%, 213 of 426 free from disabling seizures), those with malformation of cortical development-other (52·3%, 212 of 405 free from disabling seizures), and for those with no histopathological lesion (53·5%, 396 of 740 free from disabling seizures). The proportion of patients being both Engel class 1 and drug-free was 0-14% at 1 year and increased to 14-51% at 5 years. Children were more often drug-free; temporal lobe surgeries had the best seizure outcomes; and a longer duration of epilepsy was associated with reduced chance of favourable seizure outcomes and drug freedom. This effect of duration was evident for all lesions, except for hippocampal sclerosis.

INTERPRETATION

Histopathological diagnosis, age at surgery, and duration of epilepsy are important prognostic factors for outcomes of epilepsy surgery. In every patient with refractory focal epilepsy presumed to be lesional, evaluation for surgery should be considered.

FUNDING

None.

Advanced neuroimaging techniques for evaluating pediatric epilepsy

Accurate localization of the seizure onset zone is important for better seizure outcomes and preventing deficits following epilepsy surgery. Recent advances in neuroimaging techniques have increased our understanding of the underlying etiology and improved our ability to noninvasively identify the seizure onset zone. Using epilepsy-specific magnetic resonance imaging (MRI) protocols, structural MRI allows better detection of the seizure onset zone, particularly when it is interpreted by experienced neuroradiologists. Ultra-high-field imaging and postprocessing analysis with automated machine learning algorithms can detect subtle structural abnormalities in MRI-negative patients. Tractography derived from diffusion tensor imaging can delineate white matter connections associated with epilepsy or eloquent function, thus, preventing deficits after epilepsy surgery. Arterial spin-labeling perfusion MRI, simultaneous electroencephalography (EEG)-functional MRI (fMRI), and magnetoencephalography (MEG) are noinvasive imaging modalities that can be used to localize the epileptogenic foci and assist in planning epilepsy surgery with positron emission tomography, ictal single-photon emission computed tomography, and intracranial EEG monitoring. MEG and fMRI can localize and lateralize the area of the cortex that is essential for language, motor, and memory function and identify its relationship with planned surgical resection sites to reduce the risk of neurological impairments. These advanced structural and functional imaging modalities can be combined with postprocessing methods to better understand the epileptic network and obtain valuable clinical information for predicting long-term outcomes in pediatric epilepsy.

Predicting outcome of epilepsy surgery in clinical practice: Prediction models vs. clinical acumen

PURPOSE

Epilepsy surgery is an evidence-based treatment for drug-refractory focal epilepsy. We aimed to evaluate how well preoperative outcome estimates of epilepsy surgery in clinical practice correlated with postoperative outcome and to compare prediction by the clinical team with available scores (m-SFS, ESN).

METHOD

Retrospective cohort study including patients with drug-refractory focal epilepsy who underwent resective epilepsy surgery at Epilepsy Center Hessen, Marburg, between 1998-2016. Patients were categorized into four groups based on their estimated chance of postoperative seizure freedom documented in preoperative medical records. Variables required for calculation of m-SFS and ESN were also extracted from presurgical medical records. Seizure outcome using Engel/ILAE classifications was extracted from postoperative medical records.

RESULTS

148 patients were included and 98 had follow-up at 5 years. 69 (70%) had Engel I and 50 (51%) ILAE 1 outcome. Observed 5-year outcome for very good candidates was 20/22 (91%) Engel I and 14/22 (64%) ILAE 1, for good candidates 29/40 (73%) Engel I and 21/40 (53%) ILAE 1, for candidates with slightly reduced chance 11/18 (61%) Engel I and 9/18 (50%) ILAE 1 and for candidates with considerably reduced chance 1/5 (20%) Engel I and 1/5 (20%) ILAE 1.There were no significant differences in discrimination or overall performance between predictions by the clinical team, ESN and m-SFS.

CONCLUSIONS

Preoperative outcome estimates corresponded well with observed outcome indicating adequate patient counseling.

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

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.

Divergent network properties that predict early surgical failure versus late recurrence in temporal lobe epilepsy

OBJECTIVE

The objectives of this study were to identify functional and structural network properties that are associated with early versus long-term seizure outcomes after mesial temporal lobe epilepsy (mTLE) surgery and to determine how these compare to current clinically used methods for seizure outcome prediction.

METHODS

In this case-control study, 26 presurgical mTLE patients and 44 healthy controls were enrolled to undergo 3-T MRI for functional and structural connectivity mapping across an 8-region network of mTLE seizure propagation, including the hippocampus (left and right), insula (left and right), thalamus (left and right), one midline precuneus, and one midline mid-cingulate. Seizure outcome was assessed annually for up to 3 years. Network properties and current outcome prediction methods related to early and long-term seizure outcome were investigated.

RESULTS

A network model was previously identified across 8 patients with seizure-free mTLE. Results confirmed that whole-network propagation connectivity patterns inconsistent with the mTLE model predict early surgical failure. In those patients with networks consistent with the mTLE network, specific bilateral within-network hippocampal to precuneus impairment (rather than unilateral impairment ipsilateral to the seizure focus) was associated with mild seizure recurrence. No currently used clinical variables offered the same ability to predict long-term outcome.

CONCLUSIONS

It is known that there are important clinical differences between early surgical failure that lead to frequent disabling seizures and late recurrence of less frequent mild seizures. This study demonstrated that divergent network connectivity variability, whole-network versus within-network properties, were uniquely associated with these disparate outcomes.

Deep learning applied to whole-brain connectome to determine seizure control after epilepsy surgery

OBJECTIVE

We evaluated whether deep learning applied to whole-brain presurgical structural connectomes could be used to predict postoperative seizure outcome more accurately than inference from clinical variables in patients with mesial temporal lobe epilepsy (TLE).

METHODS

Fifty patients with unilateral TLE were classified either as having persistent disabling seizures (SZ) or becoming seizure-free (SZF) at least 1 year after epilepsy surgery. Their presurgical structural connectomes were reconstructed from whole-brain diffusion tensor imaging. A deep network was trained based on connectome data to classify seizure outcome using 5-fold cross-validation.

RESULTS

Classification accuracy of our trained neural network showed positive predictive value (PPV; seizure freedom) of 88 ± 7% and mean negative predictive value (NPV; seizure refractoriness) of 79 ± 8%. Conversely, a classification model based on clinical variables alone yielded <50% accuracy. The specific features that contributed to high accuracy classification of the neural network were located not only in the ipsilateral temporal and extratemporal regions, but also in the contralateral hemisphere.

SIGNIFICANCE

Deep learning demonstrated to be a powerful statistical approach capable of isolating abnormal individualized patterns from complex datasets to provide a highly accurate prediction of seizure outcomes after surgery. Features involved in this predictive model were both ipsilateral and contralateral to the clinical foci and spanned across limbic and extralimbic networks.

Accuracy of an online tool to assess appropriateness for an epilepsy surgery evaluation-A population-based Swedish study

OBJECTIVE

The Canadian Appropriateness of Epilepsy Surgery (CASES) tool was developed to help physicians identify patients who should be referred for an epilepsy surgery evaluation. The aim of this study was to determine the accuracy of this tool using a population-based cohort registry (the Swedish National Epilepsy Surgery Register) of patients who underwent epilepsy surgery between 1990 and 2012.

METHODS

Overall, 1044 patients met eligibility criteria for the study and were deemed to be surgical candidates by epilepsy experts. Demographic and epilepsy related characteristics were examined and summarized using descriptive statistics. A CASES appropriateness score was calculated for each of these patients. Chi squared analyses or fisher's exact tests were used to determine if there were any relationships between demographic and epilepsy related characteristics not captured in the tool and appropriateness scores.

RESULTS

The mean appropriateness score was 8.6 and 985 (Sensitivity: 94.35%; 95% CI, 92.77%-95.60%) patients were appropriate, 46 (4.41%; 95% CI, 3.31%-5.84%) were uncertain, and 13 (1.25%; 95% CI, 0.72%-2.13%) were inappropriate for an epilepsy surgery evaluation. The mean necessity score, which was only calculated for the 985 appropriate patients, was 8.7. All 13 inappropriate patients had tried less than two anti-epileptic drugs (AEDs). In addition, age at onset of epilepsy and age at epilepsy surgery were both significantly associated with appropriateness score.

CONCLUSIONS

These results demonstrate that the CASES tool is highly sensitive as it designated 94.3% of epilepsy surgery patients as appropriate for an epilepsy surgery evaluation. All of those classified as inappropriate were not drug resistant, as they had not yet tried two AEDs.

Factors affecting seizure outcome after epilepsy surgery: an observational series

IMPORTANCE

Surgical treatment can bring seizure remission in people with focal epilepsy but requires careful selection of candidates.

OBJECTIVES

To determine which preoperative factors are associated with postoperative seizure outcome.

DESIGN

We audited seizure outcome of 693 adults who had resective epilepsy surgery between 1990 and 2010 and used survival analysis to detect preoperatively identifiable risk factors of poor seizure outcome.

RESULTS

Seven factors were significantly associated with increased probability of recurrence of seizures with impaired awareness postsurgery: MRI findings (eg, HR adjusted for other variables in the model 2.5; 95% CI 1.6 to 3.8 for normal MRI compared with hippocampal sclerosis), a history of secondarily generalised convulsive seizures (2.3; 95% CI 1.7 to 3.0 for these seizures in the previous year vs never), psychiatric history (1.3; 95% CI 1.1 to 1.7), learning disability (1.8; 95% CI 1.2 to 2.6) and extratemporal (vs temporal) surgery (1.4; 95% CI 1.02, 2.04). People with an older onset of epilepsy had a higher probability of seizure recurrence (1.01; 95% CI 1.00, 1.02) as did those who had used more antiepileptic drugs (1.05; 95% CI 1.01 to 1.09). Combinations of variables associated with seizure recurrence gave overall low probabilities of 5-year seizure freedom (eg, a normal MRI and convulsive seizures in the previous year has a probability of seizure freedom at 5 years of approximately 0.19).

CONCLUSIONS AND RELEVANCE

Readily identified clinical features and investigations are associated with reduced probability of good outcome and need consideration when planning presurgical evaluation.

Neuroimaging in Epilepsy

In recent years, the field of neuroimaging has undergone dramatic development. Specifically, of importance for clinicians and researchers managing patients with epilepsies, new methods of brain imaging in search of the seizure-producing abnormalities have been implemented, and older methods have undergone additional refinement. Methodology to predict seizure freedom and cognitive outcome has also rapidly progressed. In general, the image data processing methods are very different and more complicated than even a decade ago. In this review, we identify the recent developments in neuroimaging that are aimed at improved management of epilepsy patients. Advances in structural imaging, diffusion imaging, fMRI, structural and functional connectivity, hybrid imaging methods, quantitative neuroimaging, and machine-learning are discussed. We also briefly summarize the potential new developments that may shape the field of neuroimaging in the near future and may advance not only our understanding of epileptic networks as the source of treatment-resistant seizures but also better define the areas that need to be treated in order to provide the patients with better long-term outcomes.

Magnetic resonance imaging connectivity for the prediction of seizure outcome in temporal lobe epilepsy

OBJECTIVE

Currently, approximately 60-70% of patients with unilateral temporal lobe epilepsy (TLE) remain seizure-free 3 years after surgery. The goal of this work was to develop a presurgical connectivity-based biomarker to identify those patients who will have an unfavorable seizure outcome 1-year postsurgery.

METHODS

Resting-state functional and diffusion-weighted 3T magnetic resonance imaging (MRI) was acquired from 22 unilateral (15 right, 7 left) patients with TLE and 35 healthy controls. A seizure propagation network was identified including ipsilateral (to seizure focus) and contralateral hippocampus, thalamus, and insula, with bilateral midcingulate and precuneus. Between each pair of regions, functional connectivity based on correlations of low frequency functional MRI signals, and structural connectivity based on streamline density of diffusion MRI data were computed and transformed to metrics related to healthy controls of the same age.

RESULTS

A consistent connectivity pattern representing the network expected in patients with seizure-free outcome was identified using eight patients who were seizure-free at 1-year postsurgery. The hypothesis that increased similarity to the model would be associated with better seizure outcome was tested in 14 other patients (Engel class IA, seizure-free: n = 5; Engel class IB-II, favorable: n = 4; Engel class III-IV, unfavorable: n = 5) using two similarity metrics: Pearson correlation and Euclidean distance. The seizure-free connectivity model successfully separated all the patients with unfavorable outcome from the seizure-free and favorable outcome patients (p = 0.0005, two-tailed Fisher's exact test) through the combination of the two similarity metrics with 100% accuracy. No other clinical and demographic predictors were successful in this regard.

SIGNIFICANCE

This work introduces a methodologic framework to assess individual patients, and demonstrates the ability to use network connectivity as a potential clinical tool for epilepsy surgery outcome prediction after more comprehensive validation.

Outcomes of resective surgery in children and adolescents with focal lesional epilepsy: The experience of a tertiary epilepsy center

OBJECTIVE

This study aimed to investigate the efficacy of resective surgery in children with focal lesional epilepsy by evaluating the predictive value of pre- and postsurgical factors in terms of seizure freedom.

METHODS

This study included 61 children aged between 2 and 18years who were admitted to the pediatric video-EEG unit for presurgical workup. Each patient was evaluated with a detailed history, video-EEG, neuroimaging, and postsurgical outcomes according to Engel classification to predict postsurgical seizure freedom. All the possible factors including history, etiology, presurgical evaluation, surgical procedures, and postsurgical results were analyzed for their predictive value for postoperative seizure freedom.

RESULTS

Of the 61 patients, 75% were diagnosed as having temporal lobe epilepsy (TLE), and 25% were diagnosed with extra-TLE. Two years after the surgery, 78.6% were seizure-free, of which 89% had TLE, and 50% had extra-TLE (p<0.05). Patients were more likely to have a favorable outcome for seizure freedom if they had rare seizure frequency, focal EEG findings, and focal seizures; had a temporal epileptogenic zone; or had TLE and hippocampal sclerosis. On the other hand, patients were more likely to have unfavorable results for seizure freedom if they had younger age of seizure onset, frequent seizures before the surgery, a frontal or multilobar epileptogenic zone, secondarily generalized seizures, extra-TLE with frontal lobe surgery, or focal cortical dysplasia.

SIGNIFICANCE

Resective surgery is one of the most effective treatment methods in children with intractable epilepsy. A history of young age of seizure onset, frequent seizures before surgery, secondarily generalized seizures, a multilobar epileptogenic zone, frontal lobe surgery, and focal cortical dysplasia (FCD) are the most important predictive factors indicating that a patient would continue having seizures after surgery. On the other hand, focal seizure semiologies, temporal lobe localization, and hippocampal sclerosis indicate that a patient would have better results in terms of seizure freedom.

Preoperative prediction of temporal lobe epilepsy surgery outcome

PURPOSE

There is controversy about relative contributions of ictal scalp video EEG recording (vEEG), routine scalp outpatient interictal EEG (rEEG), intracranial EEG (iEEG) and MRI for predicting seizure-free outcomes after temporal lobectomy. We reviewed NIH experience to determine contributions at specific time points as well as long-term predictive value of standard pre-surgical investigations.

METHODS

Raw data was obtained via retrospective chart review of 151 patients. After exclusions, 118 remained (median 5 years follow-up). MRI-proven mesial temporal sclerosis (MTSr) was considered a separate category for analysis. Logistic regression estimated odds ratios at 6-months, 1-year, and 2 years; proportional hazard models estimated long-term comparisons. Subset analysis of the proportional hazard model was performed including only patients with commonly encountered situations in each of the modalities, to maximize statistical inference.

RESULTS

Any MRI finding, MRI proven MTS, rEEG, vEEG and iEEG did not predict two-year seizure-free outcome. MTSr was predictive at six months (OR=2.894, p=0. 0466), as were MRI and MTSr at one year (OR=10.4231, p=0. 0144 and OR=3.576, p=0. 0091). Correcting for rEEG and MRI, vEEG failed to predict outcome at 6 months, 1year and 2 years. Proportional hazard analysis including all available follow-up failed to achieve significance for any modality. In the subset analysis of 83 patients with commonly encountered results, vEEG modestly predicted long-term seizure-free outcomes with a proportional hazard ratio of 1.936 (p=0.0304).

CONCLUSIONS

In this study, presurgical tools did not provide unambiguous long-term outcome predictions. Multicenter prospective studies are needed to determine optimal presurgical epilepsy evaluation.

Bridging the Gap between Evidence and Practice for Adults with Medically Refractory Temporal Lobe Epilepsy: Is a Change in Funding Policy Needed to Stimulate a Shift in Practice?

Objective. Surgery for medically refractory epilepsy (MRE) in adults has been shown to be effective but underutilized. Comprehensive health economic evaluations of surgery compared with continued medical management are limited. Policy changes may be necessary to influence practice shift. Methods. A critical review of the literature on health economic analyses for adults with MRE was conducted. The MEDLINE, EMBASE, CENTRAL, CRD, and EconLit databases were searched using relevant subject headings and keywords pertaining to adults, epilepsy, and health economic evaluations. The screening was conducted independently and in duplicate. Results. Four studies were identified (1 Canadian, 2 American, and 1 French). Two were cost-utility analyses and 2 were cost-effectiveness evaluations. Only one was conducted after the effectiveness of surgery was established through a randomized trial. All suggested surgery to be favorable in the medium to long term (7-8 years and beyond). The reduction of medication use was the major cost-saving parameter in favor of surgery. Conclusions. Although updated evaluations that are more generalizable across settings are necessary, surgery appears to be a favorable option from a health economic perspective. Given the limited success of knowledge translation endeavours, funder-level policy changes such as quality-based purchasing may be necessary to induce a shift in practice.

Outcome of surgery for temporal lobe epilepsy in adults - A cohort study

INTRODUCTION

The aim of the current study was to evaluate the factors associated with post-operative outcome in patients with temporal lobe epilepsy (TLE) undergoing Surgery.

METHODS

We analyzed data of 288 consecutive patients operated for drug-resistant TLE. All the patients had at least one year post surgery follow-up. Logistic regression model was used to evaluate the predictive value of different factors for outcome.

RESULTS

The mean age at onset of epilepsy of the study population was 15.51 ± 9.79 years; whereas the mean age at surgery was 32.16 ± 9.45 years, with 125 (43.4%) women. The age at surgery was significantly lower in the patients with favourable outcome (30.26 ± 9.05 vs. 34.06 ± 9.85 years; p = 0.007). The mean duration of epilepsy with age of onset below 12 years was higher than the rest (19.84 ± 7.30 vs. 13.00 ± 8.45 years; p < 0.001). The histopathology showed hippocampal sclerosis in 203 (70.4%) of the patients; isolated focal cortical dysplasia was associated with unfavourable outcome (9.3% vs.2.6%; p = 0.036). The duration of follow up ranged from 1 to 10.3 years. Three patients died late in the follow up. At the last follow 73% were seizure free and Engel's favourable outcome was noted in 82%. Duration of epilepsy greater than ten years (β = 6.997; 95%CI; 2.254-21.715; p = 0.01), younger age of onset of epilepsy (β = 1.07; 95%CI; 1.014-1.132; p = 0.015) and acute post operative seizures (APOS) (β = 4.761; 95%CI; 1.946-11.649; p = 0.001) were the predictors of unfavourable outcome.

CONCLUSION

Following surgery for TLE, 73% were seizure free and Engel's favourable outcome was noted in 82%. The predictors of unfavourable outcome were younger age of onset, pronged duration and of epilepsy and APOS.