Derivation and initial validation of a surgical grading scale for the preliminary evaluation of adult patients with drug-resistant focal epilepsy

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.

Imaging and Stereotactic Electroencephalography Functional Networks to Guide Epilepsy Surgery

Epilepsy surgery is a potentially curative treatment of drug-resistant epilepsy that has remained underutilized both due to inadequate referrals and incomplete localization hypotheses. The complexity of patients evaluated for epilepsy surgery has increased, thus new approaches are necessary to treat these patients. The paradigm of epilepsy surgery has evolved to match this challenge, now considering the entire seizure network with the goal of disrupting it through resection, ablation, neuromodulation, or a combination. The network paradigm has the potential to aid in identification of the seizure network as well as treatment selection.

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.

EEG-fMRI of epileptiform discharges: non-invasive investigation of the whole brain

Simultaneous EEG-fMRI is a unique and non-invasive method for investigating epileptic activity. Interictal epileptiform discharge-related EEG-fMRI provides cortical and subcortical blood oxygen level-dependent (BOLD) signal changes specific to epileptic discharges. As a result, EEG-fMRI has revealed insights into generators and networks involved in epileptic activity in different types of epilepsy, demonstrating-for instance-the implication of the thalamus in human generalized spike and wave discharges and the role of the Default Mode Network (DMN) in absences and focal epilepsy, and proposed a mechanism for the cortico-subcortical interactions in Lennox-Gastaut syndrome discharges. EEG-fMRI can find deep sources of epileptic activity not available to scalp EEG or MEG and provides critical new information to delineate the epileptic focus when considering surgical treatment or electrode implantation. In recent years, methodological advances, such as artifact removal and automatic detection of events have rendered this method easier to implement, and its clinical potential has since been established by evidence of the impact of BOLD response on clinical decision-making and of the relationship between concordance of BOLD responses with extent of resection and surgical outcome. This review presents the recent developments in EEG-fMRI methodology and EEG-fMRI studies in different types of epileptic disorders as follows: EEG-fMRI acquisition, gradient and pulse artifact removal, statistical analysis, clinical applications, pre-surgical evaluation, altered physiological state in generalized genetic epilepsy, and pediatric EEG-fMRI studies.

Temporal lobe epilepsy lateralisation and surgical outcome prediction using diffusion imaging

OBJECTIVE

We sought to augment the presurgical workup of medically refractory temporal lobe epilepsy by creating a supervised machine learning technique that uses diffusion-weighted imaging to classify patient-specific seizure onset laterality and surgical outcome.

METHODS

151 subjects were included in this analysis: 62 patients (aged 18-68 years, 36 women) and 89 healthy controls (aged 18-71 years, 47 women). We created a supervised machine learning technique that uses diffusion-weighted metrics to classify subject groups. Specifically, we sought to classify patients versus healthy controls, unilateral versus bilateral temporal lobe epilepsy, left versus right temporal lobe epilepsy and seizure-free versus not seizure-free surgical outcome. We then reduced the dimensionality of derived features with community detection for ease of interpretation.

RESULTS

We classified the subject groups in withheld testing data sets with a cross-fold average testing areas under the receiver operating characteristic curve of 0.745 for patients versus healthy controls, 1.000 for unilateral versus bilateral seizure onset, 0.662 for left versus right seizure onset, 0.800 for left-sided seizure-free vsersu not seizure-free surgical outcome and 0.775 for right-sided seizure-free versus not seizure-free surgical outcome.

CONCLUSIONS

This technique classifies important clinical decisions in the presurgical workup of temporal lobe epilepsy by generating discerning white-matter features. We believe that this work augments existing network connectivity findings in the field by further elucidating important white-matter pathology in temporal lobe epilepsy. We hope that this work contributes to recent efforts aimed at using diffusion imaging as an augmentation to the presurgical workup of this devastating neurological disorder.

Integrating Network Neuroscience Into Epilepsy Care: Progress, Barriers, and Next Steps

Drug resistant epilepsy is a disorder involving widespread brain network alterations. Recently, many groups have reported neuroimaging and electrophysiology network analysis techniques to aid medical management, support presurgical planning, and understand postsurgical seizure persistence. While these approaches may supplement standard tests to improve care, they are not yet used clinically or influencing medical or surgical decisions. When will this change? Which approaches have shown the most promise? What are the barriers to translating them into clinical use? How do we facilitate this transition? In this review, we will discuss progress, barriers, and next steps regarding the integration of brain network analysis into the medical and presurgical pipeline.

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.

Reasons for not having epilepsy surgery

OBJECTIVE

This study was undertaken to determine reasons for adults with drug-resistant focal epilepsy who undergo presurgical evaluation not proceeding with surgery, and to identify predictors of this course.

METHODS

We retrospectively analyzed data on 617 consecutive individuals evaluated for epilepsy surgery at a tertiary referral center between January 2015 and December 2019. We compared the characteristics of those in whom a decision not to proceed with surgical treatment was made with those who underwent definitive surgery in the same period. Multivariate logistic regression was performed to identify predictors of not proceeding with surgery.

RESULTS

A decision not to proceed with surgery was reached in 315 (51%) of 617 individuals evaluated. Common reasons for this were an inability to localize the epileptogenic zone (n = 104) and the presence of multifocal epilepsy (n = 74). An individual choice not to proceed with intracranial electroencephalography (icEEG; n = 50) or surgery (n = 39), risk of significant deficit (n = 33), declining noninvasive investigation (n = 12), and coexisting neurological comorbidity (n = 3) accounted for the remainder. Compared to 166 surgically treated patients, those who did not proceed to surgery were more likely to have a learning disability (odds ratio [OR] = 2.35, 95% confidence interval [CI] = 1.07-5.16), normal magnetic resonance imaging (OR = 4.48, 95% CI = 1.68-11.94), extratemporal epilepsy (OR = 2.93, 95% CI = 1.82-4.71), bilateral seizure onset zones (OR = 3.05, 95% CI = 1.41-6.61) and to live in more deprived socioeconomic areas (median deprivation decile = 40%-50% vs. 50%-60%, p < .05).

SIGNIFICANCE

Approximately half of those evaluated for surgical treatment of drug-resistant focal epilepsy do not proceed to surgery. Early consideration and discussion of the likelihood of surgical suitability or need for icEEG may help direct referral for presurgical evaluation.

Factors associated with patients not proceeding with proposed resective epilepsy surgery

BACKGROUND

This study evaluated the association between eligible patients not proceeding with resective epilepsy surgery and various demographic, disease-specific, and epilepsy-evaluation variables.

METHODS

This retrospective case-control study included patients identified as candidates for resective epilepsy surgery at the Montefiore Medical Center between January 1, 2009 and June 30, 2017. Chi-squared, two-tailed, independent sample t-test, Mann-Whitney U test and logistic regression were utilized to identify variables associated with patients not proceeding with surgery.

RESULTS

Among the 159 potential surgical candidates reviewed over the 8.5-year study period, only 53 ultimately proceeded with surgery (33%). Eighty-seven (55%) out of these 159 patients were identified as appropriate for resective epilepsy surgery during the study period. Thirty-four (39%) of these 87 patients did not proceed with surgery. Variables independently correlated (either positively or negatively) with the patient not proceeding with surgery were: being employed [Odds Ratio (OR) 4.2, 95% confidence interval (CI) 1.12-15.73], temporal lobe lesion on MRI (OR 0.35, 95% CI 0.14-0.84), temporal lobe EEG ictal onsets (OR 0.21, 95% CI 0.07-0.62), and temporal lobe epileptogenic zone (OR 0.19, 95% CI 0.07-0.55).

CONCLUSION

The novel finding in this study is the association between employment status and whether the patient had epilepsy surgery: employed patients were 4.2 times more likely to not proceed with surgery compared to unemployed patients. In addition, patients with a temporal lobe lesion on MRI, temporal lobe EEG ictal onsets, and/or a temporal epileptogenic zone were more likely to proceed with surgery. Future work will be needed to evaluate these findings prospectively, determine if they generalize to other patient populations, explore the decision whether or not to proceed with epilepsy surgery from a patient-centered perspective, and suggest strategies to reduce barriers to this underutilized treatment.

Characterizing the seizure onset zone and epileptic network using EEG-fMRI in a rat seizure model

Accurate epileptogenic zone (EZ) or seizure onset zone (SOZ) localization is crucial for epilepsy surgery optimization. Previous animal and human studies on epilepsy have reported that changes in blood oxygen level-dependent (BOLD) signals induced by epileptic events could be used as diagnostic markers for EZ or SOZ localization. Simultaneous electroencephalography and functional magnetic resonance imaging (EEG-fMRI) recording is gaining interest as a non-invasive tool for preoperative epilepsy evaluation. However, EEG-fMRI studies have reported inconsistent and ambiguous findings. Therefore, it remains unclear whether BOLD responses can be used for accurate EZ or SOZ localization. In this study, we used simultaneous EEG-fMRI recording in a rat model of 4-aminopyridine-induced acute focal seizures to assess the spatial concordance between individual BOLD responses and the SOZ. This was to determine the optimal use of simultaneous EEG-fMRI recording in the SOZ localization. We observed a high spatial consistency between BOLD responses and the SOZ. Further, dynamic BOLD responses were consistent with the regions where the seizures were propagated. These results suggested that simultaneous EEG-fMRI recording could be used as a noninvasive clinical diagnostic technique for localizing the EZ or SOZ and could be an effective tool for mapping epileptic networks.

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.

Postoperative outcomes and surgical ratio at a newly established epilepsy center: The first 100 procedures

PURPOSE

To describe the patients' characteristics, surgical ratio, and outcomes following epilepsy surgery at the newly established Epilepsy Center Frankfurt Rhine-Main.

METHODS

We retrospectively studied the first 100 consecutive patients, including adult (n = 77) and pediatric (n = 23) patients, with drug-resistant epilepsy who underwent resective or ablative surgical procedures at a single, newly established epilepsy center. Patient characteristics, seizure and neuropsychological outcomes, histopathology, complications, and surgical ratio were analyzed.

RESULTS

The mean patient age was 28.8 years (children 10.6 years, adults 34.2 years). The mean epilepsy duration was 11.9 years (children 3.9 years, adults 14.3 years), and the mean follow-up was 1.5 years. At the most recent visit, 64% of patients remained completely seizure free [Engel IA]. The rates of perioperative complications and unexpected new neurological deficits were 5%, each. The proportion of patients showing deficits in one or more cognitive domains increased six months after surgery and decreased to presurgical proportions after two years. Symptoms of depression were significantly decreased and quality of life was significantly increased after surgery. The surgical ratio was 25.3%.

CONCLUSION

Similar postsurgical outcomes were achieved at a newly established epilepsy center compared with long-standing epilepsy centers. The lower time to surgery may reflect a general decrease in time to surgery over the last decade or the improved accessibility of a new epilepsy center in a previously underserved area. The surgical ratio was not lower than reported for established centers.

Machine Learning for Localizing Epileptogenic-Zone in the Temporal Lobe: Quantifying the Value of Multimodal Clinical-Semiology and Imaging Concordance

Background: Epilepsy affects 50 million people worldwide and a third are refractory to medication. If a discrete cerebral focus or network can be identified, neurosurgical resection can be curative. Most excisions are in the temporal-lobe, and are more likely to result in seizure-freedom than extra-temporal resections. However, less than half of patients undergoing surgery become entirely seizure-free. Localizing the epileptogenic-zone and individualized outcome predictions are difficult, requiring detailed evaluations at specialist centers. Methods: We used bespoke natural language processing to text-mine 3,800 electronic health records, from 309 epilepsy surgery patients, evaluated over a decade, of whom 126 remained entirely seizure-free. We investigated the diagnostic performances of machine learning models using set-of-semiology (SoS) with and without hippocampal sclerosis (HS) on MRI as features, using STARD criteria. Findings: Support Vector Classifiers (SVC) and Gradient Boosted (GB) decision trees were the best performing algorithms for temporal-lobe epileptogenic zone localization (cross-validated Matthews correlation coefficient (MCC) SVC 0.73 ± 0.25, balanced accuracy 0.81 ± 0.14, AUC 0.95 ± 0.05). Models that only used seizure semiology were not always better than internal benchmarks. The combination of multimodal features, however, enhanced performance metrics including MCC and normalized mutual information (NMI) compared to either alone (p < 0.0001). This combination of semiology and HS on MRI increased both cross-validated MCC and NMI by over 25% (NMI, SVC SoS: 0.35 ± 0.28 vs. SVC SoS+HS: 0.61 ± 0.27). Interpretation: Machine learning models using only the set of seizure semiology (SoS) cannot unequivocally perform better than benchmarks in temporal epileptogenic-zone localization. However, the combination of SoS with an imaging feature (HS) enhance epileptogenic lobe localization. We quantified this added NMI value to be 25% in absolute terms. Despite good performance in localization, no model was able to predict seizure-freedom better than benchmarks. The methods used are widely applicable, and the performance enhancements by combining other clinical, imaging and neurophysiological features could be similarly quantified. Multicenter studies are required to confirm generalizability. Funding: Wellcome/EPSRC Center for Interventional and Surgical Sciences (WEISS) (203145Z/16/Z).

Predicting seizure freedom with AED treatment in newly diagnosed patients with MRI-negative epilepsy: A large cohort and multicenter study

OBJECTIVE

We developed and validated a prediction score for predicting the probability of 6-month and 12-month seizure freedom of antiepileptic drug (AED) treatment in newly diagnosed patients with magnetic resonance imaging (MRI)-negative epilepsy.

METHODS

The development cohort included 543 consecutive patients from the Epilepsy Center of Henan Provincial People's Hospital, while the validation cohorts included 493 consecutive patients in two independent cohorts. Univariate analysis and a forward and backward elimination of multivariate Cox regression analysis were used to select predictive factors. The performance of the score was evaluated with C-index, calibration plots, and decision curve analysis. The risk stratification was also performed.

RESULTS

The score included five routinely available predictors including Circadian rhythms, Electroencephalography before AED treatment, Neuropsychiatric disorders, Perinatal brain injury, and History of central nervous system infection (CENPH score). When applied to the external validation cohort, the score showed good discrimination with C-index (development group: 0.83; validation group: 0.78), and calibration plots indicated well calibration, as well as the decision curve analysis showed good predictive accuracy and clinical values in four cohorts. The points of the score were categorized to the following three probability levels for predicting seizure freedom: high probability (0-83.11 points), medium probability (83.11-122.71 points), and low probability (>122.71 points). And online calculator was established to make this score easily applicable in clinical practice.

CONCLUSIONS

We established a simple, practical, and evidence-based prediction score for predicting seizure freedom with AEDs to aid in the clinical consultation and treatment decision for the newly diagnosed patients with MRI-negative epilepsy.

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.

Presurgical epilepsy evaluation and epilepsy surgery

With a prevalence of 0.8 to 1.2%, epilepsy represents one of the most frequent chronic neurological disorders; 30 to 40% of patients suffer from drug-resistant epilepsy (that is, seizures cannot be controlled adequately with antiepileptic drugs). Epilepsy surgery represents a valuable treatment option for 10 to 50% of these patients. Epilepsy surgery aims to control seizures by resection of the epileptogenic tissue while avoiding neuropsychological and other neurological deficits by sparing essential brain areas. The most common histopathological findings in epilepsy surgery specimens are hippocampal sclerosis in adults and focal cortical dysplasia in children. Whereas presurgical evaluations and surgeries in patients with mesial temporal sclerosis and benign tumors recently decreased in most centers, non-lesional patients, patients requiring intracranial recordings, and neocortical resections increased. Recent developments in neurophysiological techniques (high-density electroencephalography [EEG], magnetoencephalography, electrical and magnetic source imaging, EEG-functional magnetic resonance imaging [EEG-fMRI], and recording of pathological high-frequency oscillations), structural magnetic resonance imaging (MRI) (ultra-high-field imaging at 7 Tesla, novel imaging acquisition protocols, and advanced image analysis [post-processing] techniques), functional imaging (positron emission tomography and single-photon emission computed tomography co-registered to MRI), and fMRI significantly improved non-invasive presurgical evaluation and have opened the option of epilepsy surgery to patients previously not considered surgical candidates. Technical improvements of resective surgery techniques facilitate successful and safe operations in highly delicate brain areas like the perisylvian area in operculoinsular epilepsy. Novel less-invasive surgical techniques include stereotactic radiosurgery, MR-guided laser interstitial thermal therapy, and stereotactic intracerebral EEG-guided radiofrequency thermocoagulation.

The Epilepsy Surgery Grading Scale: Validation in an independent population with drug-resistant focal epilepsy

The Epilepsy Surgery Grading Scale (ESGS) is a simple tool that predicts a patient's likelihood of progressing to resective surgery and becoming seizure-free. The aim of our study was to validate the ESGS in an independent patient cohort. We retrospectively calculated the ESGS score for adult patients with drug-resistant focal epilepsy undergoing presurgical evaluation at two reference centers for drug-resistant epilepsy in Belgium. We classified patients into ESGS grade 1 (most favorable), grade 2 (intermediate), and grade 3 (least favorable). We assessed progression to surgery and postsurgical seizure freedom. A total of 238 patients underwent presurgical evaluation (presurgical cohort), of whom 140 progressed to surgery (surgical cohort). In the presurgical cohort, we observed significant differences in rates of surgery and in rates of seizure freedom between grades 1, 2, and 3. In the surgical cohort, we observed significant differences in rates of seizure freedom between grades 1 and 2 and between grades 1 and 3. We confirm the usefulness of the ESGS for the prognostic stratification of patients with drug-resistant focal epilepsy undergoing presurgical evaluation. Our results support the use of the ESGS in the decision process of presurgical evaluation in clinical practice.

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.

The current place of epilepsy surgery

PURPOSE OF REVIEW

Three randomized controlled trials demonstrate that surgical treatment is safe and effective for drug-resistant epilepsy (DRE), yet fewer than 1% of patients are referred for surgery. This is a review of recent trends in surgical referral for DRE, and advances in the field. Reasons for continued underutilization are discussed.

RECENT FINDINGS

Recent series indicate no increase in surgical referral for DRE over the past two decades. One study suggests that decreased referrals to major epilepsy centers can be accounted for by increased referrals to low-volume nonacademic hospitals where results are poorer, and complication rates higher. The increasing ability of high-resolution MRI to identify small neocortical lesions and an increase in pediatric surgeries, in part, explain a relative greater decrease in temporal lobe surgeries. Misconceptions continue to restrict referral. Consequently, advocacy for referral of all patients with DRE to epilepsy centers that offer specialized diagnosis and other alternative treatments, as well as psychosocial support, is recommended. Recent advances will continue to improve the safety and efficacy of surgical treatment and expand the types of patients who benefit from surgical intervention.

SUMMARY

Surgical treatment for epilepsy remains underutilized, in part because of persistent misconceptions. Rather than promote referral for surgery, it would be more appropriate to advocate that all patients with DRE deserve a consultation at a full-service epilepsy center that offers many options for eliminating or reducing disability.