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

Machine learning algorithm for predicting seizure control after temporal lobe resection using peri-ictal electroencephalography

Brain resection is curative for a subset of patients with drug resistant epilepsy but up to half will fail to achieve sustained seizure freedom in the long term. There is a critical need for accurate prediction tools to identify patients likely to have recurrent postoperative seizures. Results from preclinical models and intracranial EEG in humans suggest that the window of time immediately before and after a seizure ("peri-ictal") represents a unique brain state with implications for clinical outcome prediction. Using a dataset of 294 patients who underwent temporal lobe resection for seizures, we show that machine learning classifiers can make accurate predictions of postoperative seizure outcome using 5 min of peri-ictal scalp EEG data that is part of universal presurgical evaluation (AUC 0.98, out-of-group testing accuracy > 90%). This is the first approach to seizure outcome prediction that employs a routine non-invasive preoperative study (scalp EEG) with accuracy range likely to translate into a clinical tool. Decision curve analysis (DCA) shows that compared to the prevalent clinical-variable based nomogram, use of the EEG-augmented approach could decrease the rate of unsuccessful brain resections by 20%.

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.

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.

Back to the Basics in Predictive Modeling-Predicting Surgical Success

Predicting Seizure Outcome After Epilepsy Surgery: Do We Need More Complex Models, Larger Samples, or Better Data? Eriksson MH, Ripart M, Piper RJ, Piper RJ, Moeller F, Das KB, Eltze C, Cooray G, Booth J, Whitaker KJ, Chari A, Sanfilippo PM, Caballero AP, Menzies L, McTague A, Tisdall MM, Cross JH, Baldeweg T, Adler S, Wagstyl K. Epilepsia. 2023;64(8):2014-2026. doi:10.1111/epi.17637 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; p LR = .005, p MLP = .01, p XGBoost 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.

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.

Machine Learning for Precision Epilepsy Surgery

Brain surgery offers the best chance of seizure-freedom for patients with focal drug-resistant epilepsy, but only 50% achieve sustained seizure-freedom. With the explosion of data collected during routine presurgical evaluations and recent advances in computational science, we now have a tremendous potential to achieve precision epilepsy surgery: a data-driven tailoring of surgical planning. This review highlights the clinical need, the relevant computational science focusing on machine learning, and discusses some specific applications in epilepsy surgery.

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.

Multimodal prognostic features of seizure freedom in epilepsy surgery

OBJECTIVE

Accurate preoperative predictions of seizure freedom following surgery for focal drug resistant epilepsy remain elusive. Our objective was to systematically evaluate all meta-analyses of epilepsy surgery with seizure freedom as the primary outcome, to identify clinical features that are consistently prognostic and should be included in the future models.

METHODS

We searched PubMed and Cochrane using free-text and Medical Subject Heading (MeSH) terms according to Preferred Reporting Items for Systematic Reviews and Meta-Analyses. This study was registered on PROSPERO. We classified features as prognostic, non-prognostic and uncertain and into seven subcategories: 'clinical', 'imaging', 'neurophysiology', 'multimodal concordance', 'genetic', 'surgical technique' and 'pathology'. We propose a structural causal model based on these features.

RESULTS

We found 46 features from 38 meta-analyses over 22 years. The following were consistently prognostic across meta-analyses: febrile convulsions, hippocampal sclerosis, focal abnormal MRI, Single-Photon Emission Computed Tomography (SPECT) coregistered to MRI, focal ictal/interictal EEG, EEG-MRI concordance, temporal lobe resections, complete excision, histopathological lesions, tumours and focal cortical dysplasia type IIb. Severe learning disability was predictive of poor prognosis. Others, including sex and side of resection, were non-prognostic. There were limited meta-analyses investigating genetic contributions, structural connectivity or multimodal concordance and few adjusted for known confounders or performed corrections for multiple comparisons.

SIGNIFICANCE

Seizure-free outcomes have not improved over decades of epilepsy surgery and despite a multitude of models, none prognosticate accurately. Our list of multimodal population-invariant prognostic features and proposed structural causal model may serve as an objective foundation for statistical adjustments of plausible confounders for use in high-dimensional models.

PROSPERO REGISTRATION NUMBER

CRD42021185232.

ResectVol: A tool to automatically segment and characterize lacunas in brain images

OBJECTIVE

To assess and validate the performance of a new tool developed for segmenting and characterizing lacunas in postoperative MR images of epilepsy patients.

METHODS

A MATLAB-based pipeline was implemented using SPM12 to produce the 3D mask of the surgical lacuna and estimate its volume. To validate its performance, we compared the manual and automatic lacuna segmentations obtained from 51 MRI scans of epilepsy patients who underwent temporal lobe resections.

RESULTS

The code is consolidated as a tool named ResectVol, which can be run via a graphical user interface or command line. The automatic and manual segmentation comparison resulted in a median Dice similarity coefficient of 0.77 (interquartile range: 0.71-0.81).

SIGNIFICANCE

Epilepsy surgery is the treatment of choice for pharmacoresistant focal epilepsies, and despite the extensive literature on the subject, we still cannot predict surgical outcomes accurately. As the volume and location of the resected tissue are fundamentally relevant to this prediction, researchers commonly perform a manual segmentation of the lacuna, which presents human bias and does not provide detailed information about the structures removed. In this study, we introduce ResectVol, a user-friendly, fully automatic tool to accomplish these tasks. This capability enables more advanced analytical techniques applied to surgical outcomes prediction, such as machine-learning algorithms, by facilitating coregistration of the resected area and preoperative findings with other imaging modalities such as PET, SPECT, and functional MRI ResectVol is freely available at https://www.lniunicamp.com/resectvol.

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.

Improving the prediction of epilepsy surgery outcomes using basic scalp EEG findings

OBJECTIVE

This study aims to evaluate the role of scalp electroencephalography (EEG; ictal and interictal patterns) in predicting resective epilepsy surgery outcomes. We use the data to further develop a nomogram to predict seizure freedom.

METHODS

We retrospectively reviewed the scalp EEG findings and clinical data of patients who underwent surgical resection at three epilepsy centers. Using both EEG and clinical variables categorized into 13 isolated candidate predictors and 6 interaction terms, we built a multivariable Cox proportional hazards model to predict seizure freedom 2 years after surgery. Harrell's step-down procedure was used to sequentially eliminate the least-informative variables from the model until the change in the concordance index (c-index) with variable removal was less than 0.01. We created a separate model using only clinical variables. Discrimination of the two models was compared to evaluate the role of scalp EEG in seizure-freedom prediction.

RESULTS

Four hundred seventy patient records were analyzed. Following internal validation, the full Clinical + EEG model achieved an optimism-corrected c-index of 0.65, whereas the c-index of the model without EEG data was 0.59. The presence of focal to bilateral tonic-clonic seizures (FBTCS), high preoperative seizure frequency, absence of hippocampal sclerosis, and presence of nonlocalizable seizures predicted worse outcome. The presence of FBTCS had the largest impact for predicting outcome. The analysis of the models' interactions showed that in patients with unilateral interictal epileptiform discharges (IEDs), temporal lobe surgery cases had a better outcome. In cases with bilateral IEDs, abnormal magnetic resonance imaging (MRI) predicted worse outcomes, and in cases without IEDs, patients with extratemporal epilepsy and abnormal MRI had better outcomes.

SIGNIFICANCE

This study highlights the value of scalp EEG, particularly the significance of IEDs, in predicting surgical outcome. The nomogram delivers an individualized prediction of postoperative outcome, and provides a unique assessment of the relationship between the outcome and preoperative findings.

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.

Incorporation of quantitative MRI in a model to predict temporal lobe epilepsy surgery outcome

Quantitative volumetric brain MRI measurement is important in research applications, but translating it into patient care is challenging. We explore the incorporation of clinical automated quantitative MRI measurements in statistical models predicting outcomes of surgery for temporal lobe epilepsy. Four hundred and thirty-five patients with drug-resistant epilepsy who underwent temporal lobe surgery at Cleveland Clinic, Mayo Clinic and University of Campinas were studied. We obtained volumetric measurements from the pre-operative T1-weighted MRI using NeuroQuant, a Food and Drug Administration approved software package. We created sets of statistical models to predict the probability of complete seizure-freedom or an Engel score of I at the last follow-up. The cohort was randomly split into training and testing sets, with a ratio of 7:3. Model discrimination was assessed using the concordance statistic (C-statistic). We compared four sets of models and selected the one with the highest concordance index. Volumetric differences in pre-surgical MRI located predominantly in the frontocentral and temporal regions were associated with poorer outcomes. The addition of volumetric measurements to the model with clinical variables alone increased the model’s C-statistic from 0.58 to 0.70 (right-sided surgery) and from 0.61 to 0.66 (left-sided surgery) for complete seizure freedom and from 0.62 to 0.67 (right-sided surgery) and from 0.68 to 0.73 (left-sided surgery) for an Engel I outcome score. 57% of patients with extra-temporal abnormalities were seizure-free at last follow-up, compared to 68% of those with no such abnormalities (P-value = 0.02). Adding quantitative MRI data increases the performance of a model developed to predict post-operative seizure outcomes. The distribution of the regions of interest included in the final model supports the notion that focal epilepsies are network disorders and that subtle cortical volume loss outside the surgical site influences seizure outcome.

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.

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.

Algorithms in clinical epilepsy practice: Can they really help us predict epilepsy outcomes?

Significant technological advances have improved our ability to localize epilepsy and investigate the electrophysiology in patients undergoing preparation for epilepsy surgery. Conversely, our process of decision-making and outcome prediction has remained essentially restricted to subjective clinical judgment. This may have hindered our ability to improve outcomes. In this review, we highlight the cognitive biases that interfere with medical decision-making and present data on the use of algorithms and statistical models in general health care, before pivoting to discuss applications in the context of 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.