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

Fassin, Anne Katharina; Knake, Susanne; Strzelczyk, Adam; Josephson, Colin B.; Reif, Philipp S.; Haag, Anja; Carl, Barbara; Hermsen, A.; Gorny, Iris; Möller, Leona; Pagenstecher, Axel; Nimsky, Christopher; Bauer, Sebastian; Sure, Ulrich; Menzler, Katja; Rosenow, Felix; Klein, Karl Martin

doi:10.1016/j.seizure.2020.01.016

Cited by 6 publications

(7 citation statements)

References 14 publications

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“…Some of our other classifications such as focal ICEEG onset and well-lateralized hypothesis are also based on our own internal conclusions, which is unfortunately a limitation of most single-center studies, given the heterogeneity of the patient population, variation in the design of the intracranial implant, and varying surgical practices across institutions. 16,32-34 Our approach to designing an intracranial study may also limit the generalizability of our findings, particularly for centers exclusively using SEEG, an approach never used in this series. Finally, given the time period of the study (2002–2016), we had only a small number of patients with RNS (n = 14), 1 patient post–laser ablation, and no patients with DBS, which does not provide the platform to compare the outcomes across these categories.…”

Section: Discussionmentioning

confidence: 98%

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

et al. 2022

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Objective:Identify predictors of a resective surgery and subsequent seizure freedom following intracranial EEG (ICEEG) for seizure onset localization.Methods: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’s information criteria (AIC) 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 non-resection groups. Additional univariate analysis was performed on eight select clinical scenarios commonly encountered during epilepsy surgical evaluations.Results:Multivariable analysis identified the presence of a lesional MRI, pre-surgical hypothesis suggesting temporal lobe onset, and a non-dominant hemisphere implant as independent predictors of resection (p<0.0001, AUC 0.80, CI 0.73-0.87). Focal ICEEG onset and undergoing a resective surgery predicted absolute seizure freedom at the five year follow up. Patients who underwent resective surgery were more likely to be seizure free at five years when compared with continued medical treatment or neuromodulation (60% vs. 7%; P<0.0001, HR 0.16, 95% CI, 0.09-0.28). Even patients thought to have unfavorable predictors (non-lesional MRI or extratemporal lobe hypothesis or dominant hemisphere implant) had ≥ 50% chance of seizure freedom at five years if they underwent resection.Interpretation:Unfavorable predictors, including having non-lesional extratemporal epilepsy, should not deter a thorough pre-surgical 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, pre-surgical hypothesis suggesting temporal lobe onset, and a non-dominant hemisphere implant are independent predictors of resection. Focal intracranial EEG onset and undergoing resection are independent predictors of 5-year seizure freedom

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Section: Discussionmentioning

confidence: 98%

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

et al. 2022

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“…Head-to-head comparison studies reported similar performance of experts and tools to predict outcome after epilepsy surgery (no statistically significant difference in C-index). 17,19 These studies included cohorts of operated patients, which bears a bias compared to the entire presurgical cohort. Although prediction of seizure outcome based on subjective estimation by experts may achieve performance similar to quantitative tools, the reproducibility is questionable; all experts who achieved similar performance to the prediction tools were epileptologists at a large surgical center with extensive training from worldwide institutions.…”

Section: Surgical Cohort Harrel's C-statistics [95% Ci]mentioning

confidence: 99%

Prediction tools and risk stratification in epilepsy surgery

Hadady,

Sperling,

Alcala‐Zermeno

et al. 2023

Epilepsia

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ObjectiveThis 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).MethodsWe 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.ResultsThe 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).SignificanceESGS 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.

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“…These have included the Epilepsy Surgery Nomogram, 3 the modified Seizure Freedom Score, 4 and the Epilepsy Surgery Grading Scale 5 . These tools do not, however, perform better than clinical judgment 2,6 . Researchers are, therefore, increasingly turning to machine learning in an attempt to improve prediction accuracy.…”

Section: Introductionmentioning

confidence: 99%

“… 5 These tools do not, however, perform better than clinical judgment. 2 , 6 Researchers are, therefore, increasingly turning to machine learning in an attempt to improve prediction accuracy.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2023

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

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

Cited by 6 publications

References 14 publications

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

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

Prediction tools and risk stratification in epilepsy surgery

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

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