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      Development of a clinical model to predict vagus nerve stimulation response in pediatric patients with drug-resistant epilepsy

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          Abstract

          OBJECTIVE

          Epilepsy impacts 470,000 children in the United States. For patients with drug-resistant epilepsy (DRE) and unresectable seizure foci, vagus nerve stimulation (VNS) is a treatment option. Predicting response to VNS has been historically challenging. The objective of this study was to create a clinical VNS prediction tool for use in an outpatient setting.

          METHODS

          The authors performed an 11-year retrospective cohort analysis with 1-year follow-up. Patients < 21 years of age with DRE who underwent VNS (n = 365) were included. Logistic regressions were performed to assess clinical factors associated with VNS response (≥ 50% seizure frequency reduction after 1 year); 70% and 30% of the sample were used to train and validate the multivariable model, respectively. A prediction score was subsequently developed. Sensitivity, specificity, and area under the receiver operating characteristic curve (AUC) were calculated.

          RESULTS

          Variables associated with VNS response were < 4-year epilepsy duration before VNS (p = 0.008) and focal motor seizures (p = 0.037). The variables included in the clinical prediction score were epilepsy duration before VNS, age at seizure onset, number of pre-VNS antiseizure medications, if VNS was the patient’s first therapeutic epilepsy surgery, and predominant seizure semiology. The final AUCs were 0.7013 for the "fitted" sample and 0.6159 for the "validation" sample.

          CONCLUSIONS

          The authors developed a clinical model to predict VNS response in a large sample of pediatric patients treated with VNS. Despite the large sample size, clinical variables alone were not able to accurately predict VNS response. This score may be useful after further validation, although its predictive ability underscores the need for more robust biomarkers to predict treatment response.

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          Most cited references28

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          ILAE classification of the epilepsies: Position paper of the ILAE Commission for Classification and Terminology

          The International League Against Epilepsy (ILAE) Classification of the Epilepsies has been updated to reflect our gain in understanding of the epilepsies and their underlying mechanisms following the major scientific advances that have taken place since the last ratified classification in 1989. As a critical tool for the practicing clinician, epilepsy classification must be relevant and dynamic to changes in thinking, yet robust and translatable to all areas of the globe. Its primary purpose is for diagnosis of patients, but it is also critical for epilepsy research, development of antiepileptic therapies, and communication around the world. The new classification originates from a draft document submitted for public comments in 2013, which was revised to incorporate extensive feedback from the international epilepsy community over several rounds of consultation. It presents three levels, starting with seizure type, where it assumes that the patient is having epileptic seizures as defined by the new 2017 ILAE Seizure Classification. After diagnosis of the seizure type, the next step is diagnosis of epilepsy type, including focal epilepsy, generalized epilepsy, combined generalized, and focal epilepsy, and also an unknown epilepsy group. The third level is that of epilepsy syndrome, where a specific syndromic diagnosis can be made. The new classification incorporates etiology along each stage, emphasizing the need to consider etiology at each step of diagnosis, as it often carries significant treatment implications. Etiology is broken into six subgroups, selected because of their potential therapeutic consequences. New terminology is introduced such as developmental and epileptic encephalopathy. The term benign is replaced by the terms self-limited and pharmacoresponsive, to be used where appropriate. It is hoped that this new framework will assist in improving epilepsy care and research in the 21st century.
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            Definition of drug resistant epilepsy: consensus proposal by the ad hoc Task Force of the ILAE Commission on Therapeutic Strategies.

            To improve patient care and facilitate clinical research, the International League Against Epilepsy (ILAE) appointed a Task Force to formulate a consensus definition of drug resistant epilepsy. The overall framework of the definition has two "hierarchical" levels: Level 1 provides a general scheme to categorize response to each therapeutic intervention, including a minimum dataset of knowledge about the intervention that would be needed; Level 2 provides a core definition of drug resistant epilepsy using a set of essential criteria based on the categorization of response (from Level 1) to trials of antiepileptic drugs. It is proposed as a testable hypothesis that drug resistant epilepsy is defined as failure of adequate trials of two tolerated, appropriately chosen and used antiepileptic drug schedules (whether as monotherapies or in combination) to achieve sustained seizure freedom. This definition can be further refined when new evidence emerges. The rationale behind the definition and the principles governing its proper use are discussed, and examples to illustrate its application in clinical practice are provided.
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              • Article: not found

              Internal validation of predictive models: efficiency of some procedures for logistic regression analysis.

              The performance of a predictive model is overestimated when simply determined on the sample of subjects that was used to construct the model. Several internal validation methods are available that aim to provide a more accurate estimate of model performance in new subjects. We evaluated several variants of split-sample, cross-validation and bootstrapping methods with a logistic regression model that included eight predictors for 30-day mortality after an acute myocardial infarction. Random samples with a size between n = 572 and n = 9165 were drawn from a large data set (GUSTO-I; n = 40,830; 2851 deaths) to reflect modeling in data sets with between 5 and 80 events per variable. Independent performance was determined on the remaining subjects. Performance measures included discriminative ability, calibration and overall accuracy. We found that split-sample analyses gave overly pessimistic estimates of performance, with large variability. Cross-validation on 10% of the sample had low bias and low variability, but was not suitable for all performance measures. Internal validity could best be estimated with bootstrapping, which provided stable estimates with low bias. We conclude that split-sample validation is inefficient, and recommend bootstrapping for estimation of internal validity of a predictive logistic regression model.
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                Author and article information

                Journal
                Journal of Neurosurgery: Pediatrics
                Journal of Neurosurgery Publishing Group (JNSPG)
                1933-0707
                1933-0715
                February 01 2023
                February 01 2023
                : 1-8
                Affiliations
                [1 ]Department of Neurological Surgery, University of Pittsburgh;
                [2 ]Department of Pediatrics, Division of Child Neurology, University of Pittsburgh, Pennsylvania;
                [3 ]Department of Surgery, Division of Neurosurgery, The Hospital for Sick Children, University of Toronto, Ontario, Canada; and
                [4 ]Department of Bioengineering, University of Pittsburgh, Pennsylvania
                Article
                10.3171/2023.1.PEDS22312
                36805960
                1cc40708-2f80-4f73-9b9f-2f0a14d9d1ae
                © 2023
                History

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