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Published on: July 21, 2015
Development of a clinical model to predict vagus nerve stimulation response in pediatric patients with drug-resistant
Nallammai Muthiah1, Arka N Mallela1, Lena Vodovotz1
11Department of Neurological Surgery, University of Pittsburgh.
Insights
A new clinical tool aims to predict vagus nerve stimulation (VNS) response in children with drug-resistant epilepsy. While developed on a large dataset, the tool requires further validation due to moderate predictive accuracy.
Area of Science:
- Pediatric Neurology
- Epilepsy Management
- Neurosurgery
Background:
- Epilepsy affects 470,000 children in the U.S.
- Drug-resistant epilepsy (DRE) poses significant challenges
- Vagus nerve stimulation (VNS) is an option for DRE with unresectable seizure foci
Purpose of the Study:
- Develop a clinical prediction tool for VNS response in pediatric patients
- Improve prediction of treatment outcomes for DRE
- Facilitate outpatient VNS treatment planning
Main Methods:
- Retrospective cohort analysis of 365 pediatric patients with DRE undergoing VNS
- 11-year data collection with 1-year follow-up
- Logistic regression to identify predictors of VNS response (≥50% seizure reduction)
Main Results:
- Key predictors identified: epilepsy duration < 4 years and focal motor seizures
- Clinical prediction score developed using epilepsy duration, age at onset, antiseizure medications, VNS as first surgery, and seizure semiology
- Area under the curve (AUC) for prediction was 0.7013 (training) and 0.6159 (validation)
Conclusions:
- A clinical model for VNS response prediction in pediatric DRE was developed
- Clinical variables alone showed limited predictive accuracy
- Further validation and exploration of biomarkers are needed for improved prediction
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.
