Epilepsia · 2021 · 58 citations · 36 references
MedicineNeurological SurgerySurgeryPediatric NeurologyPediatric NeurosurgerySurvival AnalysisEpilepsy SurgeryMissing Data ComputationMissing Data ImputationSeizure SemiologyEpilepsy Surgery In ChildrenGeneralized Seizure SemiologyLogistic RegressionMultiple ImputationPostoperative Seizure FreedomPostoperative Seizure OutcomeSeizure Outcome AssessmentSeizure Prognosis In Children
OBJECTIVE: To develop and validate a model to predict seizure freedom in children undergoing cerebral hemispheric surgery for the treatment of drug-resistant epilepsy. METHODS: We analyzed 1267 hemispheric surgeries performed in pediatric participants across 32 centers and 12 countries to identify predictors of seizure freedom at 3 months after surgery. A multivariate logistic regression model was developed based on 70% of the dataset (training set) and validated on 30% of the dataset (validation set). Missing data were handled using multiple imputation techniques. RESULTS: Overall, 817 of 1237 (66%) hemispheric surgeries led to seizure freedom (median follow-up = 24 months), and 1050 of 1237 (85%) were seizure-free at 12 months after surgery. A simple regression model containing age at seizure onset, presence of generalized seizure semiology, presence of contralateral 18-fluoro-2-deoxyglucose-positron emission tomography hypometabolism, etiologic substrate, and previous nonhemispheric resective surgery is predictive of seizure freedom (area under the curve = .72). A Hemispheric Surgery Outcome Prediction Scale (HOPS) score was devised that can be used to predict seizure freedom. SIGNIFICANCE: Children most likely to benefit from hemispheric surgery can be selected and counseled through the implementation of a scale derived from a multiple regression model. Importantly, children who are unlikely to experience seizure control can be spared from the complications and deficits associated with this surgery. The HOPS score is likely to help physicians in clinical decision-making.
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Internal validation of predictive models
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