Validation of a multigenic model to predict seizure control in newly treated epilepsy.

Summary A multigenic classifier based on five single nucleotide polymorphisms (SNPs) was previously reported to predict treatment response in an Australian newly-diagnosed epilepsy cohort using a k-nearest neighbour (kNN) algorithm. We assessed the validity of this classifier in predicting response to initial antiepileptic drug (AED) treatment in two UK cohorts of newlydiagnosed epilepsy and investigated the utility of these five SNPs in predicting seizure control in general. The original Australian cohort constituted the training set for the classifier and was used to predict response to the first well-tolerated AED monotherapy in independently recruited UK cohorts (Glasgow, n = 281; SANAD, n = 491). A ‘‘leave-one-out’’ cross-validation was also employed, with training sets derived internally from the UK datasets. The multigenic classifier Abbreviations: AED, antiepileptic drug; ANOVA, analysis of variance; CBZ, carbamazepine; CI, confidence interval; DNA, deoxyribonucleic acid; FN, false negative; FP, false positive; GGE, genetic generalised epilepsy; kNN, k-nearest neighbour; LTG, lamotrigine; NPV, negative predictive value; OR, odds ratio; PPV, positive predictive value; SANAD, standard and new antiepileptic drugs (trial); SNP, single nucleotide polymorphism; TN, true negative; TP, true positive; UK, United Kingdom; UNC, unclassified epilepsy; VPA, valproate.