Outcome prediction of initial lamotrigine monotherapy in adult patients with newly diagnosed localization related epilepsies

Summary Objective To develop and test model to predict outcome of treatment with initial lamotrigine monotherapy in adult patients with newly diagnosed localization – related epilepsy, using data available at the time of diagnosis. Methods Prospective longitudinal study included consecutive series of adult patients with newly diagnosed localization – related epilepsy started of lamotrigine monotherapy. Logistic regression analysis using backward procedure was performed with treatment failure as the outcome variable. We evaluated both calibration and discrimination of the model. Internal validation of the model was performed with bootstrapping techniques. Results A total of 159 patients on lamotrigine monotherapy have been included in final analysis. Among them 78 (49.06%) patients had persistent seizures. Finally fitted multivariate model included: 1) age at therapy start, 2) presence of complex partial seizures, 3) aetiology of epilepsy and 4) interaction of age and epilepsy aetiology. Estimated odds ratio for seizure relapse in old patients with symptomatic epilepsy is lower than for the old patients with cryptogenic epilepsy, despite strong positive covariate effect of epilepsy aetiology. The model correctly classified 69.23% patients with seizure relapses and 81.48% of patients with seizure freedom, with estimated c – statistic of 0.80. Testing practical application we observed threefold increase or reduction of odds for the seizure relapse after model's positive or negative prediction respectively. Conclusion Standard clinical data were modesty adequate to predict response to the initial trial of lamotrigine in adult patients with localization related epilepsy. Better markers of antiepileptic failure are required to guide optimal patient counselling and clinical decisions. Formal interaction analysis of variables improves outcome prediction and may be a key to correct interpretation of data.