Development, validation and application of an seizure quality index for the predicting of remission after electroconvulsive therapy.

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Electroconvulsive therapy (ECT) is an established treatment option in several severe mental disorders. Remission rates are about 60% for affective disorders. It could be shown, that certain properties of both, the patient and the treatment itself modify the probability of remission in depression. However, these parameters only account for a proportion of the variance. Our opinion is that the quality of the triggered seizure within the ECT treatment has also the potential to account for a relevant proportion of this variance. The generalised seizure has been identified as the necessary element for the efficacy in ECT, but it has been shown also, that the efficacy of the seizures varies. Several studies have been conducted in order to identify characteristics of the seizure, which lead to a superior efficacy. Most of these studies are only preliminary, lack a proper sample size and were analysed in a retrospective way. Commonly, only one single seizure measurement was analysed and regularly outcome parameters lacked clinical significance. In our previous work we built a model for seizure quality, which was derived from several single seizure parameters and for which a correlation with treatment efficacy could already be demonstrated. However this model must be considered as very preliminary, because its cut-off values were only broadly estimated and the categories were classified based on clinical experience. Additionally neither the model was validated nor was it correlated with clinically relevant outcome parameters such as remission. The first aim of this project is the development of a validated - Seizure Quality Index - (SQI), which represents the general quality of a seizure in ECT and is built from several independent features of a seizure and covariates such as age. In the first step retrospective data that already exists will be pooled and a reasonable model that predicts the likelihood of a clinical remission for patients with major depression will be developed. The second step consists of a prospective study, which should validate the power of the new SQI model to predict remission. A further aim of the project is to demonstrate the applicability of the novel SQI by investigate the relations between the seizure quality index on the one hand and the peripheral brain-derived neurotrophic factor (BDNF), the sleeping quality and the cognitive side effects on the other hand.