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It has been convincingly demonstrated that electroconvulsive therapy (ECT) works better and sooner than antidepressants in the treatment of certain subtypes of depression. Given this effectiveness, it would be unfortunate not to give ECT to patients with good response chances as this could substantially shorten the length of a severe depressive episode. Instead of going through all possible psychopharmacological treatment steps, ECT could be proposed much earlier as a treatment option for those patients who might have good response chances. This would be a great advantage for the severely depressed patient, with a decreased disease-burden and hospitalization duration.
However, up to now, objective and reliable predictive factors for good ECT response have not yet been established. Clinical characteristics such as psychomotor retardation, psychotic features and age have often been used to predict the outcome of ECT, but there is too little evidence to consider these as strong predictive factors.
The current project is designed to allow better prediction of ECT-response. The investgators base their selection of predictors on clinical impression and previous research results. The predictive capacity of psychomotor functioning, psychotic symptoms and several biomarkers will be investigated. With these clinical and biological patient and depression characteristics, the investigators aim to develop a decision making tool that will allow a more accurate indication of ECT.
The investigators also investigate ways to predict whether or not a patient will have a good response when treatment has already started, based on an early improvement of psychomotor functioning.
Another subject of great importance is predicting and preventing side-effects. When patients at risk for lasting cognitive side-effects can be identified early in the treatment course, treatment can be adjusted to prevent persistence of memory problems. Therefore, the second part of the study focuses on identifying people at risk for cognitive side effects early in the treatment course.
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74 participants in 1 patient group
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Data sourced from clinicaltrials.gov
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