Positive predictors for antidepressive response to prefrontal repetitive transcranial magnetic stimulation (rTMS)

被引:111
作者
Brakemeier, Eva-Lotta
Luborzewski, Alexander
Danker-Hopfe, Heidi
Kathmann, Norbert
Bajbouj, Malek
机构
[1] Univ Med Berlin, Charite, Dept Psychiat & Psychotherapy, D-14050 Berlin, Germany
[2] Humboldt Univ, Dept Clin Psychol, D-10099 Berlin, Germany
关键词
repetitive transcranial magnetic stimulation; major depressive disorder; response prediction;
D O I
10.1016/j.jpsychires.2006.01.013
中图分类号
R749 [精神病学];
学科分类号
100205 [精神病与精神卫生学];
摘要
Repetitive transcranial magnetic stimulation (rTMS) is a brain stimulation technique which had recently been investigated as a putative antidepressant intervention. However, there is little agreement about clinically useful predictors of rTMS outcome. Therefore, the objective of the present study was to determine whether specific biographical, clinical, and psychopathological parameters are associated with the antidepressant response to rTMS in a large sample of 70 depressive patients. We performed a logistic regression analysis in 70 patients with major depressive disorder treated with rTMS of the left dorsolateral prefrontal cortex testing the predictive value of various domains of the depression syndrome as well as the variables episode duration, degree of treatment resistance, and CORE criteria. Response was defined as a 50% reduction of the initial Hamilton score (HAMD). After two weeks of treatment, 21% of the patients showed a response to rTMS. The binary logistic regression model correctly assigned 86.7% of the responders and 96.4% of the non-responders to their final response group. In the model, a high level of sleep disturbances was a significant predictor for treatment response to rTMS. Also, a low score of treatment resistance and a short duration of episode were positive predictors. These findings provide new evidence that especially pronounced sleep disturbances may be a significant clinical predictor of a response to rTMS. Prospective rTMS studies are necessary to validate the predictive value of the derived model. (c) 2006 Elsevier Ltd. All rights reserved.
引用
收藏
页码:395 / 403
页数:9
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