酒精使用障碍
无血性
禁欲
脑深部刺激
随机对照试验
渴求
酒精依赖
医学
心理学
精神科
上瘾
精神分裂症(面向对象编程)
酒
内科学
帕金森病
疾病
化学
生物化学
作者
Patrick Bach,Mathias Luderer,Ulf Müller,Martin Jakobs,Juan Carlos Baldermann,Jürgen Voges,Karl Kiening,Anke Lux,Veerle Visser‐Vandewalle,Joachim Klosterkötter,Daniel Huys,Wolfgang H. Sommer,Tillmann Weber,Bernhard Bogerts,Jens Kuhn,Karl Mann
标识
DOI:10.1038/s41398-023-02337-1
摘要
Abstract Treatment resistance in alcohol use disorders (AUD) is a major problem for affected individuals and for society. In the search of new treatment options, few case studies using deep brain stimulation (DBS) of the nucleus accumbens have indicated positive effects in AUD. Here we report a double-blind randomized controlled trial comparing active DBS (“DBS-EARLY ON”) against sham stimulation (“DBS-LATE ON”) over 6 months in n = 12 AUD inpatients. This 6-month blind phase was followed by a 12-month unblinded period in which all patients received active DBS. Continuous abstinence (primary outcome), alcohol use, alcohol craving, depressiveness, anxiety, anhedonia and quality of life served as outcome parameters. The primary intention-to-treat analysis, comparing continuous abstinence between treatment groups, did not yield statistically significant results, most likely due to the restricted number of participants. In light of the resulting limited statistical power, there is the question of whether DBS effects on secondary outcomes can nonetheless be interpreted as indicative of an therapeutic effect. Analyses of secondary outcomes provide evidence for this, demonstrating a significantly higher proportion of abstinent days, lower alcohol craving and anhedonia in the DBS-EARLY ON group 6 months after randomization. Exploratory responder analyses indicated that patients with high baseline alcohol craving, depressiveness and anhedonia responded to DBS. The results of this first randomized controlled trial are suggestive of beneficial effects of DBS in treatment-resistant AUD and encourage a replication in larger samples.
科研通智能强力驱动
Strongly Powered by AbleSci AI