氯胺酮
医学
心理信息
萧条(经济学)
上瘾
难治性抑郁症
梅德林
精神科
系统回顾
人口
加药
随机对照试验
重症监护医学
重性抑郁障碍
药理学
内科学
认知
经济
宏观经济学
环境卫生
政治学
法学
作者
Gianmarco Ingrosso,Anthony J. Cleare,Mário F. Juruena
标识
DOI:10.1177/02698811241303597
摘要
Background: Ketamine has demonstrated both rapid and sustained efficacy in treating depression, especially in treatment-resistant cases. However, concerns regarding the addictive potential of ketamine during long-term depression treatment persist among clinicians. Aim: This review aimed to summarise the evidence on addiction phenomena associated with ketamine treatment of depression. Methods: A comprehensive search was conducted in MEDLINE, Embase, PsycInfo and Global Health databases, with additional relevant studies identified through reference lists. Sixteen studies were included, comprising six randomised controlled trials, three single-arm open-label studies, one retrospective study, three case series and three case reports, for a total of 2174 patients. Results: The studies employed various routes of administration, including intravenous, intramuscular, intranasal, oral and sublingual. Ketamine was administered in the racemic form, except for the studies that utilised intranasal esketamine. Among the included population, four patients were reported to exhibit clear signs of tolerance to the antidepressant effect of ketamine or dependence on the drug, while the majority did not. Cases of addiction phenomena reported in studies that did not meet the inclusion criteria are also discussed. Conclusions: Despite the heterogeneity in study designs and outcome assessment methods, the review underscores the relative safety of ketamine treatment for adult patients with depression, emphasising the importance of medically supervised administration, vigilant monitoring and judicious dosing. Future long-term studies employing quantitative scales to assess dependence phenomena could contribute to strengthening the evidence for the safe and effective use of ketamine in the treatment of depression.
科研通智能强力驱动
Strongly Powered by AbleSci AI