Pilot study evaluating the efficacy of subcutaneous ketamine for the treatment of depression in the elderly

This open-label trial (n=24) investigates the efficacy of subcutaneous ketamine (21-42mg/70kg) for treating depression (MDD) in elderly patients.

In the first phase, participants with major depressive disorder (MDD) receive single doses of 0.3mg/kg and 0.6mg/kg ketamine in order, with a one-week gap. Phase 2 involves eligible patients from Phase 1 receiving 1-2x weekly ketamine dosing for up to 3 months based on their response.

The primary outcome is the change in depression rating scale (GDS: Geriatric Depression Scale) at 24 hours post-dose. Secondary outcomes include GDS scores in Phase 2.

The study, named KED (Ketamine Elderly Depression), is non-randomised, recruiting participants aged over 65. It aims to address the dearth of evidence-based guidelines for treating depression in elderly individuals, specifically MDDE, and evaluates the role of ketamine in achieving rapid anti-depressant effects.

The trial is registered on ANZCTR with registration number ACTRN12617001130358. It was submitted on 27/07/2017, registered on 2/08/2017, last updated on 17/07/2018, and has received ethics approval from the Southern Health and Disability Committee in New Zealand on 30/01/2018.

The study is still recruiting, with the anticipated dates for the first participant enrolment, last participant enrolment, and last data collection being 24/09/2018, 31/12/2019, and 30/04/2020, respectively.

The University of Otago is the primary sponsor, funding the research. Principal investigator A/Prof Yoram Barak is leading the study to evaluate ketamine’s effects on depression ratings, safety, and the potential for sustaining improvements in elderly patients with MDD.

Topic Depression
Compound Ketamine

Trial Details



Trial Number

Sponsors & Collaborators

University of Otago
The University of Otago (specifically the Dunedin School of Medicine) is associated with psychedelic research but doesn't yet have a full profile.

Data attribution

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