Background: Improving safe and effective access to ketamine therapy is of high priority given the growing burden of mental illness. Telehealth-supported administration of sublingual ketamine is being explored toward this goal.
Methods: In this longitudinal study, moderately-to-severely depressed patients received four doses of ketamine at home over four weeks within a supportive digital health context. Treatment was structured to resemble methods of therapeutic psychedelic trials. Patients receiving a second course of treatment were also examined. Symptoms were assessed using the Patient Health Questionnaire (PHQ-9) for depression. We conducted preregistered machine learning and symptom network analyses to investigate outcomes (osf.io/v2rpx).
Results: A sample of 11,441 patients was analyzed, demonstrating a modal antidepressant response from both non-severe (n = 6384, 55.8 %) and severe (n = 2070, 18.1 %) baseline depression levels. Adverse events were detected in 3.0-4.8 % of participants and predominantly neurologic or psychiatric in nature. A second course of treatment helped extend improvements in patients who responded favorably to initial treatment. Improvement was most strongly predicted by lower depression scores and age at baseline. Symptoms of Depressed mood and Anhedonia sustained depression despite ongoing treatment.
Limitations: This study was limited by the absence of comparison or control groups and lack of a fixed-dose procedure for ketamine administration.
Conclusions: At-home, telehealth-supported ketamine administration was largely safe, well-tolerated, and associated with improvement in patients with depression. Strategies for combining psychedelic-oriented therapies with rigorous telehealth models, as explored here, may uniquely address barriers to mental health treatment.
Keywords: Depression; Ketamine; Machine learning; Network analysis; Psychedelic; Telehealth.
Copyright © 2024 The Authors. Published by Elsevier B.V. All rights reserved.