Use of ketamine for treatment resistant depression: updated review of literature and practical applications to a community ketamine program in Edmonton, Alberta, Canada
Bibliographic record
Abstract
Background: Though intravenous (IV) ketamine and intranasal (IN) esketamine are noted to be efficacious for treatment-resistant depression (TRD), access to each of these treatments within healthcare systems is limited due to cost, availability, and/or monitoring requirements. IV ketamine has been offered at two public hospital sites in Edmonton, Canada since 2015. Since then, demand for maintenance ketamine treatments has grown. This has required creative solutions for safe, accessible, evidence-based patient care. Objectives: Aims of this paper are twofold. First, we will provide a synthesis of current knowledge with regards to the clinical use of ketamine for TRD. Consideration will be given regarding; off-label racemic ketamine uses versus FDA-approved intranasal esketamine, populations treated, inclusion/exclusion criteria, dosing, assessing clinical response, concomitant medications, and tolerability/safety. Second, this paper will describe our experience as a community case study in applying evidence-based treatment. We will describe application of the literature review to our clinical programming, and in particular focus on cost-effective maintenance treatments, long-term safety concerns, routes of ketamine administration other than via intravenous, and cautious prescribing of ketamine outside of clinically monitored settings. Methodology: We conducted a literature review of the on the use of ketamine for TRD up to June 30, 2023. Key findings are reviewed, and we describe their application to our ketamine program. Conclusion: Evidence for the use of ketamine in resistant depression has grown in recent years, with evolving data to support and direct its clinical use. There is an increasing body of evidence to guide judicious use of ketamine in various clinical circumstances, for a population of patients with a high burden of suffering and morbidity. While large-scale, randomized controlled trials, comparative studies, and longer-term treatment outcomes is lacking, this community case study illustrates that currently available evidence can be applied to real-world clinical settings with complex patients. As cost is often a significant barrier to accessing initial and/or maintenance IV or esketamine treatments, public ketamine programs may incorporate SL or IN ketamine to support a sustainable and accessible treatment model. Three of such models are described.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".