Examining the impact of comorbid posttraumatic stress disorder on ketamine's real-world effectiveness in treatment-resistant depression
Bibliographic record
Abstract
Depression with comorbid posttraumatic stress disorder (PTSD) is associated with more severe symptoms and a reduced response to traditional treatments. Although ketamine shows promise as a rapid-acting antidepressant for treatment-resistant depression (TRD), its effectiveness in patients with comorbid PTSD remains underexplored. Therefore, we conducted a retrospective analysis of 134 patients from the Canadian Rapid Treatment Center of Excellence to compare the effectiveness of four ketamine infusions (0.5-0.75 mg/kg) in reducing symptoms of depression and PTSD in TRD patients with and without comorbid PTSD. A repeated-measures linear mixed model was used to evaluate the impact of comorbid PTSD on ketamine's antidepressant effectiveness, measured by the Quick Inventory of Depressive Symptomatology Self-Report (QIDS-SR16). Paired samples t-tests were used to assess changes in PTSD symptoms, measured by the PTSD Checklist for DSM-5 (PCL-5). We found a significant main effect of time on QIDS-SR16 scores, F(4, 209.32) = 36.67, p < 0.001, but no significant group-by-time interaction (p = 0.895), suggesting that comorbid PTSD did not impact the antidepressant effectiveness of ketamine. Significant improvements in PTSD symptoms were observed in overall PCL-5 scores, t(66) = 6.66, p < 0.001, and across all PCL-5 symptom clusters with moderate to large effect sizes. In a real-world sample of TRD patients, ketamine was effective in reducing symptoms of depression and PTSD, regardless of PTSD comorbidity. These findings highlight ketamine's potential as a novel intervention for a patient population that is frequently non-responders to conventional treatments. Future randomized controlled trials should explore mediating factors of improvement and long-term effects.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| 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 source (direct Gemma or distilled Codex), 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".