Ketamine helps some patients with treatment-resistant depression, but predicting who will respond is difficult. Analyzing symptom trajectories from 120 patients treated with ketamine or esketamine in a real-world clinic, all symptoms improved on average, with depressed mood improving faster than low energy. A principal component analysis identified overall treatment response and a second component reflecting differences between affective and somatic symptoms. Logistic regression classifiers predicted overall response better than chance using baseline symptoms alone. By adjusting decision thresholds, models identified 22% of patients who would not respond with over 96% negative predictive value, potentially guiding treatment recommendations to avoid ineffective treatments.
Cannabis use may weaken the antidepressant effects of ketamine and repetitive transcranial magnetic stimulation (rTMS). The antidepressant effects of these treatments rely on long-term potentiation and synaptic plasticity, but cannabis activates CB1 receptors, which can impair synaptic plasticity. This suggests that cannabis use might reduce the effectiveness of ketamine and rTMS for depression treatment.