Effects of Chronic Pain Diagnoses on the Antidepressant Efficacy of Transcranial Magnetic Stimulation.

OBJECTIVE: Major depressive disorder (MDD) and chronic pain are highly comorbid and bidirectionally related. Repetitive transcranial magnetic stimulation (rTMS) over the dorsolateral prefrontal cortex is effective in treating MDD, but additional research is needed to determine if chronic pain interferes with rTMS for MDD. METHODS: Participants were 124 veterans ( Mage = 49.14, SD = 13.83) scheduled for 30 sessions of rTMS across 6 weeks. Depression severity was monitored weekly using the Patient Health Questionnaire-9 (PHQ-9). Having any pain diagnosis, low back pain, or headache/migraine were assessed by chart review. We fit latent basis models to estimate total change by pain diagnosis in depression scores and quadratic latent growth models to examine differences in growth rates. Then, we computed χ2 tests of group differences in response (PHQ-9 reduction ≥50%) and remission rates (final PHQ-9 < 5). RESULTS: A total of 92 participants (74%) had a documented pain diagnosis, 58 (47%) had low back pain, and 32 (26%) had headache/migraine. In growth models, depression scores initially decreased (linear slope estimate = -2.04, SE = 0.26, p < .0001), but the rate of decrease slowed over time (quadratic slope estimate = 0.18, SE = 0.04, p < .001). Overall change was not different as a function of any pain diagnosis ( p = .42), low back pain (p = .11 ), or headache/migraine ( p = .28). However, we found that low back pain was a negative predictor of response ( p = .032). CONCLUSIONS: These data support rTMS as a viable treatment option for comorbid populations. Although patients with comorbid chronic pain conditions are likely to receive benefit from rTMS for depression, adjunctive pain treatment may be indicated.

Predicting non-response to ketamine for depression: An exploratory symptom-level analysis of real-world data among military veterans.

Ketamine helps some patients with treatment resistant depression (TRD), but reliable methods for predicting which patients will, or will not, respond to treatment are lacking. Herein, we aim to inform prediction models of non-response to ketamine/esketamine in adults with TRD. This is a retrospective analysis of PHQ-9 item response data from 120 patients with TRD who received repeated doses of intravenous racemic ketamine or intranasal eskatamine in a real-world clinic. Regression models were fit to patients' symptom trajectories, showing that all symptoms improved on average, but depressed mood improved relatively faster than low energy. Principal component analysis revealed a first principal component (PC) representing overall treatment response, and a second PC that reflects variance across affective versus somatic symptom subdomains. We then trained logistic regression classifiers to predict overall response (improvement on PC1) better than chance using patients' baseline symptoms alone. Finally, by parametrically adjusting the classifier decision thresholds, we identified optimal models for predicting non-response with a negative predictive value of over 96 %, while retaining a specificity of 22 %. Thus, we could identify 22 % of patients who would not respond based purely on their baseline symptoms. This approach could inform rational treatment recommendations to avoid additional treatment failures.