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

Abstract: Reports an error in 'Predicting non-response to ketamine for depression: An exploratory symptom-level analysis of real-world data among military veterans' by Eric A. Miller, Houtan Totonchi Afshar, Jyoti Mishra, Roger S. McIntyre and Dhakshin Ramanathan (Psychiatry Research, 2024[May], Vol 335, 1-9). In the original article, there was a calculation mistake. Correcting this mistake does not change any of the conclusions of the paper. The authors specifically identified a mistake in the calculation of cross validated confusion matrices for the threshold-tuned models in Fig. 3c and 3d of the paper. Correcting this mistake results in slightly lower negative predictive value (NPV) and specificity for the best models. (The following abstract of the original article appeared in record [rid]2024-74849-001[/rid]). 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.