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Miller EA, Afshar HT, Mishra J, McIntyre RS, Ramanathan D. Predicting non-response to ketamine for depression: An exploratory symptom-level analysis of real-world data among military veterans. Psychiatry Res 2024; 335:115858. [PMID: 38547599 DOI: 10.1016/j.psychres.2024.115858] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/03/2023] [Revised: 03/04/2024] [Accepted: 03/09/2024] [Indexed: 04/14/2024]
Abstract
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.
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Affiliation(s)
- Eric A Miller
- Department of Mental Health, VA San Diego Medical Center, San Diego, CA 92161, USA; Department of Psychiatry, UC San Diego, La Jolla, CA 92093, USA
| | - Houtan Totonchi Afshar
- Department of Mental Health, VA San Diego Medical Center, San Diego, CA 92161, USA; Department of Psychiatry, UC San Diego, La Jolla, CA 92093, USA
| | - Jyoti Mishra
- Department of Psychiatry, UC San Diego, La Jolla, CA 92093, USA; Center of Excellence for Stress and Mental Health, VA San Diego Medical Center, USA
| | - Roger S McIntyre
- Department of Psychiatry, University of Toronto, Toronto, Canada; Department of Pharmacology, University of Toronto, Toronto, Canada; Brain and Cognition Discovery Foundation, Toronto, Canada
| | - Dhakshin Ramanathan
- Department of Mental Health, VA San Diego Medical Center, San Diego, CA 92161, USA; Department of Psychiatry, UC San Diego, La Jolla, CA 92093, USA; Center of Excellence for Stress and Mental Health, VA San Diego Medical Center, USA.
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Berlow YA, Zandvakili A, Brennan MC, Williams LM, Price LH, Philip NS. Modeling the antidepressant treatment response to transcranial magnetic stimulation using an exponential decay function. Sci Rep 2023; 13:7138. [PMID: 37130868 PMCID: PMC10154303 DOI: 10.1038/s41598-023-33599-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2022] [Accepted: 04/15/2023] [Indexed: 05/04/2023] Open
Abstract
Recovery from depression often demonstrates a nonlinear pattern of treatment response, where the largest reduction in symptoms is observed early followed by smaller improvements. This study investigated whether this exponential pattern could model the antidepressant response to repetitive transcranial magnetic stimulation (TMS). Symptom ratings from 97 patients treated with TMS for depression were collected at baseline and after every five sessions. A nonlinear mixed-effects model was constructed using an exponential decay function. This model was also applied to group-level data from several published clinical trials of TMS for treatment-resistant depression. These nonlinear models were compared to corresponding linear models. In our clinical sample, response to TMS was well modeled with the exponential decay function, yielding significant estimates for all parameters and demonstrating superior fit compared to a linear model. Similarly, when applied to multiple studies comparing TMS modalities as well as to previously identified treatment response trajectories, the exponential decay models yielded consistently better fits compared to linear models. These results demonstrate that the antidepressant response to TMS follows a nonlinear pattern of improvement that is well modeled with an exponential decay function. This modeling offers a simple and useful framework to inform clinical decisions and future studies.
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Affiliation(s)
- Yosef A Berlow
- Department of Psychiatry and Human Behavior, Alpert Medical School of Brown University, Providence, RI, USA.
- VA RR&D Center for Neurorestoration and Neurotechnology, Providence VA Medical Center, 830 Chalkstone Ave, Providence, RI, 02908, USA.
| | - Amin Zandvakili
- Department of Psychiatry and Human Behavior, Alpert Medical School of Brown University, Providence, RI, USA
- VA RR&D Center for Neurorestoration and Neurotechnology, Providence VA Medical Center, 830 Chalkstone Ave, Providence, RI, 02908, USA
| | - McKenna C Brennan
- VA RR&D Center for Neurorestoration and Neurotechnology, Providence VA Medical Center, 830 Chalkstone Ave, Providence, RI, 02908, USA
| | - Leanne M Williams
- Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA, USA
- Sierra-Pacific Mental Illness Research, Education, and Clinical Center (MIRECC), Veterans Affairs Palo Alto Health Care System, Palo Alto, CA, USA
| | - Lawrence H Price
- Department of Psychiatry and Human Behavior, Alpert Medical School of Brown University, Providence, RI, USA
- Butler Hospital, Alpert Medical School of Brown University, Providence, RI, USA
| | - Noah S Philip
- Department of Psychiatry and Human Behavior, Alpert Medical School of Brown University, Providence, RI, USA
- VA RR&D Center for Neurorestoration and Neurotechnology, Providence VA Medical Center, 830 Chalkstone Ave, Providence, RI, 02908, USA
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Abstract
Antidepressant medications generally are considered to have a delayed onset of action; however, recent evidence is beginning to challenge this conventional wisdom. Meta-analysis of placebo-controlled, randomized trials reveals that patients with depression are more likely to experience a clinically significant response with antidepressants than with placebo by the end of the first week of treatment. About one third of the total treatment benefit over 6 weeks is evident by the end of the first week. Early response to antidepressants is not necessarily a placebo response.
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