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Use of Staging Models for Treatment-Resistant Depression Is Not Helpful in Predicting Nonresponse to Acute Intravenous Ketamine Treatment

H. Sakurai, Bettina B. Hoeppner, F. Jain, Simmie Foster, Paola Pedrelli, David Mischoulon, Maurizio Fava, Cristina Cusin

Journal of Clinical Psychopharmacology February 14, 2022 DOI: 10.1097/jcp.0000000000001524 (opens in new tab)

Study at a glance

AI-extracted from the abstract
Characteristics Observational cohort Peer reviewed
Sample size 120
Population Patients with treatment-resistant depression receiving acute intravenous ketamine treatment
Intervention Ketamine
Duration Twice weekly for 3 weeks
Measures 16-item Quick Inventory of Depressive Symptomatology-Self Report (QIDS-SR16)
Topics Depression Esketamine Ketamine
Key findings No treatment-resistant depression staging model accurately predicted depressive improvement after acute intravenous ketamine. Age and history of neuromodulation therapy were negatively associated with percent improvement on the QIDS-SR16, while ketamine efficacy was similar across higher and lower levels of treatment resistance.

Abstract

Abstract Background Some staging models for treatment-resistant depression (TRD) have been developed in the attempt to predict treatment outcome, in particular with electroconvulsive therapy. However, these models have not been tested in predicting clinical outcome of ketamine treatment. We assessed the relationship between patients' classification with different TRD staging models and subsequent nonresponse to acute intravenous ketamine treatment.

Methods: A sample of 120 patients with TRD who received acute ketamine treatment from October 2018 to November 2020 were included. Intravenous ketamine was administered twice weekly for 3 weeks as acute treatment. Generalized linear models were fitted to examine if staging classification at baseline could predict percent change in the 16-item Quick Inventory of Depressive Symptomatology-Self Report (QIDS-SR16) scale. Potential confounders such as age, sex, and primary diagnosis were included in the models. Other generalized linear models were also fitted with the Bonferroni correction to investigate if other clinical variables of potential relevance could predict percent change in the QIDS-SR16.

Results: No TRD staging model proved accurate in predicting depressive improvement after acute ketamine treatment. Clinical variables such as age (F = 6.68, P = 0.01) and history of neuromodulation therapy (F = 5.12, P = 0.03) were negatively associated with subsequent percent improvement in the QIDS-SR16 with acute ketamine treatment.

Conclusions: The efficacy of acute intravenous ketamine treatment was similar in subjects with higher and lower level of treatment resistance, using definitions based on different TRD staging models. Further exploration of ketamine treatment predictors such as age and neuromodulation therapy is warranted.

Comparable studies

Other observational and cohort studies on ketamine for depression, most cited first.

Study Year Design Participants
Concomitant BDNF and sleep slow wave changes indicate ketamine-induced plasticity in major depressive disorder Patients with treatment-resistant major depressive disorder 2012 Observational cohort n = 30
Altered peripheral immune profiles in treatment-resistant depression: response to ketamine and prediction of treatment outcome Healthy controls and actively depressed patients with treatment-resistant depression... 2017 Observational cohort n = 59
Clinical Predictors of Ketamine Response in Treatment-Resistant Major Depression Treatment-resistant inpatients with DSM-IV-TR-diagnosed major depressive disorder or... 2014 Post hoc analysis of pooled data from four studies n = 108
An investigation of amino-acid neurotransmitters as potential predictors of clinical improvement to ketamine in depression Drug-free patients with major depressive disorder 2011 Observational cohort n = 14
Efficacy of ketamine therapy in the treatment of depression Drug-free/naïve men with severe depression, no history of psychotic disorder, head... 2019 Observational cohort n = 25

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