Objective: Despite a broad arsenal of antidepressants, about a third of patients suffering
from major depressive disorder (MDD) do not respond sufficiently to adequate treatment …

Objective: The study objective was to generate a prediction model for treatment-resistant
depression (TRD) using machine learning featuring a large set of 47 clinical and …

Objectives Clinical variables were investigated in the 'treatment resistant depression (TRD)‐
III'sample to replicate earlier findings by the European research consortium 'Group for the …

Treatment-resistant depression (TRD) is a common clinical presentation responsible for
much of the burden of major depressive disorder worldwide. For this reason, TRD requires …

BACKGROUND: Early identification of depressed individuals at high risk for treatment
resistance could be helpful in selecting optimal setting and intensity of care. At present …

The article by Perlis (1) addresses a question of major importance for treatment matching of
patients with major depressive disorder (MDD): Can we identify patients seeking treatment …

OBJECTIVE: Identifying predictors of outcome among patients with treatment-resistant
depression (TRD) is challenging. We hypothesised that discrepancy between self-rated and …

Background With the emergence of evidence-based treatments for treatment-resistant
depression, strategies to identify individuals at greater risk for treatment resistance early in …

Identification of risk factors of treatment resistance may be useful to guide treatment
selection, avoid inefficient trial-and-error, and improve major depressive disorder (MDD) …

Treatment-resistant depression (TRD) typically refers to inadequate response to at least one
antidepressant trial of adequate doses and duration. TRD is a relatively common occurrence …