Vagus nerve stimulation in treatment-resistant depression: A one-year, randomized, sham-controlled trial.

Background: Few treatments are available for individuals with marked treatment-resistant depression (TRD).

Objective: Evaluate the safety and effectiveness of FDA-approved adjunctive vagus nerve stimulation (VNS) in patients with marked TRD.

Methods: This 12-month, multicenter, double-blind, sham-controlled trial included 493 adults with marked treatment-resistant major depression who were randomized to active or no-stimulation sham VNS for 12 months.

Effects of vagus nerve stimulation on daily function and quality of life in markedly treatment-resistant major depression: Findings from a one-year, randomized, sham-controlled trial.

Background: Depression treatments aim to minimize symptom burden and optimize quality of life (QoL) and psychosocial function.

Objective: Compare the effects of adjunctive versus sham vagus nerve stimulation (VNS) on QoL and function in markedly treatment-resistant depression (TRD).

Methods: In this multicenter, double-blind, sham-controlled trial, 493 adults with TRD and ≥4 adequate but unsuccessful antidepressant treatment trials (current episode) were randomized to active (n = 249) or sham (n = 244) VNS (plus treatment as usual) over a 12-month observation period.

Clinical characteristics and treatment exposure of patients with marked treatment-resistant unipolar major depressive disorder: A RECOVER trial report.

Background: RECOVER is a randomized sham-controlled trial of vagus nerve stimulation and the largest such trial conducted with a psychiatric neuromodulation intervention.

Objective: To describe pre-implantation baseline clinical characteristics and treatment history of patients with unipolar, major depressive disorder (MDD), overall and as a function of exposure to interventional psychiatric treatments (INTs), including electroconvulsive therapy, transcranial magnetic stimulation, and esketamine.

Methods: Medical, psychiatric, and treatment records were reviewed by study investigators and an independent Study Eligibility Committee prior to study qualification.

A new machine learning-derived screening measure for differentiating bipolar from unipolar mood disorders.

Background: While there are several accepted screening measures for identifying those with a bipolar disorder, variations in overall classification rates argue for the pursuit of a more discriminating measure. Extant measures, as well as the DSM-5, rate each diagnostic criterion as having equivalent weighting values; an approach which may compromise diagnostic assignment if symptoms vary considerably in their diagnostic sensitivity. We therefore sought to develop a new measure and examine whether a weighted rating scale was superior to one assigning equivalent weightings to each item.

Categorical differentiation of the unipolar and bipolar disorders.

There has been a longstanding debate as to whether the bipolar disorders differ categorically or dimensionally, with some dimensional or spectrum models including unipolar depressive disorders within a bipolar spectrum model. We analysed manic/hypomanic symptom data in samples of clinically diagnosed bipolar I, bipolar II and unipolar patients, employing latent class analyses to determine if separate classes could be identified. Mixture analyses were also undertaken to determine if a unimodal, bimodal or a trimodal pattern was present.