Stopping Antidepressant Treatment Leads to Higher Risk of Depression Relapse

October 6, 2021
Kenny Walter

Kenny Walter is an editor with HCPLive. Prior to joining MJH Life Sciences in 2019, he worked as a digital reporter covering nanotechnology, life sciences, material science and more with R&D Magazine. He graduated with a degree in journalism from Temple University in 2008 and began his career as a local reporter for a chain of weekly newspapers based on the Jersey shore. When not working, he enjoys going to the beach and enjoying the shore in the summer and watching North Carolina Tar Heel basketball in the winter.

Relapse occurred in 39% of the maintenance group, compared to 56% in the discontinuation group.

Maintaining depression medication does reduce the risk of relapse and minimizes other symptoms like anxiety and withdrawal.

A team, led by Gemma Lewis, PhD, Division of Psychiatry, Faculty of Brain Sciences, compared the results of patients with major depressive disorder who maintained antidepressant treatment with patients who were given a placebo rather than their regular treatment.

While patients with depression treated at primary care practices often receive antidepressants as treatments for prolonged periods of time, data is often limited on the effects of maintaining or discontinuing antidepressant therapy.

The Trial

In the randomized, double-blind trial, the investigators identified adult patients at 150 general practices in the UK. Each patient had a history of at least 2 depressive episodes or had been taking antidepressants for 2 years or longer and felt well enough to consider stopping treatment.

The team randomly assigned patients who had received citalopram, fluoxetine, sertraline, or mirtazapine to either maintain their current antidepressant therapy or to taper and discontinue therapy with the use of a matching placebo.

The investigators sought primary outcomes of the first relapse of depression during the 52-week trial period, which was evaluated in a time-to-event analysis.

They also sought secondary outcomes of depressive and anxiety symptoms, physical and withdrawal symptoms, quality of life, time to stopping an antidepressant or placebo, and global mood ratings.

Comparing Patients

There were a total of 1466 patients who underwent screening, 478 of which enrolled in the trial—238 in the maintenance group and 240 in the discontinuation group.

The average age of the patient population was 54 years and 73% of the participants were women. There was an adherence rate of 70% in the maintenance group and 52% in the placebo group.

At the week 52 mark, relapse occurred in 39% (n = 92) of the maintenance group and 56% (n = 135) in the discontinuation group (HR, 2.06; 95% CI, 1.56-2.70; P <0.001). The investigators also found secondary outcomes followed along the same trends as the primary outcomes did.

Finally, patients in the discontinuation group ultimately had more psychiatric symptoms, including depression, anxiety, and withdrawal than the patients in the maintenance group.

“Among patients in primary care practices who felt well enough to discontinue antidepressant therapy, those who were assigned to stop their medication had a higher risk of relapse of depression by 52 weeks than those who were assigned to maintain their current therapy,” the authors wrote.

Side Effects

Side effects can be a concern for many antidepressant treatments.

Computational model-derived learning reinforcement could help reduce negative symptoms associated with most treatments formajor depressive disorder (MDD).

A team, led by Vanessa M. Brown, PhD, Department of Psychology, Virginia Tech, determined the associations among computational model-derived reinforcement learning parameters, depression symptoms, and symptom changes following treatment for major depressive disorder.

Overall, the investigators were able to identify associations of learning with symptoms during reward learning and loss learning using computational model-based analyses of behavioral choices and nural data, respectively.

For only reward learning, anhedonia was associated with model-derived learning parameters (learning rate: posterior mean regression β = −0.14; 95% CrI, −0.12 to −0.03; outcome sensitivity: posterior mean regression β = 0.18; 95% CrI, 0.02-0.37) and neural learning signals (moderation of association between striatal prediction error and expected value signals: t97 = −2.10; P = .04).

For only loss learning, negative affect was linked to learning parameters (outcome shift: posterior mean regression β = −0.11; 95% CrI, −0.20 to −0.01) and disrupted neural encoding of learning signals (association with subgenual anterior cingulate prediction error signals: r = −0.28; P = .005).

Another discovery was that CBT resulted in symptom improvement and the normalization of learning parameters that were disrupted at baseline (reward learning rate: posterior mean regression β = 0.15; 90% CrI, 0.001 to 0.41; loss outcome shift: posterior mean regression β = 0.42; 90% CrI, 0.09-0.77).

The study, “Maintenance or Discontinuation of Antidepressants in Primary Care,” was published online in The New England Journal of Medicine.


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