Objective: Antidepressant medication is commonly used for the prevention of depression recurrence in the perinatal period, yet it is unknown what vulnerability markers may play a role in recurrence. The objective of the current study was to provide a descriptive overview of the associated characteristics of women who experienced a perinatal recurrence of depression despite ongoing antidepressant use, and further, to identify clinically measurable vulnerability markers associated with recurrence.

Methods: Eighty-five pregnant women with a history of depression who used antidepressants (e.g. Selective Serotonin Reuptake Inhibitors or Serotonin and Noradrenaline Reuptake Inhibitors) at the start of the study were included. Clinical features, including information on psychiatric history and antidepressant use, were collected throughout the perinatal period (in this study defined as the period between 12 weeks of pregnancy untill three months postpartum). The clinical features of women experiencing recurrence of depression were described in detail. To identify vulnerability markers associated with recurrence of depression, we performed exploratory univariable logistic regression analyses.

Results: Eight women (9.4%) experienced a recurrence of depression; two during pregnancy and six in the first 12 weeks postpartum. All women with recurrence of depression had first onset of depression during childhood or adolescence and had at least 2 psychiatric co-morbidities. Identification of vulnerability markers associated with recurrence of depression yielded associations with depressive symptoms around 16 weeks of pregnancy (OR 1.28, 95%CI 1.08-1.52), number of psychiatric co-morbidities (OR 1.89, 95%CI 1.16-3.09) and duration of antidepressant use (OR 1.01, 95%CI 1.00-1.02).

Conclusion: Implementing adequate risk assessment in pregnant women who use antidepressants can help identify predictors for recurrence of depression in future studies and thus ultimately lead to improved care.

Download full-text PDF

Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6386367PMC
http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0212964PLOS

Publication Analysis

Top Keywords

recurrence depression
32
vulnerability markers
20
perinatal period
12
clinical features
12
markers associated
12
recurrence
10
depression
10
pregnant women
8
reuptake inhibitors
8
weeks pregnancy
8

Similar Publications

Epilepsy is one of the common chronic neurological diseases, affecting more than 70 million people worldwide. The brains of people with epilepsy exhibit a pathological and persistent propensity for recurrent seizures. Epilepsy often coexists with cardiovascular disease, cognitive dysfunction, depression, etc.

View Article and Find Full Text PDF

Background: Digital interventions present potential solutions for aftercare and relapse prevention in anxiety and depressive disorders. This systematic review synthesizes evidence on the efficacy of internet- and mobile-based interventions for post-acute care in these conditions.

Methods: A systematic search was conducted in electronic databases (MEDLINE, CENTRAL, Scopus, Web of Science, PsycINFO, PsycARTICLES, PsycEXTRA, ProQuest Dissertations and Theses Open, Open Access Theses and Dissertations, and Open Grey) for randomized controlled trials evaluating digital aftercare or relapse prevention interventions for adults with anxiety or depressive disorders.

View Article and Find Full Text PDF

Although COVID-19 has been declared endemic in South Korea, there are economic and psychosocial after-effects. One of these is the prevalence of depression. Depressed adolescents and young adults struggle with insecurity, loneliness, and lack of confidence due to the life limitations imposed during the pandemic.

View Article and Find Full Text PDF

Background: Corona virus disease 2019 (COVID-19) reinfection, particularly short-term reinfection, poses challenges to the management of rheumatic diseases and may increase adverse clinical outcomes. This study aims to develop machine learning models to predict and identify the risk of short-term COVID-19 reinfection in patients with rheumatic diseases.

Methods: We developed four prediction models using explainable machine learning to assess the risk of short-term COVID-19 reinfection in 543 patients with rheumatic diseases.

View Article and Find Full Text PDF

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!