AI Article Synopsis
- Depression often recurs even after effective treatments, leading researchers to develop models to help adults with recurrent depression decide between sticking with antidepressants (ADM) or switching to Mindfulness-Based Cognitive Therapy (MBCT).
- Using data from the PREVENT trial with 424 participants, the study employed elastic net regression to create models predicting relapse risk at 24 months by analyzing various factors, finding that the ADM model was more accurate than just looking at initial depression severity.
- The results showed that individuals deemed at high risk with ADM who switched to MBCT had significantly lower relapse rates than those who continued with ADM, suggesting predictive modeling could enhance decision-making for preventing relapse in recurrent depression.
Article Abstract
Depression is highly recurrent, even following successful pharmacological and/or psychological intervention. We aimed to develop clinical prediction models to inform adults with recurrent depression choosing between antidepressant medication (ADM) maintenance or switching to Mindfulness-Based Cognitive Therapy (MBCT). Using data from the PREVENT trial (=424), we constructed prognostic models using elastic net regression that combined demographic, clinical and psychological factors to predict relapse at 24 months under ADM or MBCT. Only the ADM model (discrimination performance: AUC=.68) predicted relapse better than baseline depression severity (AUC=.54; one-tailed DeLong's test: =2.8, =.003). Individuals with the poorest ADM prognoses who switched to MBCT had better outcomes compared to those who maintained ADM (48% vs. 70% relapse, respectively; superior survival times [=-2.7, =.008]). For individuals with moderate-to-good ADM prognosis, both treatments resulted in similar likelihood of relapse. If replicated, the results suggest that predictive modeling can inform clinical decision-making around relapse prevention in recurrent depression.
Download full-text PDF |
Source |
|---|---|
| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7614103 | PMC |
| http://dx.doi.org/10.1177/21677026221076832 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Momentary Depression Severity Prediction in Patients With Acute Depression Who Undergo Sleep Deprivation Therapy: Speech-Based Machine Learning Approach.
JMIR Ment Health
December 2024
Department of Genetic Epidemiology in Psychiatry, Central Institute of Mental Health, Medical Faculty Mannheim / Heidelberg University, Mannheim, Germany.
Background: Mobile devices for remote monitoring are inevitable tools to support treatment and patient care, especially in recurrent diseases such as major depressive disorder. The aim of this study was to learn if machine learning (ML) models based on longitudinal speech data are helpful in predicting momentary depression severity. Data analyses were based on a dataset including 30 inpatients during an acute depressive episode receiving sleep deprivation therapy in stationary care, an intervention inducing a rapid change in depressive symptoms in a relatively short period of time.
View Article and Find Full Text PDFComprehensive analysis of risk factors associated with submucosal invasion in patients with early-stage gastric cancer.
World J Gastroenterol
December 2024
Department of Gastroenterology, Henan Provincial People's Hospital, Zhengzhou University People's Hospital, Zhengzhou 450000, Henan Province, China.
Background: Submucosal invasion in early-stage gastric cancer (GC) is a critical determinant of prognosis and treatment strategy, significantly influencing the risk of lymph node metastasis and recurrence. Identifying risk factors associated with submucosal invasion is essential for optimizing patient management and improving outcomes.
Aim: To comprehensively analyze clinical, imaging, and endoscopic characteristics to identify predictors of submucosal invasion in patients with early-stage differentiated GC.
Complications of Left Ventricular Assist Devices: A Case Study of Recurrent Bacteremia and Implications for Infection Management.
Cureus
November 2024
Internal Medicine, Northeast Georgia Medical Center Gainesville, Gainesville, USA.
Heart failure (HF) is a complex clinical condition with symptoms that result from ineffective ejection of blood due to functional or structural impairment of the heart. The most common causes of HF include ischemic heart disease, myocardial infarction (MI), hypertension, and valvular heart disease (VHD). As HF progresses to advanced stages, interventions, like left ventricular assist devices (LVADs), become essential, especially for patients ineligible for heart transplantation.
View Article and Find Full Text PDFThe state of implementation science in major depressive disorder: A narrative review.
Psychiatry Res
December 2024
Clinical Research Center, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China; Shanghai Key Laboratory of Psychotic Disorders, Shanghai, China. Electronic address:
Major depressive disorder (MDD) is one of the most prevalent and disabling mental disorders with high recurrence rate. There is often a gap between scientific evidence related to the effective and cost-effective treatment of depression and clinical practice. Implementation science is a field of inquiry that aims to advance the process of applying evidence-based interventions to real-world problems.
View Article and Find Full Text PDFAssociation between maternal schizophrenia and risk of serious asthma exacerbations in childhood.
Schizophr Res
December 2024
Department of Psychiatry, Faculty of Medicine, University of Toronto, Toronto, ON, Canada; ICES (formerly Institute for Clinical Evaluative Sciences), Toronto, ON, Canada; Institute of Health Policy, Management & Evaluation, University of Toronto, Toronto, ON, Canada; Women's College Hospital and Research Institute, Toronto, ON, Canada. Electronic address:
Background And Hypothesis: While maternal schizophrenia is linked to chronic childhood medical conditions, little is known about the risk of acute asthma exacerbations among children whose mothers have schizophrenia. This population-based study used health data for all of Ontario, Canada to evaluate whether having a mother with schizophrenia was associated with increased risk of asthma exacerbations.
Study Design: The study cohort included 385,989 children diagnosed with asthma from age 2 years onward, followed from the time of their asthma diagnosis up to a maximum of age 19 years.
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!