Background: In longitudinal research, switching between diagnoses should be considered when examining patients with depression and anxiety. We investigated course trajectories of affective disorders over a nine-year period, comparing a categorical approach using diagnoses to a dimensional approach using symptom severity.

Method: Patients with a current depressive and/or anxiety disorder at baseline (N = 1701) were selected from the Netherlands Study of Depression and Anxiety (NESDA). Using psychiatric diagnoses, we described 'consistently recovered,' 'intermittently recovered,' 'intermittently recurrent', and 'consistently chronic' at two-, four-, six-, and nine-year follow-up. Additionally, latent class growth analysis (LCGA) using depressive, anxiety, fear, and worry symptom severity scores was used to identify distinct classes.

Results: Considering the categorical approach, 8.5% were chronic, 32.9% were intermittently recurrent, 37.6% were intermittently recovered, and 21.0% remained consistently recovered from any affective disorder at nine-year follow-up. In the dimensional approach, 66.6% were chronic, 25.9% showed partial recovery, and 7.6% had recovered.

Limitations: 30.6% of patients were lost to follow-up. Diagnoses were rated by the interviewer and questionnaires were completed by the participant.

Conclusions: Using diagnoses alone as discrete categories to describe clinical course fails to fully capture the persistence of affective symptoms that were observed when using a dimensional approach. The enduring, fluctuating presence of subthreshold affective symptoms likely predisposes patients to frequent relapse. The commonness of subthreshold symptoms and their adverse impact on long-term prognoses deserve continuous clinical attention in mental health care as well further research.

Download full-text PDF

Source
http://dx.doi.org/10.1016/j.jad.2021.08.108DOI Listing

Publication Analysis

Top Keywords

dimensional approach
12
clinical course
8
depressive anxiety
8
depression anxiety
8
categorical approach
8
recovered' 'intermittently
8
nine-year follow-up
8
affective symptoms
8
anxiety
5
diagnoses
5

Similar Publications

Objectives: This study aimed to develop an automated method for generating clearer, well-aligned panoramic views by creating an optimized three-dimensional (3D) reconstruction zone centered on the teeth. The approach focused on achieving high contrast and clarity in key dental features, including tooth roots, morphology, and periapical lesions, by applying a 3D U-Net deep learning model to generate an arch surface and align the panoramic view.

Methods: This retrospective study analyzed anonymized cone-beam CT (CBCT) scans from 312 patients (mean age 40 years; range 10-78; 41.

View Article and Find Full Text PDF

Purpose: Pulmonary perfusion imaging is a key lung health indicator with clinical utility as a diagnostic and treatment planning tool. However, current nuclear medicine modalities face challenges like low spatial resolution and long acquisition times which limit clinical utility to non-emergency settings and often placing extra financial burden on the patient. This study introduces a novel deep learning approach to predict perfusion imaging from non-contrast inhale and exhale computed tomography scans (IE-CT).

View Article and Find Full Text PDF

Gene regulatory network inference based on modified adaptive lasso.

J Bioinform Comput Biol

January 2025

School of Computer Science & Technology, Dalian University of Technology, Dalian 116024, Liaoning Province, P. R. China.

Gene regulatory networks (GRNs) reveal the regulatory interactions among genes and provide a visual tool to explain biological processes. However, how to identify direct relations among genes from gene expression data in the case of high-dimensional and small samples is a critical challenge. In this paper, we proposed a new GRN inference method based on a modified adaptive least absolute shrinkage and selection operator (MALasso).

View Article and Find Full Text PDF

Machine learning recognition of hybrid lead halide perovskites and perovskite-related structures from X-ray diffraction patterns.

Nanoscale

January 2025

Laboratory of New Materials for Solar Energetics, Department of Materials Science, Lomonosov Moscow State University, 1 Lenin Hills, 119991, Moscow, Russia.

Identification of crystal structures is a crucial stage in the exploration of novel functional materials. This procedure is usually time-consuming and can be false-positive or false-negative. This necessitates a significant level of expert proficiency in the field of crystallography and, especially, requires deep experience in perovskite-related structures of hybrid perovskites.

View Article and Find Full Text PDF

Machine learning-based radiomics for guiding lymph node dissection in clinical stage I lung adenocarcinoma: a multicenter retrospective study.

Transl Lung Cancer Res

December 2024

Department of Thoracic Surgery, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.

Background: Preoperative assessment of lymph node status is critical in managing lung cancer, as it directly impacts the surgical approach and treatment planning. However, in clinical stage I lung adenocarcinoma (LUAD), determining lymph node metastasis (LNM) is often challenging due to the limited sensitivity of conventional imaging modalities, such as computed tomography (CT) and positron emission tomography/CT (PET/CT). This study aimed to establish an effective radiomics prediction model using multicenter data for early assessment of LNM risk in patients with clinical stage I LUAD.

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!