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This study aimed to identify different symptom trajectories based on the severity of depression symptoms within a 2-month follow-up, and to explore predictive factors for different symptom trajectories. Three hundred and ninety-two adults diagnosed with major depressive disorder (MDD) were recruited from two longitudinal cohorts. Patients received antidepressant treatment as usual, and the depression symptoms were evaluated by the 17-item Hamilton depression rating scale (HAMD-17) at baseline, two weeks, and eight weeks. Based on the HAMD-17 scores, different trajectories of symptom change were distinguished by applying Growth Mixture Modeling (GMM). Furthermore, the baseline sociodemographic, clinical, and cognitive characteristics were compared to identify potential predictors for different trajectories. Through GMM, three unique depressive symptom trajectories of MDD patients were identified: (1) mild-severity class with significant improvement (Mild, n = 255); (2) high-severity class with significant improvement (High, n = 39); (3) moderate-severity class with limited improvement (Limited, n = 98). Among the three trajectories, the Mild class had a relatively low level of anxiety symptoms at baseline, whereas the High class had the lowest education level and the worst cognitive performance. Additionally, participants in the Limited class exhibited an early age of onset and experienced a higher level of emotional abuse. MDD patients could be categorised into three distinct latent subtypes through different symptom trajectories in this study, and the characteristics of these subtype patients may inform identifications for trajectory-specific intervention targets.
RESUMEN
OBJECTIVE: This study aimed to identify Dizziness-Related Disability (DRD) recovery trajectories in pediatric concussion and assess clinical predictors of disability groups. MATERIALS AND METHODS: In this prospective cohort study, 81 children (8-17 years) diagnosed with an acute concussion took part in 3 evaluation sessions (baseline, 3-month, and 6-month). All sessions included the primary disability outcome, the Dizziness Handicap Inventory (DHI) to create the DRD recovery trajectories using group-based multi-trajectory modeling analysis. Each independent variable included general patients' characteristics, premorbid conditions, function and symptoms questionnaires, and clinical physical measures; and were compared between the trajectories with logistic regression models. RESULTS: Low DRD (LD) trajectory (n = 64, 79%), and a High DRD (HD) trajectory (n = 17, 21%) were identified. The Predicting and Preventing Postconcussive Problems in Pediatrics (5P) total score (Odds Ratio (OR):1.50, 95% Confidence Interval (CI): 1.01-2.22), self-reported neck pain (OR:7.25, 95%CI: 1.24-42.36), and premorbid anxiety (OR:7.25, 95%CI: 1.24-42.36) were the strongest predictors of belonging to HD group. CONCLUSIONS: Neck pain, premorbid anxiety, and the 5P score should be considered initially in clinical practice as to predict DRD at 3 and 6-month. Further research is needed to refine predictions and enhance personalized treatment strategies for pediatric concussion.
RESUMEN
OBJECTIVE: This study aimed to describe distinct trajectories of anxiety/depression symptoms and overall health status/quality of life over a period of 18 months following a breast cancer diagnosis, and identify the medical, socio-demographic, lifestyle, and psychological factors that predict these trajectories. METHODS: 474 females (mean age = 55.79 years) were enrolled in the first weeks after surgery or biopsy. Data from seven assessment points over 18 months, at 3-month intervals, were used. The two outcomes were assessed at all points. Potential predictors were assessed at baseline and the first follow-up. Machine-Learning techniques were used to detect latent patterns of change and identify the most important predictors. RESULTS: Five trajectories were identified for each outcome: stably high, high with fluctuations, recovery, deteriorating/delayed response, and stably poor well-being (chronic distress). Psychological factors (i.e., negative affect, coping, sense of control, social support), age, and a few medical variables (e.g., symptoms, immune-related inflammation) predicted patients' participation in the delayed response and the chronic distress trajectories versus all other trajectories. CONCLUSIONS: There is a strong possibility that resilience does not always reflect a stable response pattern, as there might be some interim fluctuations. The use of machine-learning techniques provides a unique opportunity for the identification of illness trajectories and a shortlist of major bio/behavioral predictors. This will facilitate the development of early interventions to prevent a significant deterioration in patient well-being.