Department of Clinical Psychology, Beijing Chao-Yang Hospital, Capital Medical University, Beijing, China.
The National Clinical Research Center for Mental Disorders & Beijing Key Laboratory of Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, China.
BMC Psychiatry. 2024 Aug 13;24(1):557. doi: 10.1186/s12888-024-05915-9.
Residual symptoms of depressive disorders are serious health problems. However, the progression process is hardly predictable due to high heterogeneity of the disease. This study aims to: (1) classify the patterns of changes in residual symptoms based on homogeneous data, and (2) identify potential predictors for these patterns.
In this study, we conducted a data-driven Latent Class Growth Analysis (LCGA) to identify distinct tendencies of changes in residual symptoms, which were longitudinally quantified using the QIDS-SR16 at baseline and 1/3/6 months post-baseline for depressed patients. The association between baseline characteristics (e.g. clinical features and cognitive functions) and different progression tendencies were also identified.
The tendency of changes in residual symptoms was categorized into four classes: "light residual symptom decline (15.4%)", "residual symptom disappears (39.3%)", "steady residual symptom (6.3%)" and "severe residual symptom decline (39.0%)". We observed that the second class displayed more favorable recuperation outcomes than the rest of patients. The severity, recurrence, polypharmacy, and medication adherence of symptoms are intricately linked to the duration of residual symptoms' persistence. Additionally, clinical characteristics including sleep disturbances, depressive moods, alterations in appetite or weight, and difficulties with concentration have been identified as significant factors in the recovery process.
Our research findings indicate that certain clinical characteristics in patients with depressive disorders are associated with poor recovery from residual symptoms following acute treatment. This revelation holds significant value in the targeted attention to specific patients and the development of early intervention strategies for residual symptoms accordingly.
抑郁障碍的残留症状是严重的健康问题。然而,由于疾病的高度异质性,进展过程很难预测。本研究旨在:(1)根据同质数据对残留症状的变化模式进行分类,(2)确定这些模式的潜在预测因素。
本研究采用数据驱动的潜在类别增长分析(LCGA)来识别残留症状变化的不同趋势,这些趋势使用 QIDS-SR16 在基线和基线后 1/3/6 个月进行纵向量化。还确定了基线特征(例如临床特征和认知功能)与不同进展趋势之间的关联。
残留症状变化的趋势分为四类:“轻度残留症状下降(15.4%)”、“残留症状消失(39.3%)”、“残留症状稳定(6.3%)”和“严重残留症状下降(39.0%)”。我们观察到,第二类患者的康复效果比其他患者更好。症状的严重程度、复发、多种药物治疗和药物依从性与残留症状持续时间密切相关。此外,临床特征包括睡眠障碍、抑郁情绪、食欲或体重改变以及注意力集中困难,已被确定为恢复过程中的重要因素。
我们的研究结果表明,抑郁障碍患者的某些临床特征与急性治疗后残留症状的恢复不良有关。这一发现对于针对特定患者的靶向关注以及相应的残留症状早期干预策略的制定具有重要意义。