Department of Psychosomatic Medicine and Psychotherapy, Medical University Hospital, Freiburg, Germany.
Department of Psychosomatic Medicine and Psychotherapy, University Medical Center, Ulm, Germany.
Psychiatry Res. 2018 Sep;267:480-489. doi: 10.1016/j.psychres.2018.03.086. Epub 2018 Apr 7.
To evaluate treatment effects in depression, it is important to monitor change during treatment and also to follow up for a reasonably long time. Describing the variability of symptom change trajectories is useful to better predict long-term status and to improve interventions. Outcome data (N_ = 518, 4 time points, 1 year of observation time) from a large naturalistic multi-center study on the effects of inpatient and day hospital treatment of unipolar depression were used to identify clusters of symptom trajectories. Common outcome classifications and statistical methods of longitudinal cluster analysis were applied. However, common outcome classifications (in terms of e.g. remission, relapse or recurrence) were not exhaustive, as 49.3% of the trajectories could not be allocated to its classes. Longitudinal cluster analysis reveals 7 clusters (fast response, slow response, retarded response, temporary or persistent relapse, recurrence, and nonresponse). Nonresponse at the end of treatment was a predictor of poor outcome at long term follow up. The classification of patterns of symptom change in depression should be extended. Longitudinal cluster analysis seems a valid option to analyze outcome trajectories over time if a limited number of time points of measurement are available.
为了评估抑郁症的治疗效果，重要的是要在治疗过程中监测变化，并且还要进行合理的长期随访。描述症状变化轨迹的可变性有助于更好地预测长期状态，并改进干预措施。本研究使用了一项大型自然主义多中心研究的结果数据（N_=518，4 个时间点，1 年的观察时间），该研究评估了单相抑郁症住院和日间医院治疗效果。这些数据被用于识别症状轨迹的聚类。应用了常见的结局分类和纵向聚类分析的统计方法。然而，常见的结局分类（例如缓解、复发或重现）并不全面，因为 49.3%的轨迹无法归入其类别。纵向聚类分析揭示了 7 个聚类（快速反应、缓慢反应、延迟反应、暂时或持续复发、重现和无反应）。治疗结束时的无反应是长期随访中预后不良的预测因素。抑郁症症状变化模式的分类应该扩展。如果可测量的时间点数量有限，纵向聚类分析似乎是分析随时间变化的结局轨迹的有效选择。
