Martinek Theresa, Jarczok Marc, Rottler Edit, Hartmann Armin, Zeeck Almut, Weiß Heinz, von Wietersheim Jörn
Department of Psychosomatic Medicine and Psychotherapy, Ulm University Medical Center, Ulm, Germany.
Department of Psychosomatic Medicine and Psychotherapy, Medical University Hospital, Freiburg, Germany.
Front Psychiatry. 2023 Apr 6;14:1081474. doi: 10.3389/fpsyt.2023.1081474. eCollection 2023.
Previously established categories for the classification of disease courses of unipolar depressive disorder (relapse, remission, recovery, recurrence) are helpful, but insufficient in describing the naturalistic disease courses over time. The intention of the present study was to identify frequent disease courses of depression by means of a cluster analysis.
For the longitudinal cluster analysis, 555 datasets of patients who participated in the INDDEP (INpatient and Day clinic treatment of DEPression) study, were used. The present study uses data of patients with at least moderate depressive symptoms (major depression) over a follow-up period of 1 year after their in-patient or day-care treatments using the LIFE (Longitudinal Interval Follow-Up Evaluation)-interview. Eight German psychosomatic hospitals participated in this naturalistic observational study.
Considering only the Calinski-Harabatz index, a 2-cluster solution gives the best statistical results. In combination with other indices and clinical interpretations, the 5-cluster solution seems to be the most interesting. The cluster sizes are large enough and numerically balanced. The KML-cluster analyses revealed five well interpretable disease course clusters over the follow-up period: "sustained treatment response" ( = 202, 36.4% of the patients), "recurrence" ( = 80, 14.4%), "persisting relapse" ( = 115, 20.7%), "temporary relapse" ( = 95, 17.1%), and remission ( = 63, 11.4%).
The disease courses of many patients diagnosed with a unipolar depression do not match with the historically developed categories such as relapse, remission, and recovery. Given this context, the introduction of disease course trajectories seems helpful. These findings may promote the implementation of new therapy options, adapted to the disease courses.
先前建立的用于单相抑郁症病程分类的类别(复发、缓解、康复、再发)是有帮助的,但在描述随时间推移的自然病程方面并不充分。本研究的目的是通过聚类分析确定抑郁症常见的病程。
为进行纵向聚类分析,使用了参与INDDEP(抑郁症住院及日间门诊治疗)研究的555例患者的数据集。本研究使用了在住院或日间治疗后1年随访期内至少有中度抑郁症状(重度抑郁症)患者的数据,采用LIFE(纵向间隔随访评估)访谈。八家德国身心医院参与了这项自然观察研究。
仅考虑卡林斯基-哈拉巴斯指数,两聚类解决方案给出了最佳统计结果。结合其他指数和临床解释,五聚类解决方案似乎最有意义。聚类规模足够大且在数值上平衡。KML聚类分析揭示了随访期内五个易于解释的病程聚类:“持续治疗反应”(=202例,占患者的36.4%)、“复发”(=80例,14.4%)、“持续复发”(=115例,20.7%)、“暂时复发”(=95例,17.1%)和“缓解”(=63例,11.4%)。
许多被诊断为单相抑郁症的患者的病程与历史上发展的类别如复发、缓解和康复不匹配。在此背景下,引入病程轨迹似乎是有帮助的。这些发现可能会促进新治疗方案的实施,使其适应病程。