Bühler Joël, Seemüller Florian, Läge Damian
Department of Psychology, University of Zurich, Zurich, Switzerland.
Departement of Psychiatry and Psychotherapy, Ludwig-Maximilians-University Munich, Munich, Germany; Department of Psychiatry, Psychotherapy and Psychosomatics, kbo-Lech-Mangfall-Klinik, Garmisch-Partenkirchen, Germany.
J Affect Disord. 2014 Jul;163:81-7. doi: 10.1016/j.jad.2014.03.053. Epub 2014 Apr 8.
Depression research has been trying to improve the response rates to treatments by identifying a valid set of differential predictor variables. Potential candidates have been proposed, one of which were different subtypes of depression. However, the results on the predictive quality of subtypes on treatment are conflicting.
The analyzed data consisted of Hamilton Depression Rating Scales (HAM-D17) of 879 depressive inpatients, which were recruited in a naturalistic multicenter study. Mean length of stay was 9.9 weeks. In a first step, a Latent Class Analysis (LCA) was conducted to classify the patients into smaller groups. In a second step, the class variable was included in a Linear Mixed Effects model to predict the same patients' response to treatment.
Five classes were obtained from LCA, showing substantially different symptom profiles. One of the classes, with a symptom profile similar to melancholic depression, showed substantially slower response to treatment (i.e., estimated time to remission; 11.3 weeks) than the remaining classes in the study (6.6-8.6 weeks).
The applied measurement instrument, the HAM-D17, did not include items for two additional, frequently found subtypes of depression: psychotic and atypical depression. Thus, these subtypes could not emerge in the LCA. Furthermore, there was no systematic variation of treatment in the data. Thus, a differential effect of the classes on treatment could not be measured.
The classification of patients according to their symptom profiles seems to be a potent predictor for treatment response. However, the obtained symptom patterns are not completely congruent with the theoretically proposed subgroups. Against the background of the results, dividing melancholic depression in a rather cognitive and vegetative subtype may be promising.
抑郁症研究一直致力于通过识别一组有效的差异预测变量来提高治疗反应率。已经提出了一些潜在的候选变量，其中之一是抑郁症的不同亚型。然而，关于亚型对治疗预测质量的结果存在矛盾。
分析的数据包括879名抑郁住院患者的汉密尔顿抑郁量表（HAM-D17），这些患者是在一项自然主义多中心研究中招募的。平均住院时间为9.9周。第一步，进行潜在类别分析（LCA）将患者分为较小的组。第二步，将类别变量纳入线性混合效应模型以预测同一患者的治疗反应。
通过LCA获得了五类，显示出明显不同的症状特征。其中一类症状特征类似于忧郁症抑郁症，其对治疗的反应明显较慢（即估计缓解时间；11.3周），比研究中的其他类别（6.6 - 8.6周）要慢得多。
所应用的测量工具HAM-D17没有包括另外两种常见的抑郁症亚型：精神病性和非典型抑郁症的项目。因此，这些亚型在LCA中未能出现。此外，数据中治疗没有系统变化。因此，无法测量类别对治疗的差异效应。
根据患者症状特征进行分类似乎是治疗反应的有力预测指标。然而，所获得的症状模式与理论上提出的亚组并不完全一致。基于这些结果，将忧郁症抑郁症分为认知型和躯体型亚型可能是有前景的。
