Department of Psychiatry and Behavioral Sciences, Center for the Study of Aging and Human Development, Duke University Medical Center, Durham, North Carolina, U.S.A.
Int Psychogeriatr. 2011 Aug;23(6):906-22. doi: 10.1017/S1041610210002346. Epub 2011 Jan 18.
Late-life depression may be undiagnosed due to symptom expression. These analyses explore the structure of depressive symptoms in older patients diagnosed with major depression by identifying clusters of patients based on their symptom profiles.
The sample comprised 366 patients enrolled in a naturalistic treatment study. Symptom profiles were defined using responses to the Center for Epidemiologic Studies Depression Scale (CES-D), the Hamilton Rating Scale for Depression (HAM-D) and the depression section of the Diagnostic Interview Schedule (DIS) administered at enrollment. Latent class analysis (LCA) was used to place patients into homogeneous clusters. As a final step, we identified a risk profile from representative items across instruments selected through variable reduction techniques.
A model with four discrete clusters provided the best fit to the data for the CES-D and the DIS depression module, while three clusters best fit the HAM-D. Using LCA to identify clusters of patients based on their endorsement of seventeen representative symptoms, we found three clusters of patients differing in ways other than severity. Age, sex, education, marital status, age of onset, functional limitations, level of perceived stress and subjective social support were differentially distributed across clusters.
We found considerable heterogeneity in symptom profiles among older adults with an index episode of major depression. Clinical indicators such as depression history may play less of a role differentiating clusters of patients than variables such as stress, social support, and functional limitations. These findings can help conceptualize depression and potentially reduce misdiagnosis for this age group.
老年期抑郁症可能因症状表现而未被诊断。这些分析通过根据患者的症状谱将患者分为不同的组来探索诊断为重度抑郁症的老年患者的抑郁症状结构。
该样本包括 366 名参加自然治疗研究的患者。症状谱使用入院时的流行病学研究抑郁量表（CES-D）、汉密尔顿抑郁量表（HAM-D）和诊断访谈表（DIS）抑郁部分的反应来定义。潜在类别分析（LCA）用于将患者归入同质群组。作为最后一步，我们使用变量减少技术选择跨仪器的代表性项目来确定风险概况。
对于 CES-D 和 DIS 抑郁模块，具有四个离散群集的模型为数据提供了最佳拟合，而对于 HAM-D，三个群集提供了最佳拟合。使用 LCA 根据十七个代表性症状的患者的认可来识别患者的聚类，我们发现了三种不同的患者聚类，其方式与严重程度不同。年龄、性别、教育程度、婚姻状况、发病年龄、功能限制、感知压力水平和主观社会支持在聚类之间呈不同分布。
我们发现，患有首发重度抑郁症的老年患者的症状谱存在相当大的异质性。临床指标，如抑郁史，可能在区分患者聚类方面的作用不如压力、社会支持和功能限制等变量大。这些发现可以帮助理解抑郁，并可能减少该年龄段的误诊。
