Université Paris Descartes, France.
J Affect Disord. 2010 Jun;123(1-3):332-6. doi: 10.1016/j.jad.2009.09.022. Epub 2009 Oct 25.
Little systematic research into the diagnostic performance of instruments used to screen for clinical depression is available for people with diabetes. The objective of this study was to compare performances of the HADS and BDI-SF and their components in association with a standard diagnostic interview.
In a sample of 298 French outpatients from a diabetes clinic (165 men, aged 59.4 + or - 10.7 years), we assessed diagnoses of clinical depression (CD, n=42) and major depression (MD, n=30) using the MINI and administered the HADS and BDI-SF.
Cognitive symptoms from the BDI-SF (BDIcog) were more closely associated with MD than CD. BDIcog and HADS total scores performed best overall in identifying clinical depression (AUCs under ROC curve 85%). For identification of CD, the sensitivity/specificity of BDI cognitive symptoms was 88/71% (cutoff 3+) and for the HADS 83/65% (cutoff 13+). For identification of MD, BDIcog scored 83/80% (cutoff 4+) and HAD-A 80/76% (cutoff 9+). Logistic regression analyses further suggested that BDIcog and HAD-A discriminated between depressed and non-depressed patients better than the somatic and anhedonia items present in the same scales. The depression subscale of the HADS performed poorly.
The consecutive nature of the sample may limit the generalizability of our findings.
Results suggest that, in addition to depressed mood, both negative thoughts and anxiety are core elements for the correct identification of clinical depression in chronic illnesses such as diabetes. It may be more appropriate to use the total score when applying the HADS and distinguish non-somatic symptoms within the BDI.
针对糖尿病患者,用于筛查临床抑郁症的工具的诊断性能鲜有系统研究。本研究旨在比较 HADS 和 BDI-SF 及其各组成部分与标准诊断访谈的关联性能。
在一家糖尿病诊所的 298 名法国门诊患者中(男性 165 名,年龄 59.4 ± 10.7 岁),我们使用 MINI 评估了临床抑郁症(CD,n=42)和重度抑郁症(MD,n=30)的诊断,并进行了 HADS 和 BDI-SF 评估。
BDI-SF 的认知症状(BDIcog)与 MD 的相关性较 CD 更为密切。BDIcog 和 HADS 总分在识别临床抑郁症方面总体表现最佳(ROC 曲线下 AUC 值均>85%)。对于 CD 的识别,BDI 认知症状的敏感度/特异性为 88/71%(截断值 3+),HADS 为 83/65%(截断值 13+)。对于 MD 的识别,BDIcog 为 83/80%(截断值 4+),HAD-A 为 80/76%(截断值 9+)。Logistic 回归分析进一步表明,BDIcog 和 HAD-A 比同一量表中存在的躯体症状和快感缺失项更能区分抑郁和非抑郁患者。HADS 的抑郁亚量表表现不佳。
样本的连续性可能限制了我们研究结果的普遍性。
结果表明,除了抑郁情绪外,负面想法和焦虑也是正确识别糖尿病等慢性病中临床抑郁症的核心要素。在应用 HADS 时,可能更适合使用总分,并区分 BDI 中的非躯体症状。