Parker G, McCraw S, Hadzi-Pavlovic D
School of Psychiatry, University of New South Wales, Sydney, Australia; Black Dog Institute, Prince of Wales Hospital, Randwick 2031, Sydney, Australia.
School of Psychiatry, University of New South Wales, Sydney, Australia; Black Dog Institute, Prince of Wales Hospital, Randwick 2031, Sydney, Australia.
J Affect Disord. 2015 Jul 15;180:148-53. doi: 10.1016/j.jad.2015.03.057. Epub 2015 Apr 11.
Studies suggest that differentiating melancholic from non-melancholic depressive disorders is advanced by use of illness course as well as symptom variables but, in practice, potentially differentiating variables are generally positioned as having equal value. Judging that differentiating features are more likely to vary in their signal intensity, we sought to determine the number of features required to effect differentiation and their hierarchical order.
The 24-item clinician-rated Sydney Melancholia Prototype Index (SMPI-CR) was completed for 364 unipolar depressed patients. The sample was divided into two cohorts according to the recruitment period. An RPART classification tree analysis identified the most discriminating SMPI items in the development sample of 197 patients, and examined the sensitivity and specificity of the diagnostic decisions, then sought to replicate findings in a validation sample of 169 patients.
Independent analyses of putative SMPI items identified only seven items as required to discriminate those with clinically-diagnosed melancholic or non-melancholic depression when the conditions were examined separately. An RPART analysis considering differentiation of melancholic and non-melancholic depression in the total samples retained five of those items in the classification tree, three of which were non-symptom items, and with 92% sensitivity and 80% specificity in the development sample. This reduced item set showed 93% sensitivity and 82% specificity in the validation sample.
Our clinical judgment of melancholic or non-melancholic depression may not correspond with the clinical logic employed by other clinicians.
Only five SMPI items were required to derive a succinct and efficient decision tree, comprising high sensitivity and specificity in differentiating melancholic and non-melancholic depression. Current study findings provide an empirical model that could enrich clinicians׳ approach to differentiating melancholic and non-melancholic depression.
研究表明，利用病程以及症状变量有助于区分 melancholic 抑郁障碍和非 melancholic 抑郁障碍，但在实践中，潜在的区分变量通常被认为具有同等价值。鉴于区分特征在信号强度上更可能存在差异，我们试图确定实现区分所需的特征数量及其层次顺序。
对 364 名单相抑郁症患者完成了由临床医生评定的 24 项悉尼 melancholia 原型指数（SMPI-CR）。根据招募时期将样本分为两个队列。一项 RPART 分类树分析确定了 197 名患者的开发样本中最具区分性的 SMPI 项目，并检查了诊断决策的敏感性和特异性，然后试图在 169 名患者的验证样本中复制研究结果。
对假定的 SMPI 项目进行独立分析时，当分别检查条件时，仅确定了七个项目可用于区分临床诊断为 melancholic 或非 melancholic 抑郁症的患者。在总样本中考虑 melancholic 和非 melancholic 抑郁症区分的 RPART 分析在分类树中保留了其中五个项目，其中三个是非症状项目，在开发样本中的敏感性为 92%，特异性为 80%。这个简化的项目集在验证样本中的敏感性为 93%，特异性为 82%。
我们对 melancholic 或非 melancholic 抑郁症的临床判断可能与其他临床医生采用的临床逻辑不一致。
仅需五个 SMPI 项目即可得出一个简洁有效的决策树，在区分 melancholic 和非 melancholic 抑郁症方面具有高敏感性和特异性。当前的研究结果提供了一个实证模型，可丰富临床医生区分 melancholic 和非 melancholic 抑郁症的方法。
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