The Center for Psychiatric Oncology and Behavioral Sciences, Massachusetts General Hospital, Boston, Massachusetts, USA.
J Pain Symptom Manage. 2011 Jul;42(1):52-9. doi: 10.1016/j.jpainsymman.2010.10.262. Epub 2011 Mar 12.
A central aim in the management of cancer-related fatigue (CRF) is to identify treatable causes, such as depression. However, CRF and depression symptoms overlap and frequently co-occur, complicating diagnostic assessment.
As cancer-related symptoms have been associated with more functional impairment among patients who are depressed, this study tested the ratio of fatigue interference to fatigue severity as a method for identifying depression cases. Patients who reported that interference was greater than severity were expected to show higher rates of depression as measured by self-report instrument or structured interview.
A secondary analysis was conducted using data from patients who were attending a hospital thoracic oncology clinic and who completed the Fatigue Symptom Inventory (FSI) and Hospital Anxiety and Depression Scale (Sample 1, n = 86). Analyses were then replicated in a sample of diverse cancer patients who completed the FSI and a structured clinical interview for depression on presentation to a CRF clinic at the same hospital (Sample 2, n = 39).
Receiver operating curve analyses supported use of the FSI interference/severity ratio in distinguishing depression cases and noncases (area under the curve: Sample 1 = 0.84, 95% confidence interval [CI] 0.74-0.94; Sample 2 = 0.87, 95% CI 0.76-0.99). With sensitivity and specificity weighted equally, the optimal cutoff was ≥ 1.0 in Sample 1 (sensitivity = 62.5%, specificity = 91.4%) and Sample 2 (sensitivity = 90.9%, specificity=85.7%).
A fatigue score pattern in which interference was greater than or equal to severity predicted depression in two patient samples. This ratio may be useful for brief initial screening of depression in the context of fatigue.
癌症相关疲劳（CRF）管理的一个主要目标是确定可治疗的原因，如抑郁。然而，CRF 和抑郁症状重叠且经常同时发生，这使得诊断评估变得复杂。
由于癌症相关症状与抑郁患者的更多功能障碍相关，因此本研究测试了疲劳干扰与疲劳严重程度的比值作为识别抑郁病例的方法。预计报告干扰大于严重程度的患者将表现出更高的抑郁率，这是通过自我报告工具或结构化访谈来衡量的。
使用正在参加医院胸肿瘤门诊的患者的数据进行了二次分析，并完成了疲劳症状量表（FSI）和医院焦虑和抑郁量表（样本 1，n=86）。然后，在同一医院的 CRF 诊所就诊时完成 FSI 和结构化临床抑郁访谈的不同癌症患者样本（样本 2，n=39）中复制了分析。
接受者操作曲线分析支持使用 FSI 干扰/严重程度比来区分抑郁病例和非病例（曲线下面积：样本 1=0.84，95%置信区间[CI] 0.74-0.94；样本 2=0.87，95%CI 0.76-0.99）。灵敏度和特异性加权相等时，最佳截断值在样本 1 中为≥1.0（灵敏度=62.5%，特异性=91.4%）和样本 2（灵敏度=90.9%，特异性=85.7%）。
在两个患者样本中，干扰大于或等于严重程度的疲劳评分模式预测了抑郁。这种比率可能对疲劳背景下的抑郁进行初步快速筛查有用。
