Department of Physical Therapy, Thomas J. Long School of Pharmacy and Health Sciences, University of the Pacific, Stockton, CA 95211, USA.
Disabil Rehabil. 2011;33(19-20):1768-75. doi: 10.3109/09638288.2010.546936. Epub 2011 Jan 6.
To determine the diagnostic accuracy for single symptoms and clusters of symptoms to distinguish between individuals with and without chronic fatigue syndrome (CFS).
A cohort study was conducted in an exercise physiology laboratory in an academic setting. Thirty subjects participated in this study (n = 16 individuals with CFS; n = 14 non-disabled sedentary matched control subjects). An open-ended symptom questionnaire was administered 1 week following the second of two maximal cardiopulmonary exercise tests administered 24 h apart.
Receiver operating characteristics (ROC) curve analysis was significant for failure to recover within 1 day (area under the curve = 0.864, 95% confidence interval [CI]: 0.706-1.00, p = 0.001) but not within 7 days. Clinimetric properties of failure to recover within 1 day to predict membership in the CFS cohort were sensitivity 0.80, specificity 0.93, positive predictive value 0.92, negative predictive value 0.81, positive likelihood ratio 11.4, and negative likelihood ratio 0.22. Fatigue demonstrated high sensitivity and modest specificity to distinguish between cohorts, while neuroendocrine dysfunction, immune dysfunction, pain, and sleep disturbance demonstrated high specificity and modest sensitivity. ROC analysis suggested cut-point of three associated symptoms (0.871, 95% CI: 0.717-1.00, p < 0.001). A significant binary logistic regression model (p < 0.001) revealed immune abnormalities, sleep disturbance and pain accurately classified 92% of individuals with CFS and 88% of control subjects.
A cluster of associated symptoms distinguishes between individuals with and without CFS. Fewer associated symptoms may be necessary to establish a diagnosis of CFS than currently described.
确定用于区分慢性疲劳综合征（CFS）患者和非 CFS 患者的单一症状和症状群的诊断准确性。
在学术环境下的运动生理学实验室中进行了队列研究。30 名受试者参与了这项研究（n=16 名 CFS 患者；n=14 名非残疾久坐匹配对照受试者）。在两次 24 小时间隔的最大心肺运动测试后的第 2 天进行了一项开放式症状问卷。
ROC 曲线分析对于 1 天内无法恢复的情况具有显著意义（曲线下面积=0.864，95%置信区间[CI]：0.706-1.00，p=0.001），但在 7 天内则没有意义。1 天内无法恢复预测 CFS 队列成员身份的临床特性为敏感性 0.80，特异性 0.93，阳性预测值 0.92，阴性预测值 0.81，阳性似然比 11.4，阴性似然比 0.22。疲劳对于区分队列具有高敏感性和适度特异性，而神经内分泌功能障碍、免疫功能障碍、疼痛和睡眠障碍则具有高特异性和适度敏感性。ROC 分析提示存在三个相关症状的切点（0.871，95%CI：0.717-1.00，p<0.001）。显著的二元逻辑回归模型（p<0.001）揭示了免疫异常、睡眠障碍和疼痛可以准确地将 92%的 CFS 患者和 88%的对照受试者进行分类。
一组相关症状可以区分 CFS 患者和非 CFS 患者。与目前描述的相比，建立 CFS 诊断可能需要较少的相关症状。
