Application value of selected serum indicators in the differential diagnosis of geriatric depression and transient depressive state
Authors Xu Y, Yao S, Wei H, Zhu X, Yu M, Li Y
Received 21 September 2017
Accepted for publication 22 December 2017
Published 8 February 2018 Volume 2018:14 Pages 459—465
Checked for plagiarism Yes
Review by Single-blind
Peer reviewer comments 3
Editor who approved publication: Professor Wai Kwong Tang
Yuhao Xu,1,* Shun Yao,2,* Hong Wei,1 Xiaolan Zhu,3 Ming Yu,1 Yuefeng Li2
1Department of Neurology, The Affiliated Hospital of Jiangsu University, Zhenjiang, People’s Republic of China; 2Department of Radiology, The Affiliated Hospital of Jiangsu University, Zhenjiang, People’s Republic of China; 3Department of Central Laboratory, The Fourth Affiliated Hospital of Jiangsu University, Zhenjiang, People’s Republic of China
*These authors contributed equally to this work
Background: Transient depressive state (TDS) is a transient, negative emotional state caused by certain events or situations. Because of the similarity in depressive symptoms between depression and TDS that arise within 2 weeks of their onset, it is difficult to distinguish TDS from depression. The aims of the present study were to investigate the application value of selected serum indicators in the differential diagnosis of geriatric depression and TDS in the early stage and to provide evidence for treatment.
Patients and methods: In this study, a total of 274 elderly patients were divided into the depression group (n=144) and the TDS group (n=130). All participants’ serum samples were collected, and 9 selected serum indicators were analyzed. Afterward, 90 patients with depression and 90 patients with TDS were used to build the diagnostic model. A binary logistic regression analysis was used to establish regression models, and the area under the receiver operating characteristic (ROC) curve was drawn. Finally, another 54 patients with depression and 40 patients with TDS were used to validate our model.
Results: For the 9 screening serum indicators, the 3 serum indicators selected to build the regression model were BDNF (P=0.001), IL-1β (P<0.001), and cortisol (P<0.001). The regression equation was Y = 1/[1 + e-(-16.258 - 0.018 (BDNF) + 0.256 (IL-1β) + 0.093 (Cortisol))], and the ROC curve of combined detection was 0.926. The diagnostic rate of the logistic model was 89.36%.
Conclusion: The logistic regression model and ROC curves based on serum levels of BDNF, IL-1β, and cortisol could distinguish depression from TDS in early stage, which could provide assistance to the differential diagnosis of geriatric depression and TDS.
Keywords: CCMD-3, depression, logistic regression, transient depressive state, ROC curve
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