Nomogram reliability for predicting potential risk in postgraduate medical students with anxiety symptoms

Zewen Huang; Lejun Zhang; Junyu Wang; Tingting Wang; Lu Xu; Xialing Yang; Heli Lu

doi:10.1016/j.heliyon.2022.e10803

Nomogram reliability for predicting potential risk in postgraduate medical students with anxiety symptoms

Heliyon. 2022 Oct 3;8(10):e10803. doi: 10.1016/j.heliyon.2022.e10803. eCollection 2022 Oct.

Authors

Zewen Huang^{1

2}, Lejun Zhang³, Junyu Wang⁴, Tingting Wang², Lu Xu⁵, Xialing Yang⁶, Heli Lu¹

Affiliations

¹ Department of Psychosomatic Medicine, The Second Affiliated Hospital of Nanchang University, Nanchang, Jiangxi, China.
² Faculty of Medicine, Macau University of Science and Technology, Macao, China.
³ School of Psychology, South China Normal University, Guangzhou, Guangdong, China.
⁴ School of Cyberspace, Hangzhou Dianzi University, Hangzhou, Zhejiang, China.
⁵ School of Psychology and Cognitive Science, East China Normal University, Shanghai, China.
⁶ Graduate Department, The Second Affiliated Hospital of Nanchang University, Nanchang, Jiangxi, China.

Abstract

Purpose: This research aims to develop a Nomogram for exact anxiety symptoms prediction in postgraduate medical students so that they may be identified as high-risk individuals early and get focused care.

Methods: Using a convenient sampling method, for case-control matching, 126 participants with anxiety symptoms and 774 participants of the same age and gender but without anxiety symptoms were designated as the case group and control group, respectively. Multivariable logistic regression analysis was utilized to identify influencing factors for anxiety symptoms, then used to design and verify a Nomogram of anxiety symptoms.

Results: Multivariate logistic regression analysis showed that lack of social support (OR = 0.95, 95%CI: 0.91-0.99), low life satisfaction (OR = 0.91, 95%CI: 0.86-0.95), low subjective well-being (OR = 0.58, 95%CI: 0.41-0.83) and frequent tobacco and alcohol use (OR = 1.75, 95%CI: 1.10-2.80) were independent predictors of anxiety symptoms in postgraduate medical students (P < 0.05). The Nomogram risk prediction model based on the above four independent prediction factors was established, and the verified C-index (Concordance index) is 0.787 (95%CI: 0.744-0.803, P < 0.001).

Conclusions: Anxiety symptoms in postgraduate medical students are influenced by various variables. The Nomogram prediction model has high accuracy, validity, and reliability, which can provide reference for predicting anxiety symptoms in postgraduate medical students.

Keywords: Anxiety symptoms; Case-control study; Medical students; Nomogram.