This study aims to develop a nomogram to accurately predict anxiety symptoms in postgraduate medical students, enabling early identification of high-risk individuals and provision of targeted interventions. A convenience sampling method was employed to select participants for a case-control study, comprising 126 individuals with anxiety symptoms as the case group and 774 age- and gender-matched individuals without anxiety symptoms as the control group. Multivariable logistic regression analysis was conducted to identify factors associated with anxiety symptoms, which were subsequently used to develop and validate a nomogram for predicting anxiety risk. The multivariate logistic regression analysis revealed that limited social support (OR = 0.95, 95% CI: 0.91–0.99), lower life satisfaction (OR = 0.91, 95% CI: 0.86–0.95), reduced subjective well-being (OR = 0.58, 95% CI: 0.41–0.83), and frequent consumption of tobacco and alcohol (OR = 1.75, 95% CI: 1.10–2.80) were independently associated with anxiety symptoms among postgraduate medical students (P < 0.05). Based on these four predictors, a nomogram was constructed to estimate individual anxiety risk, with the model demonstrating good predictive performance as indicated by a validated C-index of 0.787 (95% CI: 0.744–0.803, P < 0.001). Anxiety symptoms among postgraduate medical students are affected by multiple factors. The developed nomogram demonstrates strong accuracy, validity, and reliability, offering a practical tool for predicting anxiety risk in this population.”
