Predicting depressive symptoms in college students: A nomogram integrating recent and early life events with social support.

Objective: Depressive symptoms is a common psychological problem among college students, although it lacks a prognostic prediction model. This study aims to develop a nomogram for depressive symptoms in first-year college students.

Method: A longitudinal survey was conducted among 6672 first-year students from three colleges in Anhui province, China. Logistic regression, lasso regression, and random forest models were combined to identify the most significant predictors of depressive symptoms. A nomogram was constructed based on multifactor logistic regression models.

Results: Seven risk factors for depressive symptoms were identified: emotional abuse, peer violence, academic stress, punishment, health adaptation, positive childhood experiences (PCEs), and support utilization. The training set, validation set (internal validation) and testing set (external validation) of the binary logistic regression-based model showed good discrimination (area under the curve (AUC) 0.757, 95 % CI: 0.726-0.789; 0.699, 95 % CI: 0.648-0.751; 0.709, 95 % CI: 0.670-0.748, respectively), and accuracy (Brier scores of 0.061, 0.066, and 0.073, respectively). The nomogram shows good prediction of discrimination, calibration and generalization.

Conclusion: The comprehensive nomogram constructed in this study is a useful and convenient tool for assessing the risk of depressive symptoms among first-year college students. It will help healthcare professionals to assess the risk of depressive symptoms among first-year college students, identify high-risk groups and take more effective preventive measures.