Psychometric validation and predictive efficacy of a comprehensive depression risk model for undergraduates

Undergraduate depression is prevalent, yet traditional screening is unidimensional and inefficient. We developed a biopsychosocial risk classification model for the cross-sectional identification of current depressive symptoms.

A cross-sectional study enrolled 898 undergraduates from a medical university in Western China (March-June 2024). The participants were randomized to training (n = 719, 80%) and test (n = 179, 20%) sets. Assessments included demographics, somatic symptoms (Somatic Symptom Scale), insomnia severity (Insomnia Severity Index, ISI), and depressive symptoms (Patient Health Questionnaire-9, PHQ-9). To avoid circularity, all PHQ-9 items were excluded from predictors; the total score defined the outcome (≥5). Independent risk factors were identified via univariate and multivariate logistic regression. Three models-random forest, XGBoost, and logistic regression-were developed. Performance was evaluated using discriminative metrics (AUC, accuracy, sensitivity, specificity, PPV, NPV, and F1 score), calibration plots, and decision curve analysis. Internal validation utilized fivefold cross-validation and bootstrap resampling (1,000 iterations). Subgroup analyses stratified the results by gender, grade, and somatic symptom severity.

The point prevalence of depressive symptoms (PHQ-9 ≥5) was 45.21% (406/898), which was significantly higher in women (OR = 2.98). Multivariate analysis identified severe somatic symptoms (OR = 37.94), moderate somatic symptoms, and social isolation as key independent risk factors. Excluding PHQ-9 items to avoid circularity, the random forest model achieved an AUC of 0.872 (95% CI: 0.841-0.903), outperforming scale-only (ΔAUC = 0.110, p < 0.001) and linear models (ΔAUC = 0.031, p = 0.042). Feature importance consistently highlighted somatic distress, insomnia severity, and lack of close friends over emotional items. Calibration was excellent (Hosmer-Lemeshow p > 0.05), and decision curve analysis supported net clinical benefit (thresholds 0.2-0.9).

A comprehensive model combining physiological, psychological, and social factors yields excellent cross-sectional discriminative capability and stability for identifying undergraduates currently at risk for depressive symptoms. The proposed three-step clinical pathway (universal screening, targeted re-evaluation, and precision intervention) can facilitate large-scale, early identification in university settings. Due to the cross-sectional design, the term "prediction" is not used in a temporal or causal sense; rather, the model estimates the probability of concurrent depressive symptoms.