Development and validation of a risk prediction nomogram model for re-enrollment intentions in adolescent depression patients on academic leave: a cross-sectional study

Abstract Background Depression is prevalent among adolescents and can result in academic leave, and these individuals may face difficulties when returning to school. This study aimed to investigate re-enrollment intentions among adolescent depression patients on leave, identify influencing factors, and develop a preliminary internally validated predictive nomogram model to provide evidence for potential future intervention strategies. Methods A convenience sample of 331 adolescent depression patients (all of whom were either on formal academic leave or had been away from school for more than three months) was surveyed between April and December 2023 at the outpatient departments and inpatient wards of two hospitals in Hunan, China. Participants were randomly split into training (70%) and internal validation (30%) groups. Data included demographics, re-enrollment-related experiences, a self-designed re-enrollment intentions questionnaire, depression severity, academic self-efficacy, study stress, family function, social support, and perceived discrimination. Binary logistic regression identified predictors, and a nomogram model was developed and validated. Results All participants were on academic leave (or away from school for more than three months). Among them, only 40.5% intended to re-enroll. Key predictors were academic self-efficacy, perceived school support, depressive symptoms, social support, and grade level. The nomogram showed promising discriminative ability and acceptable calibration in the internal validation cohort. Decision curve analysis suggested potential clinical utility. Conclusions The proportion of adolescent depression patients on leave with re-enrollment intentions is relatively low. The internally validated predictive model developed in this study demonstrates preliminary potential in this single-center dataset and may offer a basis for future research on risk assessment. The factors influencing re-enrollment intentions in these patients are multifaceted, with academic self-efficacy and school support having the most significant impact. These findings suggest that multidisciplinary efforts to enhance self-efficacy and school support merit further investigation as potential strategies to foster re-enrollment intentions.