Analysis of risk factors of suicidal ideation in adolescent patients with depression and construction of prediction model.

Abstract

Major depressive disorder is a common mental illness among adolescents and is the largest disease burden in this age group. Most adolescent patients with depression have suicidal ideation (SI); however, few studies have focused on the factors related to SI, and effective predictive models are lacking. To construct a risk prediction model for SI in adolescent depression and provide a reference assessment tool for prevention. The data of 150 adolescent patients with depression at the First People's Hospital of Lianyungang from June 2020 to December 2022 were retrospectively analyzed. Based on whether or not they had SI, they were divided into a SI group (n = 91) and a non-SI group (n = 59). The general data and laboratory indices of the two groups were compared. Logistic regression was used to analyze the factors influencing SI in adolescent patients with depression, a nomogram prediction model was constructed based on the analysis results, and internal evaluation was performed. Receiver operating characteristic and calibration curves were used to evaluate the model's efficacy, and the clinical application value was evaluated using decision curve analysis (DCA). There were differences in trauma history, triggers, serum ferritin levels (SF), high-sensitivity C-reactive protein levels (hs-CRP), and high-density lipoprotein (HDL-C) levels between the two groups (P < 0.05). Logistic regression analysis showed that trauma history, predisposing factors, SF, hs-CRP, and HDL-C were factors influencing SI in adolescent patients with depression. The area under the curve of the nomogram prediction model was 0.831 (95%CI: 0.763-0.899), sensitivity was 0.912, and specificity was 0.678. The higher net benefit of the DCA and the average absolute error of the calibration curve were 0.043, indicating that the model had a good fit. The nomogram prediction model based on trauma history, triggers, ferritin, serum hs-CRP, and HDL-C levels can effectively predict the risk of SI in adolescent patients with depression.