Predictive factors for the development of depression in children and adolescents: a clinical study

Abstract

BackgroundThe prevalence of depression among adolescents has been gradually increasing with the COVID-19 pandemic, and the purpose of this study was to develop and validate logistic regression models to predict the likelihood of depression among 6-17 year olds.MethodsWe screened participants from the National Center for Health Statistics (NCHS) in 2022. Independent risk factors were identified via univariate logistic regression analyses and least absolute shrinkage and selection operator (LASSO) for feature screening. Area under the curve (AUC) and decision curve analysis (DCA) were used to compare the predictive performance and clinical utility of these models. In addition, calibration curves were used to assess calibration.ResultsMultivariate logistic regression analyses revealed that risk factors for depression included girls, higher age, treated/judged based on race/ethnicity, ever lived with anyone mentally ill, experienced as a victim of/witnessed violence, and ever had autism, ever had attention-deficit disorder (ADD), etc. Afterwards, the results are visualized using a nomogram. The AUC of the training set is 0.731 and the AUC of the test set is 0.740. Also, the DCA and calibration curves demonstrate excellent performance.ConclusionValidated nomogram can accurately predict the risk of depression in children and adolescents, providing clues for clinical practitioners to develop targeted interventions and support.