Study on the prediction model of non-suicidal self-injury behavior risk during hospitalization for adolescent inpatients with depression based on medical data

Abstract

Purpose: To develop a predictive model for identifying risk factors of non-suicidal self-injury (NSSI) during hospitalization in adolescents. By analyzing 1242 inpatient records, we explored NSSI risk factors in depressed adolescents and established a clinical predictive nomogram. Methods: We collected electronic medical records from the First Affiliated Hospital of Zhengzhou University from January 2021 to May 2023. The least absolute shrinkage and selection operator (LASSO) regression with tenfold cross-validation was used for variable selection. Multivariable logistic regression was then applied to build the predictive model. A nomogram was developed based on the selected variables and validated using a calibration plot, receiver operating characteristic curve (ROC), and decision curve analysis (DCA). External validation was also performed. Results: Six predictors were identified: sex, self-injury within 1 month before hospitalization, current course, history of attempted suicide, with suicide idea, and history of self-injury. The nomogram showed satisfactory discrimination in both the training (AUC 0.927; 95 % CI: 0.844–0.905) and validation (AUC 0.907; 95 % CI: 0.879–0.902) sets. Decision curve analysis (DCA) indicated clinical utility when the risk threshold was between 15 % and 83 %, with external validation confirming this range as 17 % to 80 %. Conclusion: We developed a nomogram to predict NSSI risk in hospitalized adolescent inpatients with depression. The nomogram demonstrated favorable calibration and discrimination, aiding clinicians in identifying at-risk inpatients and facilitating timely interventions, providing a reference for future prevention.

Keywords

• Non-suicidal self-injury behavior
• Risk factor
• Nomogram
• Depression
• Adolescent inpatients