Predicting the trajectory of non‐suicidal self‐injury among adolescents

Background Non‐suicidal self‐injury (NSSI) is common among adolescents receiving inpatient psychiatric treatment and the months post‐discharge is a high‐risk period for self‐injurious behavior. Thus, identifying predictors that shape the course of post‐discharge NSSI may provide insights into ways to improve clinical outcomes. Accordingly, we used machine learning to identify the strongest predictors of NSSI trajectories drawn from a comprehensive clinical assessment. Methods The study included adolescents ( N = 612; females n = 435; 71.1%) aged 13–19‐years‐old ( M = 15.6, SD = 1.4) undergoing inpatient treatment. Youth were administered clinical interviews and symptom questionnaires at intake (baseline) and before termination. NSSI frequency was assessed at 1‐, 3‐, and 6‐month follow‐ups. Latent class growth analyses were used to group adolescents based on their pattern of NSSI across follow‐ups. Results Three classes were identified: Low Stable ( n = 83), Moderate Fluctuating ( n = 260), and High Persistent ( n = 269). Important predictors of the High Persistent class in our regularized regression models (LASSO) included baseline psychiatric symptoms and comorbidity, past‐week suicidal ideation (SI) severity, lifetime average and worst‐point SI intensity, and NSSI in the past 30 days ( b s = 0.75–2.33). Only worst‐point lifetime suicide ideation intensity was identified as a predictor of the Low Stable class ( b = −8.82); no predictors of the Moderate Fluctuating class emerged. Conclusions This study found a set of intake clinical variables that indicate which adolescents may experience persistent NSSI post‐discharge. Accordingly, this may help identify youth that may benefit from additional monitoring and support post‐hospitalization.