Predicting the Trajectory of Non-suicidal Self-injury Among Adolescents

Objective: 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. Method: The study included adolescents (N=612; females n=435; 71.1%) aged 13-19-years-old (M=15.6, SD=1.4) receiving treatment from a psychiatric inpatient service. Youth were administered clinical interviews and symptom questionnaires at treatment initiation (baseline) and before termination. NSSI frequency was assessed at one-, three-, and six-month follow-ups. Latent class growth analyses were used to group adolescents based on their pattern of NSSI engagement across follow-ups. Results: Three classes were identified, reflecting: 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 (bs=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 Moderate Fluctuating class were identified. Conclusion: This study identified a set of intake clinical variables that indicate which adolescents may experience persistent NSSI post-discharge. Accordingly, this may help identify the youth that may benefit from additional monitoring and support post-hospitalization.