Predicting the trajectory of non-suicidal self-injury among adolescents / Geneva E. Mason ; Randy P. AUERBACH ; Jeremy G. Stewart in Journal of Child Psychology and Psychiatry, 66-2 (February 2025)  

Public ISBD [article]  inJournal of Child Psychology and Psychiatry > 66-2 (February 2025) . - p.189-201 Titre : Predicting the trajectory of non-suicidal self-injury among adolescents : Journal of Child Psychology and Psychiatry Type de document : Texte imprimé et/ou numérique Auteurs : Geneva E. Mason, Auteur ; Randy P. AUERBACH, Auteur ; Jeremy G. Stewart, Auteur Article en page(s) : p.189-201 Langues : Anglais (eng) Mots-clés : Adolescence  self-injury  suicidal behavior  longitudinal studies  machine learning Index. décimale : PER Périodiques Résumé : 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 (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 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. En ligne : https://doi.org/10.1111/jcpp.14046 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=545 [article] Predicting the trajectory of non-suicidal self-injury among adolescents : Journal of Child Psychology and Psychiatry [Texte imprimé et/ou numérique] / Geneva E. Mason, Auteur ; Randy P. AUERBACH, Auteur ; Jeremy G. Stewart, Auteur . - p.189-201. Langues : Anglais (eng) in Journal of Child Psychology and Psychiatry > 66-2 (February 2025) . - p.189-201 Mots-clés : Adolescence  self-injury  suicidal behavior  longitudinal studies  machine learning Index. décimale : PER Périodiques Résumé : 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 (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 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. En ligne : https://doi.org/10.1111/jcpp.14046 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=545

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